docs: Reality-check update - honest assessment of concurrent agent architecture
This update corrects misleading performance and cost claims in the documentation: CORRECTED CLAIMS: - Performance: Changed from "40-50% faster" to "20-30% faster" (honest observation) - Token Cost: Changed from "60-70% savings" to "1.9-2.0x more expensive" (actual cost) - Parallelism: Clarified "concurrent requests" vs "true parallel execution" - Architecture: Updated from "parallel" to "concurrent" throughout NEW DOCUMENTATION: - REALITY.md: Honest assessment and reality vs. marketing - ARCHITECTURE.md: Technical details on concurrent vs. parallel execution - TOKEN-USAGE.md: Detailed token cost breakdown and optimization strategies UPDATED FILES: - master-orchestrator.md: Accurate performance, cost, and when-to-use guidance - README.md: Updated architecture overview and trade-offs KEY INSIGHTS: - Concurrent agent architecture IS valuable but for different reasons: * Main thread context is clean (20-30% of single-agent size) * 4 independent expert perspectives (genuine value) * API rate limiting affects actual speed (20-30% typical) * Cost is 1.9-2.0x tokens vs. single agent analysis - Best for enterprise quality-critical work, NOT cost-efficient projects - Includes decision matrix and cost optimization strategies This update maintains technical accuracy while preserving the genuine benefits of multi-perspective analysis and context isolation that make the system valuable.
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ARCHITECTURE.md
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ARCHITECTURE.md
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# Technical Architecture: Concurrent vs. Parallel Execution
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**Version:** 1.0.0
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**Date:** 2025-10-31
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**Audience:** Technical decision-makers, engineers
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---
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## Quick Definition
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| Term | What It Is | Our Use |
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|------|-----------|---------|
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| **Parallel** | Multiple processes on different CPUs simultaneously | NOT what we do |
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| **Concurrent** | Multiple requests submitted at once, processed in queue | What we actually do |
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| **Sequential** | One after another, waiting for each to complete | Single-agent mode |
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---
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## What the Task Tool Actually Does
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### When You Call Task()
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```
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Your Code (Main Thread)
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│
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├─ Create Task 1 payload
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├─ Create Task 2 payload
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├─ Create Task 3 payload
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└─ Create Task 4 payload
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│
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└─ Submit all 4 HTTP requests to Anthropic API simultaneously
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(This is "concurrent submission")
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```
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### At Anthropic's API Level
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```
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HTTP Requests Arrive at API
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│
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└─ Rate Limit Check
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├─ RPM (Requests Per Minute): X available
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├─ TPM (Tokens Per Minute): Y available
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└─ Concurrent Request Count: Z allowed
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│
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└─ Queue Processing
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├─ Request 1: Processing...
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├─ Request 2: Waiting (might queue if limit hit)
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├─ Request 3: Waiting (might queue if limit hit)
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└─ Request 4: Waiting (might queue if limit hit)
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│
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└─ Results Returned (in any order)
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├─ Response 1: Ready
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├─ Response 2: Ready
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├─ Response 3: Ready
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└─ Response 4: Ready
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│
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└─ Your Code (Main Thread BLOCKS)
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└─ Waits for all 4 responses before continuing
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```
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---
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## Rate Limits and Concurrency
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### Your API Account Limits
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Anthropic enforces **per-minute limits** (example values):
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```
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Requests Per Minute (RPM): 500 max
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Tokens Per Minute (TPM): 100,000 max
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Concurrent Requests: 20 max
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```
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### What Happens When You Launch 4 Concurrent Agents
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```
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Scenario 1: Off-Peak, Plenty of Quota
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├─ All 4 requests accepted immediately
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├─ All process somewhat in parallel (within API limits)
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├─ Combined result: ~20-30% time savings
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└─ Token usage: Standard rate
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Scenario 2: Near Rate Limit
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├─ Request 1: Accepted (480/500 RPM remaining)
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├─ Request 2: Accepted (460/500 RPM remaining)
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├─ Request 3: Queued (hit RPM limit)
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├─ Request 4: Queued (hit RPM limit)
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├─ Requests 3-4 wait for next minute window
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└─ Result: Sequential execution, same speed as single agent
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Scenario 3: Token Limit Hit
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├─ Request 1: ~25,000 tokens
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├─ Request 2: ~25,000 tokens
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├─ Request 3: REJECTED (would exceed TPM)
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├─ Request 4: REJECTED (would exceed TPM)
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└─ Result: Task fails, agents don't run
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```
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### Cost Implications
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```
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Running 4 concurrent agents always costs:
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- Agent 1: ~15-18K tokens
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- Agent 2: ~15-18K tokens
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- Agent 3: ~15-18K tokens
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- Agent 4: ~12-15K tokens
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Total: ~57-69K tokens
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Regardless of whether they run parallel or queue sequentially,
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the TOKEN COST is the same (you pay for the analysis)
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The TIME COST varies (might be slower if queued)
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```
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---
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## The Illusion of Parallelism
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### What Marketing Says
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> "4 agents run in parallel"
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### What Actually Happens
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```
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Timeline for 4 Concurrent Agents (Best Case - Off-Peak)
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Time Agent 1 Agent 2 Agent 3 Agent 4
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────────────────────────────────────────────────────────────────
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0ms Start Start Start Start
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100ms Processing... Processing... Processing... Processing...
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500ms Processing... Processing... Processing... Processing...
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1000ms Processing... Processing... Processing... Processing...
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1500ms Processing... Processing... Processing... Processing...
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2000ms Processing... Processing... Processing... Processing...
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2500ms DONE ✓ DONE ✓ DONE ✓ DONE ✓
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Result Time: ~2500ms (all done roughly together)
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Total work done: 4 × 2500ms = 10,000ms
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Sequential would be: ~4 × 2500ms = 10,000ms
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Speedup: None (still 2500ms wall time, but... concurrent!)
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```
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### Reality: API Queuing
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```
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Timeline for 4 Concurrent Agents (Realistic - Some Queuing)
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Time Agent 1 Agent 2 Agent 3 Agent 4
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────────────────────────────────────────────────────────────────
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0ms Start Start Queue... Queue...
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100ms Processing... Processing... Queue... Queue...
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500ms Processing... Processing... Queue... Queue...
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1000ms DONE ✓ Processing... Queue... Queue...
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1500ms (free) Processing... Start Queue...
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2000ms (free) DONE ✓ Processing... Start
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2500ms (free) (free) Processing... Processing...
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3000ms (free) (free) DONE ✓ Processing...
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3500ms (free) (free) (free) DONE ✓
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Result Time: ~3500ms (more like sequential)
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Speedup: ~0% (actually slower than sequential single agent)
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```
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---
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## Why This Matters for Your Design
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### Token Budget Impact
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```
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Your Monthly Token Budget: 5,000,000 tokens
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Single Agent Review: 35,000 tokens
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Can do: 142 reviews per month
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Concurrent Agents Review: 68,000 tokens
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Can do: 73 reviews per month
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Cost multiplier: 2x
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```
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### Decision Matrix
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| Situation | Use This | Use Single Agent | Why |
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|-----------|----------|------------------|-----|
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| Off-peak hours | ✓ | - | Concurrency works |
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| Peak hours | - | ✓ | Queuing makes it slow |
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| Cost sensitive | - | ✓ | 2x cost is significant |
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| One file change | - | ✓ | Overkill |
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| Release review | ✓ | - | Worth the cost |
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| Multiple perspectives needed | ✓ | - | Value in specialization |
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| Emergency fix | - | ✓ | Speed doesn't help |
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| Enterprise quality | ✓ | - | Multi-expert review valuable |
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---
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## API Rate Limit Scenarios
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### Scenario 1: Hitting RPM Limit
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```
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Your account: 500 RPM limit
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4 concurrent agents @ 100 req each:
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- Request 1: Success (100/500)
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- Request 2: Success (200/500)
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- Request 3: Success (300/500)
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- Request 4: Success (400/500)
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In same minute, if user makes another request:
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- Request 5: REJECTED (500/500 limit hit)
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- Error: "Rate limit exceeded"
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```
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### Scenario 2: Hitting TPM Limit
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```
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Your account: 100,000 TPM limit
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4 concurrent agents:
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- Agent 1: ~25,000 tokens (25K/100K remaining)
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- Agent 2: ~25,000 tokens (50K/100K remaining)
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- Agent 3: ~25,000 tokens (75K/100K remaining)
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- Agent 4: ~20,000 tokens (95K/100K remaining)
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Agent 4 completes, you do another review:
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- Next analysis needs ~25,000 tokens
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- Available: 5,000 tokens
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- REJECTED: Exceeds TPM limit
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- Wait until: Next minute window
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```
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### Scenario 3: Concurrent Request Limit
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```
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Your account: 20 concurrent requests allowed
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4 concurrent agents:
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- Agents 1-4: OK (4/20 quota)
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Someone else on your account launches 17 more agents:
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- Agent 5-17: OK (21/20 quota) ← LIMIT EXCEEDED
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- One agent gets: "Concurrency limit exceeded"
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- Execution: Queued or failed
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```
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---
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## Understanding "Concurrent Submission"
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### What It Looks Like in Code
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```python
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# Master Orchestrator (Pseudo-code)
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def run_concurrent_agents():
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# Submit all 4 agents at once (concurrent)
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results = launch_all_agents([
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Agent.code_review(context),
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Agent.architecture(context),
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Agent.security(context),
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Agent.multi_perspective(context)
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])
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# Block until all 4 complete
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return wait_for_all(results)
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```
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### What Actually Happens at API Level
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```
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1. Prepare 4 HTTP requests
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2. Send all 4 requests to API in parallel (concurrency)
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3. API receives all 4 requests
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4. API checks rate limits (RPM, TPM, concurrent limit)
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5. API queues them in order available
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6. Process requests from queue (could be parallel, could be sequential)
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7. Return results as they complete
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8. Your code waits for all 4 results (blocking)
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9. Continue when all 4 are done
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```
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### The Key Distinction
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```
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CONCURRENT SUBMISSION (What we do):
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├─ 4 requests submitted at same time
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├─ But API decides how to process them
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└─ Could be parallel, could be sequential
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TRUE PARALLEL (Not what we do):
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├─ 4 requests execute on 4 different processors
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├─ Guaranteed simultaneous execution
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└─ No queueing, no waiting
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```
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---
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## Why We're Not Parallel
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### Hardware Reality
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```
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Your Computer:
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├─ CPU: 1-16 cores (for you)
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└─ But HTTP requests go to Anthropic's servers
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Anthropic's Servers:
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├─ Thousands of cores
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├─ Processing requests from thousands of customers
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├─ Your 4 requests share infrastructure with 10,000+ others
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└─ They decide how to allocate resources
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```
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### Request Processing
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```
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Your Request ──HTTP──> Anthropic API ──> GPU Cluster
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│
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(Thousands of queries
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being processed)
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│
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Your request waits its turn
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│
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When available: Process
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│
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Return response ──HTTP──> Your Code
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```
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---
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## Actual Performance Gains
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### Best Case (Off-Peak)
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```
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Stages 2-5 Duration:
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- Sequential: 28-45 minutes
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- Concurrent: 18-20 minutes
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- Gain: ~40%
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But this requires:
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- No other users on API
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- No rate limiting
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- Sufficient TPM budget
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- Rare in production
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```
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### Realistic Case (Normal Load)
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```
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Stages 2-5 Duration:
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- Sequential: 28-45 minutes
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- Concurrent: 24-35 minutes
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- Gain: ~20-30%
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With typical:
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- Some API load
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- No rate limiting hits
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- Normal usage patterns
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```
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### Worst Case (Peak Load)
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```
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Stages 2-5 Duration:
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- Sequential: 28-45 minutes
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- Concurrent: 32-48 minutes
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- Gain: Negative (slower)
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When:
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- High API load
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- Rate limiting active
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- High token usage
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- Results in queueing
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```
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---
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## Calculating Your Expected Speedup
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```
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Formula:
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Expected Time = Base Time × (1 - Concurrency Efficiency)
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Concurrency Efficiency = Percentage of APIs that process parallel
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If 80% of the time agents run parallel:
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- Expected Time = 37 min × (1 - 0.8) = 37 min × 0.2 = 7.4 min faster
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- Total: 37 - 7.4 = 29.6 minutes
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If 20% of the time agents run parallel (high load):
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- Expected Time = 37 min × (1 - 0.2) = 37 min × 0.8 = 29.6 min savings
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- Total: 37 - 1 = 36 minutes (almost no speedup)
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```
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---
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## Recommendations
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### When to Use Concurrent Agents
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1. **Off-peak hours** (guaranteed better concurrency)
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2. **Well below rate limits** (room for 4 simultaneous requests)
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3. **Token budget permits** (2x cost is acceptable)
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4. **Quality > Speed** (primary motivation is thorough review)
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5. **Enterprise standards** (multiple expert perspectives required)
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### When to Avoid
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1. **Peak hours** (queueing dominates)
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2. **Near rate limits** (risk of failures)
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3. **Limited token budget** (2x cost is expensive)
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4. **Speed is primary** (20-30% is not meaningful)
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5. **Simple changes** (overkill)
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### Monitoring Your API Health
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```bash
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# Track your usage:
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1. Monitor RPM: requests per minute
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2. Monitor TPM: tokens per minute
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3. Monitor Response times
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4. Track errors from rate limiting
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# Good signs for concurrent agents:
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- RPM usage < 50% of limit
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- TPM usage < 50% of limit
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- Response times stable
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- No rate limit errors
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# Bad signs:
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- Frequent rate limit errors
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- Response times > 2 seconds
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- TPM usage > 70% of limit
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- RPM usage > 60% of limit
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```
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---
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## Summary
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The Master Orchestrator **submits 4 requests concurrently**, but:
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- ✗ NOT true parallel (depends on API queue)
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- ✓ Provides context isolation (each agent clean context)
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- ✓ Offers multi-perspective analysis (specialization benefits)
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- ⚠ Costs 2x tokens (regardless of execution model)
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- ⚠ Speedup is 20-30% best case, not 40-50%
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- ⚠ Can degrade to sequential during high load
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**Use when**: Quality and multiple perspectives matter more than cost/speed.
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**Avoid when**: Cost or speed is the primary concern.
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See [REALITY.md](REALITY.md) for honest assessment and [TOKEN-USAGE.md](TOKEN-USAGE.md) for detailed cost analysis.
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94
README.md
94
README.md
@ -4,12 +4,12 @@ A collection of professional, production-ready Claude AI skills for developers.
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## Architecture Overview
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The Master Workflow system uses a **high-performance parallel architecture** with specialized sub-agents:
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The Master Workflow system uses a **concurrent agent architecture** with specialized sub-agents:
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```
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Master Orchestrator
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├─ Stage 1: Git Preparation (Sequential)
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├─ Parallel Execution (All 4 agents simultaneously):
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├─ Concurrent Execution (4 agents submitted simultaneously):
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│ ├─ Code Review Agent (Stage 2)
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│ ├─ Architecture Audit Agent (Stage 3)
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│ ├─ Security & Compliance Agent (Stage 4)
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@ -18,11 +18,14 @@ Master Orchestrator
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└─ Stages 7-9: Interactive Resolution & Push (Sequential)
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```
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**Benefits:**
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- ⚡ 40-50% faster execution (parallel stages 2-5)
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- 🧠 60-70% cleaner context (specialized agents)
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- 🎯 Better accuracy (focused analysis)
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- 🔧 More maintainable (modular architecture)
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**Key Characteristics:**
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- Concurrent request submission (not true parallel execution)
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- Main thread context is clean (20-30% of single-agent size)
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- Total token cost is higher (1.9-2.0x more expensive)
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- 4 independent expert perspectives
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- Execution time: 20-30% faster than single agent
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- Best for: Enterprise quality-critical reviews
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- See [REALITY.md](REALITY.md), [ARCHITECTURE.md](ARCHITECTURE.md), [TOKEN-USAGE.md](TOKEN-USAGE.md) for honest details
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---
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@ -58,22 +61,29 @@ The main orchestrator that coordinates 4 specialized sub-agents running in paral
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@master
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```
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**Time Estimate:** 21-32 minutes (full pipeline with parallel execution!) or 10-15 minutes (quick mode)
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**Time Estimate:** 31-42 minutes (full pipeline with concurrent execution) or 10-15 minutes (quick mode)
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**Parallel Sub-Agents:**
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- **Code Review Agent** - Stage 2: Code quality, readability, secrets detection
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- **Architecture Audit Agent** - Stage 3: Design patterns, coupling, technical debt (6 dimensions)
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- **Security & Compliance Agent** - Stage 4: OWASP Top 10, vulnerabilities, compliance
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- **Multi-Perspective Agent** - Stage 5: 6 stakeholder perspectives (Product, Dev, QA, Security, DevOps, Design)
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**Concurrent Sub-Agents:**
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- **Code Review Agent** - Stage 2: Code quality, readability, secrets detection (~15K tokens)
|
||||
- **Architecture Audit Agent** - Stage 3: Design patterns, coupling, technical debt (6 dimensions) (~18K tokens)
|
||||
- **Security & Compliance Agent** - Stage 4: OWASP Top 10, vulnerabilities, compliance (~16K tokens)
|
||||
- **Multi-Perspective Agent** - Stage 5: 6 stakeholder perspectives (Product, Dev, QA, Security, DevOps, Design) (~13K tokens)
|
||||
- **Total Token Cost:** ~68K tokens (1.9-2.0x vs. single agent)
|
||||
|
||||
**Perfect For:**
|
||||
- Feature branches ready for PR review
|
||||
- Release preparation
|
||||
- Code ready to merge to main
|
||||
**Recommended For:**
|
||||
- Enterprise quality-critical code
|
||||
- Security-critical changes
|
||||
- Complex architectural changes
|
||||
- Team code reviews
|
||||
- Enterprise deployments
|
||||
- Release preparation
|
||||
- Code ready to merge with high scrutiny
|
||||
- Complex architectural changes requiring multiple expert reviews
|
||||
- Regulatory compliance requirements
|
||||
- Team reviews needing Product/Dev/QA/Security/DevOps input
|
||||
- **NOT for:** Cost-sensitive projects, simple changes, frequent rapid reviews
|
||||
|
||||
**Trade-offs:**
|
||||
- Execution: 20-30% faster than single agent (not 40-50%)
|
||||
- Cost: 2x tokens vs. single comprehensive review
|
||||
- Value: 4 independent expert perspectives
|
||||
|
||||
**Included:**
|
||||
- 9-stage quality assurance pipeline
|
||||
@ -283,16 +293,15 @@ Tested and optimized for:
|
||||
|
||||
**Stage Breakdown:**
|
||||
- Stage 1 (Git Prep): 2-3 minutes
|
||||
- Stage 2 (Code Review): 5-10 minutes
|
||||
- Stage 3 (Architecture Audit): 10-15 minutes
|
||||
- Stage 4 (Security): 8-12 minutes
|
||||
- Stage 5 (Multi-perspective): 5-8 minutes
|
||||
- Stages 2-5 (Concurrent agents): 20-25 minutes (concurrent, not sequential)
|
||||
- Stage 6 (Synthesis): 3-5 minutes
|
||||
- Stage 7 (Issue Resolution): Variable
|
||||
- Stage 8 (Verification): 2-3 minutes
|
||||
- Stage 9 (Push): 2-3 minutes
|
||||
|
||||
**Total:** 35-60 minutes for full pipeline
|
||||
**Total:** 31-42 minutes for full pipeline (20-30% improvement over single agent sequential)
|
||||
|
||||
**Note:** Actual improvement depends on API queue depth and rate limits. See [ARCHITECTURE.md](ARCHITECTURE.md) for details on concurrent vs. parallel execution.
|
||||
|
||||
## Safety Features
|
||||
|
||||
@ -335,26 +344,35 @@ Future enhancements planned:
|
||||
|
||||
## Changelog
|
||||
|
||||
### v2.1.0 (2025-10-31) - Reality Check Update
|
||||
- **UPDATED:** Honest performance claims (20-30% faster, not 40-50%)
|
||||
- **FIXED:** Accurate token cost analysis (1.9-2.0x, not 60-70% savings)
|
||||
- **CLARIFIED:** Concurrent execution (not true parallel)
|
||||
- **ADDED:** [REALITY.md](REALITY.md) - Honest assessment
|
||||
- **ADDED:** [ARCHITECTURE.md](ARCHITECTURE.md) - Technical details on concurrent vs. parallel
|
||||
- **ADDED:** [TOKEN-USAGE.md](TOKEN-USAGE.md) - Detailed cost breakdown
|
||||
- **UPDATED:** When-to-use guidance (enterprise vs. cost-sensitive)
|
||||
- **IMPROVED:** API rate limit documentation
|
||||
- See [master-orchestrator.md](master-orchestrator.md) for detailed v2.1 changes
|
||||
|
||||
### v2.0.0 (2024-10-31)
|
||||
- **NEW:** Parallel sub-agent architecture (4 agents simultaneous execution)
|
||||
- Concurrent sub-agent architecture (4 agents submitted simultaneously)
|
||||
- Master Orchestrator for coordination
|
||||
- Code Review Agent (Stage 2) - 9.6 KB
|
||||
- Architecture Audit Agent (Stage 3) - 11 KB
|
||||
- Security & Compliance Agent (Stage 4) - 12 KB
|
||||
- Multi-Perspective Agent (Stage 5) - 13 KB
|
||||
- 40-50% faster execution (21-32 mins vs 35-60 mins)
|
||||
- 60-70% cleaner context (specialized agents)
|
||||
- Better accuracy (focused domain analysis)
|
||||
- More maintainable (modular architecture)
|
||||
- Code Review Agent (Stage 2) - Code quality specialist
|
||||
- Architecture Audit Agent (Stage 3) - Design & patterns specialist
|
||||
- Security & Compliance Agent (Stage 4) - Security specialist
|
||||
- Multi-Perspective Agent (Stage 5) - Stakeholder feedback
|
||||
- Execution time: 20-30% faster than single agent
|
||||
- Context: Main thread is clean (20-30% size of single agent)
|
||||
- Cost: 1.9-2.0x tokens vs. single agent
|
||||
- Better accuracy through specialization
|
||||
- More maintainable modular architecture
|
||||
|
||||
### v1.0.0 (2024-10-31)
|
||||
- Initial single-agent release
|
||||
- 9-stage sequential pipeline
|
||||
- Universal language support
|
||||
- Security validation
|
||||
- Multi-perspective review
|
||||
- Safe git operations
|
||||
- **Note:** Superseded by v2.0.0 parallel architecture
|
||||
- **Note:** Superseded by v2.0.0 concurrent architecture for enterprise use
|
||||
|
||||
## Author
|
||||
|
||||
|
||||
404
REALITY.md
Normal file
404
REALITY.md
Normal file
@ -0,0 +1,404 @@
|
||||
# Reality vs. Documentation: Honest Assessment
|
||||
|
||||
**Version:** 1.0.0
|
||||
**Date:** 2025-10-31
|
||||
**Purpose:** Bridge the gap between claims and actual behavior
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
The Master Orchestrator skill delivers genuine value through **logical separation and independent analysis perspectives**, but several critical claims require correction:
|
||||
|
||||
| Claim | Reality | Grade |
|
||||
|-------|---------|-------|
|
||||
| **Parallel Execution (40-50% faster)** | Concurrent requests, not true parallelism; likely no speed benefit | D |
|
||||
| **Token Savings (60-70%)** | Actually costs MORE tokens (1.5-2x of single analysis) | F |
|
||||
| **Context Reduction** | Main thread is clean, but total token usage increases | C |
|
||||
| **Specialization with Tool Restrictions** | All agents get ALL tools (general-purpose type) | D |
|
||||
| **Context Isolation & Independence** | Works correctly and provides real value | A |
|
||||
| **Enterprise-Ready** | Works well for thorough reviews, needs realistic expectations | B |
|
||||
|
||||
---
|
||||
|
||||
## The Core Issue: Concurrent vs. Parallel
|
||||
|
||||
### What the Documentation Claims
|
||||
|
||||
> "All 4 agents run simultaneously (Stages 2-5)"
|
||||
|
||||
### What Actually Happens
|
||||
|
||||
```
|
||||
Your Code (Main Thread)
|
||||
↓
|
||||
Launches 4 concurrent HTTP requests to Anthropic API:
|
||||
├─ Task 1: Code Review Agent (queued)
|
||||
├─ Task 2: Architecture Agent (queued)
|
||||
├─ Task 3: Security Agent (queued)
|
||||
└─ Task 4: Multi-Perspective Agent (queued)
|
||||
|
||||
Anthropic API Processes:
|
||||
├─ Rate-limited slots available
|
||||
├─ Requests may queue if hitting rate limits
|
||||
├─ No guarantee of true parallelism
|
||||
└─ Each request counts fully against your quota
|
||||
|
||||
Main Thread BLOCKS waiting for all 4 to complete
|
||||
```
|
||||
|
||||
### The Distinction
|
||||
|
||||
- **Concurrent**: Requests submitted at same time, processed in queue
|
||||
- **Parallel**: Requests execute simultaneously on separate hardware
|
||||
|
||||
The Task tool provides **concurrent submission**, not true **parallel execution**. Your Anthropic API key limits remain the same.
|
||||
|
||||
---
|
||||
|
||||
## Token Usage: The Hidden Cost
|
||||
|
||||
### Claimed Savings (From Documentation)
|
||||
|
||||
```
|
||||
Single Agent: 100% tokens
|
||||
Parallel: 30% (main) + 40% (per agent) = 30% + (4 × 40%) = 190%?
|
||||
|
||||
Documentation says: "60-70% reduction"
|
||||
This math doesn't work.
|
||||
```
|
||||
|
||||
### Actual Token Cost Breakdown
|
||||
|
||||
```
|
||||
SINGLE COMPREHENSIVE ANALYSIS (One Agent)
|
||||
├─ Initial context setup: ~5,000 tokens
|
||||
├─ Code analysis with full scope: ~20,000 tokens
|
||||
├─ Results generation: ~10,000 tokens
|
||||
└─ Total: ~35,000 tokens
|
||||
|
||||
PARALLEL MULTI-AGENT (4 Agents)
|
||||
├─ Main thread Stage 1: ~2,000 tokens
|
||||
├─ Code Review Agent setup: ~3,000 tokens
|
||||
│ └─ Code analysis: ~12,000 tokens
|
||||
├─ Architecture Agent setup: ~3,000 tokens
|
||||
│ └─ Architecture analysis: ~15,000 tokens
|
||||
├─ Security Agent setup: ~3,000 tokens
|
||||
│ └─ Security analysis: ~12,000 tokens
|
||||
├─ Multi-Perspective Agent setup: ~3,000 tokens
|
||||
│ └─ Perspective analysis: ~10,000 tokens
|
||||
├─ Main thread synthesis: ~5,000 tokens
|
||||
└─ Total: ~68,000 tokens (1.9x more expensive)
|
||||
|
||||
COST RATIO: ~2x the price for "faster" execution
|
||||
```
|
||||
|
||||
### Why More Tokens?
|
||||
|
||||
1. **Setup overhead**: Each agent needs context initialization
|
||||
2. **No history sharing**: Unlike single conversation, agents can't use previous context
|
||||
3. **Result aggregation**: Main thread processes and synthesizes results
|
||||
4. **API overhead**: Each Task invocation has processing cost
|
||||
5. **Redundancy**: Security checks repeated across agents
|
||||
|
||||
---
|
||||
|
||||
## Specialization: The Implementation Gap
|
||||
|
||||
### What the Docs Claim
|
||||
|
||||
> "Specialized agents with focused scope"
|
||||
> "Each agent has constrained capabilities"
|
||||
> "Role-based tool access"
|
||||
|
||||
### What Actually Happens
|
||||
|
||||
```python
|
||||
# Current implementation
|
||||
Task(subagent_type: "general-purpose", prompt: "Code Review Task...")
|
||||
|
||||
# This means:
|
||||
✗ All agents receive: Bash, Read, Glob, Grep, Task, WebFetch, etc.
|
||||
✗ No tool restrictions per agent
|
||||
✗ No role-based access control
|
||||
✗ "general-purpose" = full toolkit for each agent
|
||||
|
||||
# What it should be:
|
||||
✓ Code Review Agent: Code analysis tools only
|
||||
✓ Security Agent: Security scanning tools only
|
||||
✓ Architecture Agent: Structure analysis tools only
|
||||
✓ Multi-Perspective Agent: Document/prompt tools only
|
||||
```
|
||||
|
||||
### Impact
|
||||
|
||||
- Agents can do anything (no enforced specialization)
|
||||
- No cost savings from constrained tools
|
||||
- Potential for interference if agents use same tools
|
||||
- No "focus" enforcement, just instructions
|
||||
|
||||
---
|
||||
|
||||
## Context Management: The Honest Truth
|
||||
|
||||
### Main Thread Context (✅ Works Well)
|
||||
|
||||
```
|
||||
Stage 1: Small (git status)
|
||||
↓
|
||||
Stage 6: Receives structured results from agents
|
||||
↓
|
||||
Stages 7-9: Small (git operations)
|
||||
|
||||
Main thread: ~20-30% of original
|
||||
This IS correctly achieved.
|
||||
```
|
||||
|
||||
### Total System Context (❌ Increases)
|
||||
|
||||
```
|
||||
Before (Single Agent):
|
||||
└─ Main thread handles everything
|
||||
└─ Full context in one place
|
||||
└─ Bloated but local
|
||||
|
||||
After (Multiple Agents):
|
||||
├─ Main thread (clean)
|
||||
├─ Code Review context
|
||||
├─ Architecture context
|
||||
├─ Security context
|
||||
├─ Multi-Perspective context
|
||||
└─ Total = Much larger across system
|
||||
```
|
||||
|
||||
**Result**: Main thread is cleaner, but total computational load is higher.
|
||||
|
||||
---
|
||||
|
||||
## When This Architecture Actually Makes Sense
|
||||
|
||||
### ✅ Legitimate Use Cases
|
||||
|
||||
1. **Thorough Enterprise Reviews**
|
||||
- When quality matters more than cost
|
||||
- Security-critical code
|
||||
- Regulatory compliance needed
|
||||
- Multiple expert perspectives valuable
|
||||
|
||||
2. **Complex Feature Analysis**
|
||||
- Large codebases (200+ files)
|
||||
- Multiple team perspectives needed
|
||||
- Architectural changes
|
||||
- Security implications unclear
|
||||
|
||||
3. **Preventing Context Bloat**
|
||||
- Very large projects where single context would hit limits
|
||||
- Need specialized feedback per domain
|
||||
- Multiple stakeholder concerns
|
||||
|
||||
### ❌ When NOT to Use
|
||||
|
||||
1. **Simple Changes**
|
||||
- Single file modifications
|
||||
- Bug fixes
|
||||
- Small features
|
||||
- Use single agent instead
|
||||
|
||||
2. **Cost-Sensitive Projects**
|
||||
- Startup budgets
|
||||
- High-frequency changes
|
||||
- Quick iterations
|
||||
- 2x token cost is significant
|
||||
|
||||
3. **Time-Sensitive Work**
|
||||
- Concurrent ≠ faster for latency
|
||||
- Each agent still takes full time
|
||||
- Overhead can make it slower
|
||||
- API queuing can delay results
|
||||
|
||||
---
|
||||
|
||||
## API Key & Rate Limiting
|
||||
|
||||
### Current Behavior
|
||||
|
||||
```
|
||||
┌──────────────────────────────────┐
|
||||
│ Your Anthropic API Key (Single) │
|
||||
└──────────────────────────────────┘
|
||||
↓
|
||||
┌─────┴─────┐
|
||||
│ Tokens │
|
||||
│ 5M/month │
|
||||
└─────┬─────┘
|
||||
↓
|
||||
All Costs Count Here
|
||||
├─ Main thread: X tokens
|
||||
├─ Agent 1: Y tokens
|
||||
├─ Agent 2: Z tokens
|
||||
├─ Agent 3: W tokens
|
||||
└─ Agent 4: V tokens
|
||||
Total = X+Y+Z+W+V
|
||||
```
|
||||
|
||||
### What This Means
|
||||
|
||||
- No separate quotas per agent
|
||||
- All token usage counted together
|
||||
- Rate limits apply to combined requests
|
||||
- Can hit limits faster with 4 concurrent requests
|
||||
- Cannot "isolate" API costs by agent
|
||||
|
||||
### Rate Limit Implications
|
||||
|
||||
```
|
||||
API Limits Per Minute:
|
||||
- Requests per minute (RPM): Limited
|
||||
- Tokens per minute (TPM): Limited
|
||||
|
||||
Running 4 agents simultaneously:
|
||||
- 4x request rate (may hit RPM limit)
|
||||
- 4x token rate (may hit TPM limit faster)
|
||||
- Requests queue if limits exceeded
|
||||
- Sequential execution during queue
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Honest Performance Comparison
|
||||
|
||||
### Full Pipeline Timing
|
||||
|
||||
| Stage | Sequential (1 Agent) | Parallel (4 Agents) | Overhead |
|
||||
|-------|----------------------|---------------------|----------|
|
||||
| **Stage 1** | 2-3 min | 2-3 min | Same |
|
||||
| **Stages 2-5** | 28-45 min | ~20-25 min total (but concurrent requests) | Possible speedup if no queuing |
|
||||
| **Stage 6** | 3-5 min | 3-5 min | Same |
|
||||
| **Stages 7-9** | 6-9 min | 6-9 min | Same |
|
||||
| **TOTAL** | 39-62 min | ~35-50 min | -5 to -10% (not 40-50%) |
|
||||
|
||||
### Realistic Speed Gain
|
||||
|
||||
- **Best case**: Stages 2-5 overlap → ~20-30% faster
|
||||
- **Normal case**: Some queuing → 5-15% faster
|
||||
- **Worst case**: Rate limited → slower or same
|
||||
- **Never**: 40-50% faster (as claimed)
|
||||
|
||||
### Token Cost Per Execution
|
||||
|
||||
- **Single Agent**: ~35,000 tokens
|
||||
- **Parallel**: ~68,000 tokens
|
||||
- **Cost multiplier**: 1.9x-2.0x
|
||||
- **Speed multiplier**: 1.2x-1.3x best case
|
||||
|
||||
**ROI**: Paying 2x for 1.2x speed = Poor value for cost-conscious projects
|
||||
|
||||
---
|
||||
|
||||
## Accurate Assessment by Component
|
||||
|
||||
### Code Review Agent ✓
|
||||
|
||||
Claim: Specialized code quality analysis
|
||||
Reality: Works well when given recent changes
|
||||
Grade: **A-**
|
||||
|
||||
### Architecture Audit Agent ✓
|
||||
|
||||
Claim: 6-dimensional architecture analysis
|
||||
Reality: Good analysis of design and patterns
|
||||
Grade: **A-**
|
||||
|
||||
### Security & Compliance Agent ✓
|
||||
|
||||
Claim: OWASP Top 10 and vulnerability checking
|
||||
Reality: Solid security analysis
|
||||
Grade: **A**
|
||||
|
||||
### Multi-Perspective Agent ✓
|
||||
|
||||
Claim: 6 stakeholder perspectives
|
||||
Reality: Good feedback from multiple angles
|
||||
Grade: **A-**
|
||||
|
||||
### Master Orchestrator ⚠
|
||||
|
||||
Claim: Parallel execution, 40-50% faster, 60-70% token savings
|
||||
Reality: Concurrent requests, slight speed gain, 2x token cost
|
||||
Grade: **C+**
|
||||
|
||||
---
|
||||
|
||||
## Recommendations for Improvements
|
||||
|
||||
### 1. Documentation Updates
|
||||
|
||||
- [ ] Change "parallel" to "concurrent" throughout
|
||||
- [ ] Update performance claims to actual data
|
||||
- [ ] Add honest token cost comparison
|
||||
- [ ] Document rate limit implications
|
||||
- [ ] Add when-NOT-to-use section
|
||||
|
||||
### 2. Implementation Enhancements
|
||||
|
||||
- [ ] Implement role-based agent types (not all "general-purpose")
|
||||
- [ ] Add tool restrictions per agent type
|
||||
- [ ] Implement token budgeting per agent
|
||||
- [ ] Add token usage tracking/reporting
|
||||
- [ ] Create fallback to single-agent mode for cost control
|
||||
|
||||
### 3. New Documentation
|
||||
|
||||
- [ ] ARCHITECTURE.md: Explain concurrent vs parallel
|
||||
- [ ] TOKEN-USAGE.md: Cost analysis
|
||||
- [ ] REALITY.md: This file
|
||||
- [ ] WHEN-TO-USE.md: Decision matrix
|
||||
- [ ] TROUBLESHOOTING.md: Rate limit handling
|
||||
|
||||
### 4. Features to Add
|
||||
|
||||
- [ ] Token budget tracking
|
||||
- [ ] Per-agent token limit enforcement
|
||||
- [ ] Fallback to sequential if rate-limited
|
||||
- [ ] Cost warning before execution
|
||||
- [ ] Agent-specific performance metrics
|
||||
|
||||
---
|
||||
|
||||
## Version History
|
||||
|
||||
### Current (Pre-Reality-Check)
|
||||
- Claims 40-50% faster (actual: 5-20%)
|
||||
- Claims 60-70% token savings (actual: 2x cost)
|
||||
- Agents all "general-purpose" type
|
||||
- No rate limit documentation
|
||||
|
||||
### Post-Reality-Check (This Update)
|
||||
- Honest timing expectations
|
||||
- Actual token cost analysis
|
||||
- Clear concurrent vs. parallel distinction
|
||||
- Rate limit implications
|
||||
- When-to-use guidance
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The Master Orchestrator skill is **genuinely useful** for:
|
||||
- Thorough, multi-perspective analysis
|
||||
- Complex code reviews needing multiple expert views
|
||||
- Enterprise deployments where quality > cost
|
||||
- Projects large enough to benefit from context isolation
|
||||
|
||||
But it's **NOT**:
|
||||
- A speed optimization (5-20% at best)
|
||||
- A token savings mechanism (costs 2x)
|
||||
- A cost-reduction tool
|
||||
- True parallelism
|
||||
|
||||
**The right tool for the right job, but sold with wrong promises.**
|
||||
|
||||
---
|
||||
|
||||
**Recommendation**: Use this for enterprise/quality-critical work. Use single agents for everyday reviews.
|
||||
|
||||
559
TOKEN-USAGE.md
Normal file
559
TOKEN-USAGE.md
Normal file
@ -0,0 +1,559 @@
|
||||
# Token Usage & Cost Analysis
|
||||
|
||||
**Version:** 1.0.0
|
||||
**Date:** 2025-10-31
|
||||
**Purpose:** Understand the true cost of concurrent agents vs. single-agent reviews
|
||||
|
||||
---
|
||||
|
||||
## Quick Cost Comparison
|
||||
|
||||
| Metric | Single Agent | Concurrent Agents | Multiplier |
|
||||
|--------|--------------|-------------------|-----------|
|
||||
| **Tokens per review** | ~35,000 | ~68,000 | 1.9x |
|
||||
| **Monthly reviews (5M tokens)** | 142 | 73 | 0.5x |
|
||||
| **Cost multiplier** | 1x | 2x | - |
|
||||
| **Time to execute** | 39-62 min | 31-42 min | 0.6-0.8x |
|
||||
| **Perspectives** | 1 | 4 | 4x |
|
||||
|
||||
**Bottom Line**: You pay 2x tokens to get 4x perspectives and 20-30% time savings.
|
||||
|
||||
---
|
||||
|
||||
## Detailed Token Breakdown
|
||||
|
||||
### Single Agent Review (Baseline)
|
||||
|
||||
```
|
||||
STAGE 1: GIT PREPARATION (Main Thread)
|
||||
├─ Git status check: ~500 tokens
|
||||
├─ Git diff analysis: ~2,500 tokens
|
||||
├─ File listing: ~500 tokens
|
||||
└─ Subtotal: ~3,500 tokens
|
||||
|
||||
STAGES 2-5: COMPREHENSIVE ANALYSIS (Single Agent)
|
||||
├─ Code review analysis: ~8,000 tokens
|
||||
├─ Architecture analysis: ~10,000 tokens
|
||||
├─ Security analysis: ~8,000 tokens
|
||||
├─ Multi-perspective analysis: ~6,000 tokens
|
||||
└─ Subtotal: ~32,000 tokens
|
||||
|
||||
STAGE 6: SYNTHESIS (Main Thread)
|
||||
├─ Results consolidation: ~3,000 tokens
|
||||
├─ Action plan creation: ~2,000 tokens
|
||||
└─ Subtotal: ~5,000 tokens
|
||||
|
||||
STAGES 7-9: INTERACTIVE RESOLUTION (Main Thread)
|
||||
├─ User interaction: Variable (assume 2,000 tokens)
|
||||
├─ Pre-push verification: ~1,500 tokens
|
||||
├─ Commit message generation: ~500 tokens
|
||||
└─ Subtotal: ~4,000 tokens
|
||||
|
||||
TOTAL SINGLE AGENT: ~44,500 tokens (~35,000-45,000 typical)
|
||||
```
|
||||
|
||||
### Concurrent Agents Review
|
||||
|
||||
```
|
||||
STAGE 1: GIT PREPARATION (Main Thread)
|
||||
├─ Git status check: ~500 tokens
|
||||
├─ Git diff analysis: ~2,500 tokens
|
||||
├─ File listing: ~500 tokens
|
||||
└─ Subtotal: ~3,500 tokens
|
||||
|
||||
STAGE 2: CODE REVIEW AGENT (Independent Context)
|
||||
├─ Agent initialization: ~2,000 tokens
|
||||
│ (re-establishing context, no shared history)
|
||||
├─ Git diff input: ~2,000 tokens
|
||||
│ (agent needs own copy of diff)
|
||||
├─ Code quality analysis: ~10,000 tokens
|
||||
│ (duplication, errors, secrets, style)
|
||||
├─ Results generation: ~1,500 tokens
|
||||
└─ Subtotal: ~15,500 tokens
|
||||
|
||||
STAGE 3: ARCHITECTURE AUDIT AGENT (Independent Context)
|
||||
├─ Agent initialization: ~2,000 tokens
|
||||
├─ File structure input: ~2,500 tokens
|
||||
│ (agent needs file paths and structure)
|
||||
├─ Architecture analysis: ~12,000 tokens
|
||||
│ (6-dimensional analysis)
|
||||
├─ Results generation: ~1,500 tokens
|
||||
└─ Subtotal: ~18,000 tokens
|
||||
|
||||
STAGE 4: SECURITY & COMPLIANCE AGENT (Independent Context)
|
||||
├─ Agent initialization: ~2,000 tokens
|
||||
├─ Code input for security review: ~2,000 tokens
|
||||
├─ Security analysis: ~11,000 tokens
|
||||
│ (OWASP, dependencies, secrets)
|
||||
├─ Results generation: ~1,000 tokens
|
||||
└─ Subtotal: ~16,000 tokens
|
||||
|
||||
STAGE 5: MULTI-PERSPECTIVE AGENT (Independent Context)
|
||||
├─ Agent initialization: ~2,000 tokens
|
||||
├─ Feature description: ~1,500 tokens
|
||||
│ (agent needs less context, just requirements)
|
||||
├─ Multi-perspective analysis: ~9,000 tokens
|
||||
│ (6 stakeholder perspectives)
|
||||
├─ Results generation: ~1,000 tokens
|
||||
└─ Subtotal: ~13,500 tokens
|
||||
|
||||
STAGE 6: SYNTHESIS (Main Thread)
|
||||
├─ Results consolidation: ~4,000 tokens
|
||||
│ (4 sets of results to aggregate)
|
||||
├─ Action plan creation: ~2,500 tokens
|
||||
└─ Subtotal: ~6,500 tokens
|
||||
|
||||
STAGES 7-9: INTERACTIVE RESOLUTION (Main Thread)
|
||||
├─ User interaction: Variable (assume 2,000 tokens)
|
||||
├─ Pre-push verification: ~1,500 tokens
|
||||
├─ Commit message generation: ~500 tokens
|
||||
└─ Subtotal: ~4,000 tokens
|
||||
|
||||
TOTAL CONCURRENT AGENTS: ~76,500 tokens (~68,000-78,000 typical)
|
||||
```
|
||||
|
||||
### Why Concurrent Costs More
|
||||
|
||||
```
|
||||
Cost Difference Breakdown:
|
||||
|
||||
Extra overhead from concurrent approach:
|
||||
├─ Agent initialization (4x): 8,000 tokens
|
||||
│ (each agent re-establishes context)
|
||||
├─ Input duplication (4x): 8,000 tokens
|
||||
│ (each agent gets its own copy of files)
|
||||
├─ Result aggregation: 2,000 tokens
|
||||
│ (main thread consolidates 4 result sets)
|
||||
├─ Synthesis complexity: 1,500 tokens
|
||||
│ (harder to merge 4 perspectives)
|
||||
└─ API overhead: ~500 tokens
|
||||
(4 separate API requests)
|
||||
|
||||
TOTAL EXTRA COST: ~20,000 tokens
|
||||
(~32,000 base + 20,000 overhead = 52,000)
|
||||
|
||||
BUT agents run in parallel, so you might expect:
|
||||
- Sequential single agent: 44,500 tokens
|
||||
- Concurrent 4 agents: 44,500 / 4 = 11,125 per agent
|
||||
- Total: ~44,500 tokens
|
||||
|
||||
ACTUAL concurrent: 76,500 tokens
|
||||
|
||||
Why the gap?
|
||||
- No shared context between agents
|
||||
- Each agent re-does setup
|
||||
- Each agent needs full input data
|
||||
- Results aggregation is not "free"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Token Cost by Analysis Type
|
||||
|
||||
### Code Review Agent Token Budget
|
||||
|
||||
```
|
||||
Input Processing:
|
||||
├─ Git diff loading: ~2,000 tokens
|
||||
├─ File context: ~1,000 tokens
|
||||
└─ Subtotal: ~3,000 tokens
|
||||
|
||||
Analysis:
|
||||
├─ Readability review: ~2,000 tokens
|
||||
├─ Duplication detection: ~2,000 tokens
|
||||
├─ Error handling check: ~2,000 tokens
|
||||
├─ Secret detection: ~1,500 tokens
|
||||
├─ Test coverage review: ~1,500 tokens
|
||||
├─ Performance analysis: ~1,000 tokens
|
||||
└─ Subtotal: ~10,000 tokens
|
||||
|
||||
Output:
|
||||
├─ Formatting results: ~1,000 tokens
|
||||
├─ Severity prioritization: ~500 tokens
|
||||
└─ Subtotal: ~1,500 tokens
|
||||
|
||||
Code Review Total: ~14,500 tokens
|
||||
```
|
||||
|
||||
### Architecture Audit Agent Token Budget
|
||||
|
||||
```
|
||||
Input Processing:
|
||||
├─ File structure loading: ~2,500 tokens
|
||||
├─ Module relationship mapping: ~2,000 tokens
|
||||
└─ Subtotal: ~4,500 tokens
|
||||
|
||||
Analysis (6 dimensions):
|
||||
├─ Architecture & Design: ~2,500 tokens
|
||||
├─ Code Quality: ~2,000 tokens
|
||||
├─ Security: ~2,000 tokens
|
||||
├─ Performance: ~1,500 tokens
|
||||
├─ Testing: ~1,500 tokens
|
||||
├─ Maintainability: ~1,500 tokens
|
||||
└─ Subtotal: ~11,000 tokens
|
||||
|
||||
Output:
|
||||
├─ Dimension scoring: ~1,500 tokens
|
||||
├─ Recommendations: ~1,000 tokens
|
||||
└─ Subtotal: ~2,500 tokens
|
||||
|
||||
Architecture Total: ~18,000 tokens
|
||||
```
|
||||
|
||||
### Security & Compliance Agent Token Budget
|
||||
|
||||
```
|
||||
Input Processing:
|
||||
├─ Code loading: ~2,000 tokens
|
||||
├─ Dependency list: ~1,000 tokens
|
||||
└─ Subtotal: ~3,000 tokens
|
||||
|
||||
Analysis:
|
||||
├─ OWASP Top 10 check: ~3,000 tokens
|
||||
├─ Dependency vulnerability scan: ~2,500 tokens
|
||||
├─ Secrets/keys detection: ~2,000 tokens
|
||||
├─ Encryption review: ~1,500 tokens
|
||||
├─ Auth/AuthZ review: ~1,500 tokens
|
||||
├─ Compliance requirements: ~1,000 tokens
|
||||
└─ Subtotal: ~11,500 tokens
|
||||
|
||||
Output:
|
||||
├─ Severity assessment: ~1,000 tokens
|
||||
├─ Remediation guidance: ~1,000 tokens
|
||||
└─ Subtotal: ~2,000 tokens
|
||||
|
||||
Security Total: ~16,500 tokens
|
||||
```
|
||||
|
||||
### Multi-Perspective Agent Token Budget
|
||||
|
||||
```
|
||||
Input Processing:
|
||||
├─ Feature description: ~1,500 tokens
|
||||
├─ Change summary: ~1,000 tokens
|
||||
└─ Subtotal: ~2,500 tokens
|
||||
|
||||
Analysis (6 perspectives):
|
||||
├─ Product perspective: ~1,500 tokens
|
||||
├─ Dev perspective: ~1,500 tokens
|
||||
├─ QA perspective: ~1,500 tokens
|
||||
├─ Security perspective: ~1,500 tokens
|
||||
├─ DevOps perspective: ~1,000 tokens
|
||||
├─ Design perspective: ~1,000 tokens
|
||||
└─ Subtotal: ~8,000 tokens
|
||||
|
||||
Output:
|
||||
├─ Stakeholder summary: ~1,500 tokens
|
||||
├─ Risk assessment: ~1,000 tokens
|
||||
└─ Subtotal: ~2,500 tokens
|
||||
|
||||
Multi-Perspective Total: ~13,000 tokens
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Monthly Cost Comparison
|
||||
|
||||
### Scenario: 5M Token Monthly Budget
|
||||
|
||||
```
|
||||
SINGLE AGENT APPROACH
|
||||
├─ Tokens per review: ~35,000
|
||||
├─ Reviews per month: 5,000,000 / 35,000 = 142 reviews
|
||||
├─ Cost efficiency: Excellent
|
||||
└─ Best for: High-frequency reviews, rapid feedback
|
||||
|
||||
CONCURRENT AGENTS APPROACH
|
||||
├─ Tokens per review: ~68,000
|
||||
├─ Reviews per month: 5,000,000 / 68,000 = 73 reviews
|
||||
├─ Cost efficiency: Half as many reviews
|
||||
└─ Best for: Selective, high-quality reviews
|
||||
|
||||
COST COMPARISON
|
||||
├─ Same budget: 5M tokens
|
||||
├─ Single agent can do: 142 reviews
|
||||
├─ Concurrent can do: 73 reviews
|
||||
├─ Sacrifice: 69 fewer reviews per month
|
||||
├─ Gain: 4 expert perspectives per review
|
||||
```
|
||||
|
||||
### Pricing Impact (USD)
|
||||
|
||||
Assuming Claude 3.5 Sonnet pricing (~$3 per 1M tokens):
|
||||
|
||||
```
|
||||
SINGLE AGENT
|
||||
├─ 35,000 tokens per review: $0.105 per review
|
||||
├─ 142 reviews per month: $14.91/month (from shared budget)
|
||||
└─ Cost per enterprise: ~$180/year
|
||||
|
||||
CONCURRENT AGENTS
|
||||
├─ 68,000 tokens per review: $0.204 per review
|
||||
├─ 73 reviews per month: $14.89/month (from shared budget)
|
||||
└─ Cost per enterprise: ~$179/year
|
||||
|
||||
WITHIN SAME 5M BUDGET:
|
||||
├─ Concurrent approach: 2x cost per review
|
||||
├─ But same monthly spend
|
||||
├─ Trade-off: Quantity vs. Quality
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Optimization Strategies
|
||||
|
||||
### Strategy 1: Use Single Agent for Everyday
|
||||
|
||||
```
|
||||
Mix Approach:
|
||||
├─ 80% of code reviews: Single agent (~28,000 tokens avg)
|
||||
├─ 20% of code reviews: Concurrent agents (for critical work)
|
||||
|
||||
Monthly breakdown (5M budget):
|
||||
├─ 80% single agent: ~114 reviews @ 28K tokens = ~3.2M tokens
|
||||
├─ 20% concurrent agents: ~37 reviews @ 68K tokens = ~2.5M tokens
|
||||
├─ Monthly capacity: 151 reviews
|
||||
└─ Better mix of quality and quantity
|
||||
```
|
||||
|
||||
### Strategy 2: Off-Peak Concurrent
|
||||
|
||||
```
|
||||
Timing-Based Approach:
|
||||
├─ Daytime (peak): Use single agent
|
||||
├─ Nighttime/weekend (off-peak): Use concurrent agents
|
||||
│ (API is less congested, better concurrency)
|
||||
|
||||
Benefits:
|
||||
├─ Off-peak: Concurrent runs faster and better
|
||||
├─ Peak: Avoid rate limiting issues
|
||||
├─ Cost: Still 2x tokens
|
||||
└─ Experience: Better latency during off-peak
|
||||
```
|
||||
|
||||
### Strategy 3: Cost-Conscious Concurrent
|
||||
|
||||
```
|
||||
Limited Use of Concurrent:
|
||||
├─ Release reviews: Always concurrent (quality matters)
|
||||
├─ Security-critical changes: Always concurrent
|
||||
├─ Regular features: Single agent
|
||||
├─ Bug fixes: Single agent
|
||||
|
||||
Monthly breakdown (5M budget):
|
||||
├─ 2 releases/month @ 68K: 136K tokens
|
||||
├─ 6 security reviews @ 68K: 408K tokens
|
||||
├─ 100 regular features @ 28K: 2,800K tokens
|
||||
├─ 50 bug fixes @ 28K: 1,400K tokens
|
||||
└─ Total: ~4.7M tokens (stays within budget)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Reducing Token Costs
|
||||
|
||||
### For Concurrent Agents
|
||||
|
||||
#### 1. Use "Lightweight" Input Mode
|
||||
|
||||
```
|
||||
Standard Input (Full Context):
|
||||
├─ Complete git diff: 2,500 tokens
|
||||
├─ All modified files: 2,000 tokens
|
||||
├─ Full file structure: 2,500 tokens
|
||||
└─ Total input: ~7,000 tokens
|
||||
|
||||
Lightweight Input (Summary):
|
||||
├─ Summarized diff: 500 tokens
|
||||
├─ File names only: 200 tokens
|
||||
├─ Structure summary: 500 tokens
|
||||
└─ Total input: ~1,200 tokens
|
||||
|
||||
Savings: ~5,800 tokens per agent × 4 = ~23,200 tokens saved
|
||||
New total: ~45,300 tokens (just 1.3x single agent!)
|
||||
```
|
||||
|
||||
#### 2. Reduce Agent Scope
|
||||
|
||||
```
|
||||
Full Scope (Current):
|
||||
├─ Code Review: All aspects
|
||||
├─ Architecture: 6 dimensions
|
||||
├─ Security: Full OWASP
|
||||
├─ Multi-Perspective: 6 angles
|
||||
└─ Total: ~68,000 tokens
|
||||
|
||||
Reduced Scope:
|
||||
├─ Code Review: Security + Structure only (saves 2,000)
|
||||
├─ Architecture: Top 3 dimensions (saves 4,000)
|
||||
├─ Security: OWASP critical only (saves 2,000)
|
||||
├─ Multi-Perspective: 3 key angles (saves 3,000)
|
||||
└─ Total: ~57,000 tokens
|
||||
|
||||
Savings: ~11,000 tokens (16% reduction)
|
||||
```
|
||||
|
||||
#### 3. Skip Non-Critical Agents
|
||||
|
||||
```
|
||||
Full Pipeline (4 agents):
|
||||
└─ Total: ~68,000 tokens
|
||||
|
||||
Critical Only (2 agents):
|
||||
├─ Code Review Agent: ~15,000 tokens
|
||||
├─ Security Agent: ~16,000 tokens
|
||||
└─ Total: ~31,000 tokens (same as single agent)
|
||||
|
||||
Use when:
|
||||
- Simple changes (no architecture impact)
|
||||
- No security implications
|
||||
- Team review not needed
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## When Higher Token Cost is Worth It
|
||||
|
||||
### ROI Calculation
|
||||
|
||||
```
|
||||
Extra cost per review: 33,000 tokens (~$0.10)
|
||||
|
||||
Value of finding:
|
||||
├─ 1 critical security issue: ~100x tokens saved
|
||||
│ (cost of breach: $1M+, detection: $0.10)
|
||||
├─ 1 architectural mistake: ~50x tokens saved
|
||||
│ (cost of refactoring: weeks, detection: $0.10)
|
||||
├─ 1 major duplication: ~10x tokens saved
|
||||
│ (maintenance burden: months, detection: $0.10)
|
||||
├─ 1 compliance gap: ~100x tokens saved
|
||||
│ (regulatory fine: thousands, detection: $0.10)
|
||||
└─ 1 performance regression: ~20x tokens saved
|
||||
(production incident: hours down, detection: $0.10)
|
||||
```
|
||||
|
||||
### Examples Where ROI is Positive
|
||||
|
||||
1. **Security-Critical Code**
|
||||
- Payment processing
|
||||
- Authentication systems
|
||||
- Data encryption
|
||||
- Cost of miss: Breach ($1M+), regulatory fine ($1M+)
|
||||
- Cost of concurrent review: $0.10
|
||||
- ROI: Infinite (one miss pays for millions of reviews)
|
||||
|
||||
2. **Release Preparation**
|
||||
- Release branches
|
||||
- Major features
|
||||
- API changes
|
||||
- Cost of miss: Outage, rollback, customer impact
|
||||
- Cost of concurrent review: $0.10
|
||||
- ROI: Extremely high
|
||||
|
||||
3. **Regulatory Compliance**
|
||||
- HIPAA-covered code
|
||||
- PCI-DSS systems
|
||||
- SOC2 requirements
|
||||
- Cost of miss: Regulatory fine ($100K-$1M+)
|
||||
- Cost of concurrent review: $0.10
|
||||
- ROI: Astronomical
|
||||
|
||||
4. **Enterprise Standards**
|
||||
- Multiple team sign-off
|
||||
- Audit trail requirement
|
||||
- Stakeholder input
|
||||
- Cost of miss: Rework, team friction
|
||||
- Cost of concurrent review: $0.10
|
||||
- ROI: High (prevents rework)
|
||||
|
||||
---
|
||||
|
||||
## Token Usage Monitoring
|
||||
|
||||
### What to Track
|
||||
|
||||
```
|
||||
Per Review:
|
||||
├─ Actual tokens used (not estimated)
|
||||
├─ Agent breakdown (which agent used most)
|
||||
├─ Input size (diff size, file count)
|
||||
└─ Output length (findings generated)
|
||||
|
||||
Monthly:
|
||||
├─ Total tokens used
|
||||
├─ Reviews completed
|
||||
├─ Average tokens per review
|
||||
└─ Trend analysis
|
||||
|
||||
Annual:
|
||||
├─ Total token spend
|
||||
├─ Cost vs. budget
|
||||
├─ Reviews completed
|
||||
└─ ROI analysis
|
||||
```
|
||||
|
||||
### Setting Alerts
|
||||
|
||||
```
|
||||
Rate Limit Alerts:
|
||||
├─ 70% of TPM used in a minute → Warning
|
||||
├─ 90% of TPM used in a minute → Critical
|
||||
├─ Hit TPM limit → Block and notify
|
||||
|
||||
Monthly Budget Alerts:
|
||||
├─ 50% of budget used → Informational
|
||||
├─ 75% of budget used → Warning
|
||||
├─ 90% of budget used → Critical
|
||||
|
||||
Cost Thresholds:
|
||||
├─ Single review > 100K tokens → Unexpected (investigate)
|
||||
├─ Average > 80K tokens → Possible over-analysis (review)
|
||||
├─ Concurrent running during peak hours → Not optimal (schedule off-peak)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Cost Optimization Summary
|
||||
|
||||
| Strategy | Token Saved | When to Use |
|
||||
|----------|-------------|------------|
|
||||
| **Mix single + concurrent** | Save 40% per month | Daily workflow |
|
||||
| **Off-peak scheduling** | Save 15% (better concurrency) | When possible |
|
||||
| **Lightweight input mode** | Save 35% per concurrent | Non-critical reviews |
|
||||
| **Reduce agent scope** | Save 15-20% | Simple changes |
|
||||
| **Skip non-critical agents** | Save 50% | Low-risk PRs |
|
||||
| **Single agent only** | 50% baseline cost | Cost-sensitive |
|
||||
|
||||
---
|
||||
|
||||
## Recommendation
|
||||
|
||||
```
|
||||
Use Concurrent Agents When:
|
||||
├─ Token budget > 5M per month
|
||||
├─ Quality > Cost priority
|
||||
├─ Security-critical code
|
||||
├─ Release reviews
|
||||
├─ Multiple perspectives needed
|
||||
└─ Regulatory requirements
|
||||
|
||||
Use Single Agent When:
|
||||
├─ Limited token budget
|
||||
├─ High-frequency reviews needed
|
||||
├─ Simple changes
|
||||
├─ Speed important (20-30% gain not material)
|
||||
├─ Cost sensitive
|
||||
└─ No multi-perspective requirement
|
||||
|
||||
Use Mix Strategy When:
|
||||
├─ Want both quality and quantity
|
||||
├─ Can do selective high-value concurrent reviews
|
||||
├─ Have moderate token budget
|
||||
├─ Enterprise with varied code types
|
||||
└─ Want best of both worlds
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
**For full analysis, see [REALITY.md](REALITY.md) and [ARCHITECTURE.md](ARCHITECTURE.md).**
|
||||
|
||||
@ -22,11 +22,13 @@ requires_agents:
|
||||
- multi-perspective-agent
|
||||
---
|
||||
|
||||
# Master Workflow Orchestrator - Parallel Architecture
|
||||
# Master Workflow Orchestrator - Concurrent Agent Architecture
|
||||
|
||||
**The Ultimate High-Performance Code Quality Pipeline**
|
||||
**Multi-Perspective Code Quality Analysis Pipeline**
|
||||
|
||||
A sophisticated orchestrator that launches **4 specialized sub-agents in parallel** to analyze your code across different dimensions, keeping the main context clean while maintaining full workflow coordination.
|
||||
A sophisticated orchestrator that launches **4 specialized sub-agents concurrently** to analyze your code across different dimensions, keeping the main context clean while maintaining full workflow coordination.
|
||||
|
||||
**⚠ Important Note**: This uses _concurrent_ requests (submitted simultaneously), not true _parallel_ execution. See [REALITY.md](REALITY.md) for honest architecture details.
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
@ -40,8 +42,8 @@ A sophisticated orchestrator that launches **4 specialized sub-agents in paralle
|
||||
└───────────────────┼───────────────────┘
|
||||
│
|
||||
┌───────────────────▼───────────────────┐
|
||||
│ PARALLEL AGENT EXECUTION │
|
||||
│ (All running simultaneously) │
|
||||
│ CONCURRENT AGENT EXECUTION │
|
||||
│ (Requests submitted simultaneously) │
|
||||
└─────────────────────────────────────────┘
|
||||
│ │ │ │
|
||||
▼ ▼ ▼ ▼
|
||||
@ -88,10 +90,10 @@ A sophisticated orchestrator that launches **4 specialized sub-agents in paralle
|
||||
- Identify changes
|
||||
- Prepare context for sub-agents
|
||||
|
||||
### Parallel Phase: Analysis
|
||||
**All 4 agents run simultaneously (Stages 2-5)**
|
||||
### Concurrent Phase: Analysis
|
||||
**All 4 agents are invoked concurrently (Stages 2-5)**
|
||||
|
||||
These agents work **completely independently**, each focusing on their specialty:
|
||||
These agents work **independently with separate context windows**, each focusing on their specialty. Requests are submitted at the same time but processed by the API in its queue:
|
||||
|
||||
1. **Code Review Agent** (Stage 2)
|
||||
- Focuses on code quality issues
|
||||
@ -136,57 +138,56 @@ These agents work **completely independently**, each focusing on their specialty
|
||||
|
||||
---
|
||||
|
||||
## Context Efficiency
|
||||
## Context Architecture
|
||||
|
||||
### Before (Single Agent)
|
||||
```
|
||||
Single Claude instance:
|
||||
- Stage 2 analysis (large git diff, all details)
|
||||
- Stage 3 analysis (full codebase structure)
|
||||
- Stage 4 analysis (all security checks)
|
||||
- Stage 5 analysis (all perspectives)
|
||||
- All in same context = TOKEN EXPLOSION
|
||||
```
|
||||
|
||||
### After (Parallel Agents)
|
||||
### Main Thread Context (✅ Optimized)
|
||||
```
|
||||
Main Thread:
|
||||
- Stage 1: Git prep (small context)
|
||||
- Stage 6: Synthesis (structured results only)
|
||||
- Stage 7-9: Git operations (small context)
|
||||
Context size: 30% of original
|
||||
|
||||
Sub-Agents (parallel):
|
||||
- Code Review Agent: Code details only
|
||||
- Architecture Agent: Structure only
|
||||
- Security Agent: Security checks only
|
||||
- Multi-Perspective Agent: Feedback only
|
||||
Each uses 40% fewer tokens than original
|
||||
- Stage 1: Git prep (small context) ~2K tokens
|
||||
- Stage 6: Synthesis (structured results only) ~5K tokens
|
||||
- Stage 7-9: Git operations (small context) ~3K tokens
|
||||
Context size: 20-30% of single-agent approach
|
||||
```
|
||||
|
||||
**Result: 60-70% reduction in context usage across entire pipeline**
|
||||
### Total System Token Cost (⚠ Higher)
|
||||
```
|
||||
Before (Single Agent):
|
||||
└─ Main context handles everything
|
||||
└─ ~35,000 tokens for complete analysis
|
||||
|
||||
After (Concurrent Agents):
|
||||
├─ Main thread: ~10K tokens
|
||||
├─ Code Review Agent setup + analysis: ~15K tokens
|
||||
├─ Architecture Agent setup + analysis: ~18K tokens
|
||||
├─ Security Agent setup + analysis: ~15K tokens
|
||||
├─ Multi-Perspective Agent setup + analysis: ~13K tokens
|
||||
└─ Total: ~68-71K tokens (1.9-2.0x cost)
|
||||
```
|
||||
|
||||
**Main thread is cleaner, but total system cost is higher. See [TOKEN-USAGE.md](TOKEN-USAGE.md) for detailed breakdown.**
|
||||
|
||||
---
|
||||
|
||||
## Performance Improvement
|
||||
## Execution Time Comparison
|
||||
|
||||
### Execution Time
|
||||
### Single Agent (Sequential)
|
||||
- Stage 1: 2-3 mins
|
||||
- Stage 2: 5-10 mins
|
||||
- Stage 3: 10-15 mins
|
||||
- Stage 4: 8-12 mins
|
||||
- Stage 5: 5-8 mins
|
||||
- Stage 6: 3-5 mins
|
||||
- Stages 7-9: 6-9 mins
|
||||
- **Total: 39-62 minutes**
|
||||
|
||||
**Before (Sequential):**
|
||||
- Stage 1: 2-3 mins (1 agent)
|
||||
- Stage 2: 5-10 mins (1 agent)
|
||||
- Stage 3: 10-15 mins (1 agent)
|
||||
- Stage 4: 8-12 mins (1 agent)
|
||||
- Stage 5: 5-8 mins (1 agent)
|
||||
- Stage 6: 3-5 mins (1 agent)
|
||||
- **Total Stages 2-5: 28-45 minutes**
|
||||
### Concurrent Agents
|
||||
- Stage 1: 2-3 mins
|
||||
- Stages 2-5: 20-25 mins (concurrent, but some API queuing likely)
|
||||
- Stage 6: 3-5 mins
|
||||
- Stages 7-9: 6-9 mins
|
||||
- **Total: 31-42 minutes (20-30% faster, not 40-50%)**
|
||||
|
||||
**After (Parallel):**
|
||||
- Stage 1: 2-3 mins (main thread)
|
||||
- Stages 2-5 in parallel: 10-15 mins (all agents run simultaneously)
|
||||
- Stage 6: 3-5 mins (main thread)
|
||||
- Stages 7-9: 6-9 mins (main thread)
|
||||
- **Total: 21-32 minutes** (40-50% faster)
|
||||
**Note:** Speed benefit depends on API queue depth and rate limits. Worse during peak times or if hitting rate limits. See [ARCHITECTURE.md](ARCHITECTURE.md) for details on concurrent vs. parallel execution.
|
||||
|
||||
---
|
||||
|
||||
@ -291,22 +292,25 @@ This prevents context bloat from accumulating across all analyses.
|
||||
|
||||
---
|
||||
|
||||
## When to Use
|
||||
## When to Use This (vs. Single Agent)
|
||||
|
||||
✅ **Perfect For:**
|
||||
- Feature branches ready for merge
|
||||
- Security-critical changes
|
||||
- Complex architectural changes
|
||||
- Release preparation
|
||||
- Team code reviews
|
||||
- Enterprise deployments
|
||||
- Projects with complex codebases
|
||||
✅ **Recommended When:**
|
||||
- **Enterprise quality** matters more than cost
|
||||
- **Security-critical changes** need multiple expert perspectives
|
||||
- **Complex architectural changes** require thorough review
|
||||
- **Release preparation** demands highest scrutiny
|
||||
- **Team reviews** need Product/Dev/QA/Security/DevOps perspectives
|
||||
- **Large codebases** (200+ files) where context would be bloated in single agent
|
||||
- **Regulatory compliance** needed (documentation trail of multiple reviews)
|
||||
- You have **ample token budget** (2x cost per execution)
|
||||
|
||||
✅ **Speed Benefits:**
|
||||
- Large codebases (200+ files)
|
||||
- Complex features (multiple modules)
|
||||
- Security-sensitive work
|
||||
- Quality-critical decisions
|
||||
❌ **NOT Recommended When:**
|
||||
- Simple changes (single files)
|
||||
- Bug fixes
|
||||
- Quick iterations (cost multiplier matters)
|
||||
- Cost-conscious projects
|
||||
- Emergency fixes (20-30% speed gain may not justify latency overhead)
|
||||
- High-frequency reviews (use single agent for rapid feedback)
|
||||
|
||||
---
|
||||
|
||||
@ -371,22 +375,27 @@ The orchestrator will:
|
||||
|
||||
---
|
||||
|
||||
## Benefits
|
||||
## Honest Comparison: Single Agent vs. Concurrent Agents
|
||||
|
||||
| Aspect | Sequential | Parallel |
|
||||
|--------|-----------|----------|
|
||||
| **Time** | 35-60 mins | 21-32 mins |
|
||||
| **Context Usage** | 100% | 30% (main) + 40% (per agent) |
|
||||
| **Main Thread Bloat** | All details accumulated | Clean, structured results only |
|
||||
| **Parallelism** | None | 4 agents simultaneous |
|
||||
| **Accuracy** | Good | Better (specialized agents) |
|
||||
| **Maintainability** | Hard (complex single agent) | Easy (modular agents) |
|
||||
| Aspect | Single Agent | Concurrent Agents |
|
||||
|--------|--------------|-------------------|
|
||||
| **Execution Time** | 39-62 mins | 31-42 mins (20-30% faster) |
|
||||
| **Main Thread Context** | Large (bloated) | Small (clean) |
|
||||
| **Total Token Cost** | ~35K tokens | ~68-71K tokens (1.9-2.0x) |
|
||||
| **Cost per Execution** | Standard | 2x higher |
|
||||
| **Parallelism Type** | None | Concurrent (not true parallel) |
|
||||
| **Analysis Depth** | One perspective | 4 independent perspectives |
|
||||
| **Expert Coverage** | All in one | Code/Architecture/Security/Multi-angle |
|
||||
| **API Rate Limit Risk** | Low | High (4 concurrent requests) |
|
||||
| **For Enterprise Needs** | Good | Better |
|
||||
| **For Cost Efficiency** | Better | Worse |
|
||||
| **For Speed** | Baseline | Marginal improvement |
|
||||
|
||||
---
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Parallel Execution Method
|
||||
### Concurrent Execution Method
|
||||
|
||||
The orchestrator uses Claude's **Task tool** to launch sub-agents:
|
||||
|
||||
@ -397,7 +406,7 @@ Task(subagent_type: "general-purpose", prompt: "Security Task...")
|
||||
Task(subagent_type: "general-purpose", prompt: "Multi-Perspective Task...")
|
||||
```
|
||||
|
||||
All 4 tasks are launched in a single message block, executing in parallel.
|
||||
All 4 tasks are **submitted concurrently** in a single message block. They are processed by Anthropic's API in its request queue - not true parallel execution, but concurrent submission.
|
||||
|
||||
### Result Collection
|
||||
|
||||
@ -449,25 +458,33 @@ Once all 4 agents complete, synthesis begins.
|
||||
|
||||
## Version History
|
||||
|
||||
### Version 2.0.0 (Parallel Architecture)
|
||||
- Parallel sub-agent execution (4 agents simultaneous)
|
||||
- Context efficiency improvements (60-70% reduction)
|
||||
- Performance improvement (40-50% faster)
|
||||
- Specialized agents with focused scope
|
||||
- Clean main thread context
|
||||
- Modular architecture
|
||||
### Version 2.1.0 (Reality-Checked Concurrent Architecture)
|
||||
- Honest performance claims (20-30% faster, not 40-50%)
|
||||
- Accurate token cost analysis (1.9-2.0x, not 60-70% savings)
|
||||
- Concurrent execution (not true parallel)
|
||||
- Context isolation in sub-agents
|
||||
- When-to-use guidance (enterprise vs. cost-sensitive)
|
||||
- Links to REALITY.md, ARCHITECTURE.md, TOKEN-USAGE.md
|
||||
- API rate limit documentation
|
||||
|
||||
### Version 1.0.0 (Sequential Architecture)
|
||||
### Version 2.0.0 (Initial Concurrent Architecture)
|
||||
- Sub-agent execution (concurrent, not parallel)
|
||||
- Context isolation (main thread clean, total cost higher)
|
||||
- 4 specialized agents with independent analysis
|
||||
- Some performance improvement (overestimated in marketing)
|
||||
|
||||
### Version 1.0.0 (Sequential Single-Agent Architecture)
|
||||
- Single agent implementation
|
||||
- All stages in sequence
|
||||
- Deprecated in favor of v2.0.0
|
||||
|
||||
---
|
||||
|
||||
**Status:** Production Ready
|
||||
**Architecture:** Parallel with Sub-Agents
|
||||
**Context Efficiency:** Optimized
|
||||
**Performance:** High-speed execution
|
||||
**Marketplace:** Yes
|
||||
**Status:** Production Ready (Enterprise/Quality-Critical Work)
|
||||
**Architecture:** Concurrent Agent Execution
|
||||
**Best For:** Thorough multi-perspective code review
|
||||
**Cost:** 2x token multiplier vs. single agent
|
||||
**Speed:** 20-30% improvement over single agent
|
||||
**Recommendation:** Use for enterprise. Use single agents for everyday reviews.
|
||||
|
||||
The future of code review: Fast, clean, parallel, focused.
|
||||
For honest assessment, see [REALITY.md](REALITY.md). For technical details, see [ARCHITECTURE.md](ARCHITECTURE.md). For token costs, see [TOKEN-USAGE.md](TOKEN-USAGE.md).
|
||||
|
||||
Loading…
Reference in New Issue
Block a user