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Project	Deployment	Preview	Comments	Updated (UTC)
agents-docs	Ready	Preview	Comment	Dec 2, 2025 9:26pm
agents-manage-api	Ready	Preview	Comment	Dec 2, 2025 9:26pm
agents-manage-ui	Ready	Preview	Comment	Dec 2, 2025 9:26pm
agents-run-api	Ready	Preview	Comment	Dec 2, 2025 9:26pm

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Staff Engineer Review Complete

I've conducted a comprehensive architectural review of this PR covering both the AGENTS.md refactoring and the context tracking evaluation system. Here's my analysis:

🎯 Overall Assessment: Strong Refactoring with Well-Architected Context Tracking

This PR successfully addresses two distinct objectives: progressive disclosure for agent guidance and sophisticated context analysis capabilities. Both are well-executed with thoughtful architectural decisions.

📋 AGENTS.md Refactoring Analysis

✅ Excellent Progressive Disclosure Implementation

The refactoring from 442 to 96 lines following HumanLayer's best practices is architecturally sound:

• Cognitive Load Reduction: Maintains ~150-200 instruction limit for frontier models
• Universal Applicability: Core AGENTS.md now contains only universally relevant commands and patterns
• Smart Information Architecture: Task-specific content properly separated into docs/agent-guides/

✅ Well-Structured Guide Files

The progressive disclosure files demonstrate good documentation patterns:

• Consistent Structure: All guides follow similar format (Stack → Commands → Guidelines)
• Practical Focus: Each guide provides immediately actionable information
• Proper Scope: Each guide covers a cohesive domain (testing, UI, docs, etc.)

⚠️ Minor Naming Inconsistency

Issue: File is named Agents.md (PascalCase) but referenced as AGENTS.md in documentation.

Recommendation:

# Standardize on AGENTS.md convention
git mv Agents.md AGENTS.md

🔍 Context Tracking System Analysis

This is a sophisticated addition that adds significant observability value. The implementation demonstrates strong system design principles:

✅ Excellent Token Estimation Strategy

agents-run-api/src/utils/token-estimator.ts:

• Model Agnostic: 4-chars-per-token approximation works across OpenAI/Anthropic/Gemini
• Performance Optimized: Simple string length calculation vs expensive tokenizer calls
• Appropriate Precision: Perfect for relative comparisons between context components

✅ Strong Type Safety & Extensibility

ContextBreakdown Interface Design:

export interface ContextBreakdown {
  systemPromptTemplate: number;
  coreInstructions: number;
  agentPrompt: number;
  // ... 11 total components with clear semantic naming
  total: number;
}

• Comprehensive Coverage: Captures all major context components
• Clear Semantics: Field names clearly indicate their purpose
• Future-Proof: Easy to extend with new components

✅ Sophisticated UI Implementation

agents-manage-ui/src/components/traces/context-breakdown.tsx:

• Progressive Visual Design: Stacked bar + detailed breakdown provides multiple levels of insight
• Accessibility: Proper ARIA labeling, tooltips, and color coding
• Performance: useMemo for expensive calculations
• User Experience: Filters out segments <0.5% to avoid visual clutter

✅ Clean Architectural Integration

System Integration Points:

• SystemPromptBuilder: Returns AssembleResult with both prompt and breakdown
• Phase1Config/Phase2Config: Implements token tracking throughout prompt assembly
• UI Components: Seamlessly integrates breakdown display in trace viewer

🔧 Architectural Considerations & Recommendations

1. Token Estimation Accuracy Trade-offs

Current Approach: Character-based approximation (~4 chars/token)
Considerations:

• ✅ Fast, model-agnostic, dependency-free
• ⚠️ May be 15-25% off for some content types (code vs prose)

Recommendation: Current approach is excellent for the use case. Consider adding a configuration option for precision-sensitive scenarios:

interface TokenEstimatorConfig {
  method: 'character-based' | 'model-specific';
  model?: string; // For future model-specific tokenizers
}

2. Context Component Granularity

Current Breakdown: 11 components tracked individually
Consideration: Balance between granularity and usability

Recommendation: Current granularity is appropriate. Consider grouping for high-level views:

interface ContextSummary {
  instructions: number; // systemPromptTemplate + coreInstructions + agentPrompt
  tools: number; // toolsSection + artifactComponents
  history: number; // conversationHistory
  // ... etc
}

3. Performance & Memory Considerations

Current Implementation: Creates breakdown objects for every prompt assembly

Considerations:

• Memory usage for high-volume scenarios
• CPU overhead of token estimation

Recommendations:

• ✅ Current implementation is efficient (simple calculations)
• Consider adding feature flag for production environments where breakdown isn't needed
• Potential optimization: Cache breakdown results for identical prompt components

🚨 Critical Items (None Found)

No critical architectural flaws or security issues identified. Both the documentation refactoring and context tracking implementation follow solid engineering principles.

🎯 Customer Impact Assessment

Positive Impacts:

1. Developer Experience: Cleaner, more discoverable agent guidance
2. Debugging Capabilities: Rich context analysis for troubleshooting token usage
3. Performance Insights: Ability to optimize prompts based on component breakdown
4. Maintainability: Progressive disclosure makes guidance easier to maintain

Risk Assessment: LOW

• No breaking changes to APIs or data contracts
• Additive features with graceful degradation
• Backward compatible changes only

📈 Future Extensibility

The architecture supports several natural extensions:

1. Token Budget Warnings: Alert when approaching model context limits
2. Component Optimization: Suggest which components to trim when over budget
3. Historical Analysis: Track context evolution over time
4. A/B Testing: Compare token efficiency across prompt variations

✅ Final Recommendation: APPROVE

This PR demonstrates excellent system architecture across both objectives:

• AGENTS.md Refactoring: Thoughtful progressive disclosure following industry best practices
• Context Tracking: Well-designed observability system with strong technical implementation

The only suggestion is the minor naming standardization for AGENTS.md. Otherwise, this is production-ready code that adds significant value to the platform.

Confidence Level: High - This is well-architected, thoroughly implemented, and follows TypeScript/React best practices throughout.