techwavedev Agent Performance Optimization Workflow
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
[Extended thinking: Agent optimization requires a data-driven approach combining performance metrics, user feedback analysis, and advanced prompt engineering techniques. Success depends on systematic evaluation, targeted improvements, and rigorous testing with rollback capabilities for production safety.]
Use this skill when
- Improving an existing agent's performance or reliability
- Analyzing failure modes, prompt quality, or tool usage
- Running structured A/B tests or evaluation suites
- Designing iterative optimization workflows for agents
Do not use this skill when
- You are building a brand-new agent from scratch
- There are no metrics, feedback, or test cases available
- The task is unrelated to agent performance or prompt quality
Instructions
- Establish baseline metrics and collect representative examples.
- Identify failure modes and prioritize high-impact fixes.
- Apply prompt and workflow improvements with measurable goals.
- Validate with tests and roll out changes in controlled stages.
Safety
- Avoid deploying prompt changes without regression testing.
- Roll back quickly if quality or safety metrics regress.
Phase 1: Performance Analysis and Baseline Metrics
Comprehensive analysis of agent performance using context-manager for historical data collection.
1.1 Gather Performance Data
Use: context-manager
Command: analyze-agent-performance $ARGUMENTS --days 30
Collect metrics including:
- Task completion rate (successful vs failed tasks)
- Response accuracy and factual correctness
- Tool usage efficiency (correct tools, call frequency)
- Average response time and token consumption
- User satisfaction indicators (corrections, retries)
- Hallucination incidents and error patterns
1.2 User Feedback Pattern Analysis
Identify recurring patterns in user interactions:
- Correction patterns: Where users consistently modify outputs
- Clarification requests: Common areas of ambiguity
- Task abandonment: Points where users give up
- Follow-up questions: Indicators of incomplete responses
- Positive feedback: Successful patterns to preserve
1.3 Failure Mode Classification
Categorize failures by root cause:
- Instruction misunderstanding: Role or task confusion
- Output format errors: Structure or formatting issues
- Context loss: Long conversation degradation
- Tool misuse: Incorrect or inefficient tool selection
- Constraint violations: Safety or business rule breaches
- Edge case handling: Unusual input scenarios
1.4 Baseline Performance Report
Generate quantitative baseline metrics:
Performance Baseline:
- Task Success Rate: [X%]
- Average Corrections per Task: [Y]
- Tool Call Efficiency: [Z%]
- User Satisfaction Score: [1-10]
- Average Response Latency: [Xms]
- Token Efficiency Ratio: [X:Y]
Phase 2: Prompt Engineering Improvements
Apply advanced prompt optimization techniques using prompt-engineer agent.
2.1 Chain-of-Thought Enhancement
Implement structured reasoning patterns:
Use: prompt-engineer
Technique: chain-of-thought-optimization
- Add explicit reasoning steps: "Let's approach this step-by-step..."
- Include self-verification checkpoints: "Before proceeding, verify that..."
- Implement recursive decomposition for complex tasks
- Add reasoning trace visibility for debugging
2.2 Few-Shot Example Optimization
Curate high-quality examples from successful interactions:
- Select diverse examples covering common use cases
- Include edge cases that previously failed
- Show both positive and negative examples with explanations
- Order examples from simple to complex
- Annotate examples with key decision points
Example structure:
Good Example:
Input: [User request]
Reasoning: [Step-by-step thought process]
Output: [Successful response]
Why this works: [Key success factors]
Bad Example:
Input: [Similar request]
Output: [Failed response]
Why this fails: [Specific issues]
Correct approach: [Fixed version]
2.3 Role Definition Refinement
Strengthen agent identity and capabilities:
- Core purpose: Clear, single-sentence mission
- Expertise domains: Specific knowledge areas
- Behavioral traits: Personality and interaction style
- Tool proficiency: Available tools and when to use them
- Constraints: What the agent should NOT do
- Success criteria: How to measure task completion
2.4 Constitutional AI Integration
Implement self-correction mechanisms:
Constitutional Principles:
1. Verify factual accuracy before responding
2. Self-check for potential biases or harmful content
3. Validate output format matches requirements
4. Ensure response completeness
5. Maintain consistency with previous responses
Add critique-and-revise loops:
- Initial response generation
- Self-critique against principles
- Automatic revision if issues detected
- Final validation before output
2.5 Output Format Tuning
Optimize response structure:
- Structured templates for common tasks
- Dynamic formatting based on complexity
- Progressive disclosure for detailed information
- Markdown optimization for readability
- Code block formatting with syntax highlighting
- Table and list generation for data presentation
Phase 3: Testing and Validation
Comprehensive testing framework with A/B comparison.
3.1 Test Suite Development
Create representative test scenarios:
Test Categories:
1. Golden path scenarios (common successful cases)
2. Previously failed tasks (regression testing)
3. Edge cases and corner scenarios
4. Stress tests (complex, multi-step tasks)
5. Adversarial inputs (potential breaking points)
6. Cross-domain tasks (combining capabilities)
3.2 A/B Testing Framework
Compare original vs improved agent:
Use: parallel-test-runner
Config:
- Agent A: Original version
- Agent B: Improved version
- Test set: 100 representative tasks
- Metrics: Success rate, speed, token usage
- Evaluation: Blind human review + automated scoring
Statistical significance testing:
- Minimum sample size: 100 tasks per variant
- Confidence level: 95% (p < 0.05)
- Effect size calculation (Cohen's d)
- Power analysis for future tests
3.3 Evaluation Metrics
Comprehensive scoring framework:
Task-Level Metrics:
- Completion rate (binary success/failure)
- Correctness score (0-100% accuracy)
- Efficiency score (steps taken vs optimal)
- Tool usage appropriateness
- Response relevance and completeness
Quality Metrics:
- Hallucination rate (factual errors per response)
- Consistency score (alignment with previous responses)
- Format compliance (matches specified structure)
- Safety score (constraint adherence)
- User satisfaction prediction
Performance Metrics:
- Response latency (time to first token)
- Total generation time
- Token consumption (input + output)
- Cost per task (API usage fees)
- Memory/context efficiency
3.4 Human Evaluation Protocol
Structured human review process:
- Blind evaluation (evaluators don't know version)
- Standardized rubric with clear criteria
- Multiple evaluators per sample (inter-rater reliability)
- Qualitative feedback collection
- Preference ranking (A vs B comparison)
Phase 4: Version Control and Deployment
Safe rollout with monitoring and rollback capabilities.
4.1 Version Management
Systematic versioning strategy:
Version Format: agent-name-v[MAJOR].[MINOR].[PATCH]
Example: customer-support-v2.3.1
MAJOR: Significant capability changes
MINOR: Prompt improvements, new examples
PATCH: Bug fixes, minor adjustments
Maintain version history:
- Git-based prompt storage
- Changelog with improvement details
- Performance metrics per version
- Rollback procedures documented
4.2 Staged Rollout
Progressive deployment strategy:
- Alpha testing: Internal team validation (5% traffic)
- Beta testing: Selected users (20% traffic)
- Canary release: Gradual increase (20% → 50% → 100%)
- Full deployment: After success criteria met
- Monitoring period: 7-day observation window
4.3 Rollback Procedures
Quick recovery mechanism:
Rollback Triggers:
- Success rate drops >10% from baseline
- Critical errors increase >5%
- User complaints spike
- Cost per task increases >20%
- Safety violations detected
Rollback Process:
1. Detect issue via monitoring
2. Alert team immediately
3. Switch to previous stable version
4. Analyze root cause
5. Fix and re-test before retry
4.4 Continuous Monitoring
Real-time performance tracking:
- Dashboard with key metrics
- Anomaly detection alerts
- User feedback collection
- Automated regression testing
- Weekly performance reports
Success Criteria
Agent improvement is successful when:
- Task success rate improves by ≥15%
- User corrections decrease by ≥25%
- No increase in safety violations
- Response time remains within 10% of baseline
- Cost per task doesn't increase >5%
- Positive user feedback increases
Post-Deployment Review
After 30 days of production use:
- Analyze accumulated performance data
- Compare against baseline and targets
- Identify new improvement opportunities
- Document lessons learned
- Plan next optimization cycle
Continuous Improvement Cycle
Establish regular improvement cadence:
- Weekly: Monitor metrics and collect feedback
- Monthly: Analyze patterns and plan improvements
- Quarterly: Major version updates with new capabilities
- Annually: Strategic review and architecture updates
Remember: Agent optimization is an iterative process. Each cycle builds upon previous learnings, gradually improving performance while maintaining stability and safety.
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Agent Orchestration Improve Agent"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags agent-orchestration-improve-agent ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.