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

1. •Establish baseline metrics and collect representative examples.
2. •Identify failure modes and prioritize high-impact fixes.
3. •Apply prompt and workflow improvements with measurable goals.
4. •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:

• •In