Agent performance optimization

--- name: agent-orchestration-improve-agent description: "Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration." risk: unknown source: community date_added: "2026-02-27" --- # 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 be