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