Agent Orchestration Improve Agent

# 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 though