agent-orchestration-improve-agent

name	agent-orchestration-improve-agent
description	Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
type	skill
created	"2026-02-27T00:00:00.000Z"
domain	ai-ml
category	llm-agents
risk	unknown
source	community
tags	["skill","ai-ml","llm-agents","agent","orchestration","improve"]

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:

• 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:

1. Alpha testing: Internal team validation (5% traffic)
2. Beta testing: Selected users (20% traffic)
3. Canary release: Gradual increase (20% → 50% → 100%)
4. Full deployment: After success criteria met
5. 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:

1. Analyze accumulated performance data
2. Compare against baseline and targets
3. Identify new improvement opportunities
4. Document lessons learned
5. 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.

Connections

• Domain: [[KI & Machine Learning]]
• Kategorie: [[LLM & KI-Agenten]]
• Navigation: [[Skills Uebersicht]], [[Home]]