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Self-Improving Agents

Build agents that learn from experience and improve over time

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Feedback Mechanisms

Improvement requires feedback. Without knowing what works and what fails, agents cannot learn. Four primary feedback sources exist: implicit signals (automatic), explicit ratings (user-provided), LLM-as-judge (AI evaluation), and human review (expert assessment). Each has trade-offs in quality, latency, and cost.

Interactive: Feedback Source Comparison

Compare different feedback mechanisms and their characteristics:

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Implicit Signals

Task success/failure, execution time, error rates—automatic feedback from system metrics

Quality
Latency
Cost

Hybrid Approach

Best practice: Combine multiple feedback sources. Use implicit signals for immediate feedback, LLM-as-judge for quality checks, explicit ratings when available, and human review for edge cases.

Strategy:
• Implicit: Monitor every interaction (free, instant)
• LLM-Judge: Evaluate 10% sample (balanced cost/quality)
• Explicit: Ask users after failures (targeted feedback)
• Human: Review top 1% outliers (catch critical issues)

Interactive: Feedback Quality Calculator

See how feedback quality affects improvement rate:

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Design Principle

Start with cheap, high-volume feedback (implicit + LLM-judge). Add expensive, high-quality feedback (human review) only for critical decisions or unclear cases. This maximizes learning speed while controlling costs. A 90/10 split (90% automatic, 10% human) works well for most applications.

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