Self-Improving Agents
Build agents that learn from experience and improve over time
Your Progress
0 / 5 completedKey Takeaways
The Improvement Cycle
Execute → Evaluate → Reflect → Learn. This cycle repeats continuously. Typical improvement: 20-40% accuracy gain over 3-6 months without manual intervention.
Feedback Quality Matters
Implicit signals (free, instant) + LLM-judge (scalable) + explicit ratings (targeted) + human review (critical cases). Hybrid approach balances cost and quality.
Experience Replay Foundation
Store interactions, sample batches, train from history. Most stable learning strategy. Start with 100-500 interactions before first training cycle.
Online Learning for Personalization
Update in real-time for user-specific adaptation. Risk: catastrophic forgetting. Solution: Combine with experience replay for stability.
Meta-Learning for Multi-Task
Train agent to learn how to learn. Enables rapid adaptation to new tasks with minimal examples. Requires diverse task distribution during training.
Self-Reflection for Quality
Agent critiques own outputs, identifies mistakes, generates improvements. Best for complex outputs (code, reports). Requires evaluation capability.
Four Core Components
Memory Store (save interactions), Evaluator (assess quality), Learning Engine (update from experience), Monitor (track progress). All four needed for complete system.
Monitor Improvement Rate
Incremental Updates
Small, frequent updates (hourly) beat large, rare updates (weekly). Faster adaptation, safer rollback. Version snapshots before each update.
Staging to Production
What You've Learned
✓ The four-stage improvement cycle and how to implement it
✓ How to collect and use feedback from multiple sources
✓ Four learning strategies: Experience Replay, Online Learning, Meta-Learning, Self-Reflection
✓ Production-ready code for memory, evaluator, learner, and monitor components
✓ Best practices for staged deployment and continuous monitoring