Self-Improving Agent with Dynamic Context and Continuous Learning
Source: https://ashpreetbedi.com/self-improving-agent Author: Ashpreet Bedi Date: 2025-12-16
Summary
Technical implementation of a self-improving agent that uses dynamic context updating and continuous learning. The agent tracks its own performance, identifies failure patterns, and updates its context (system prompt, few-shot examples) accordingly.
Key Claims
- Architecture: agent + performance tracker + context updater. The tracker logs success/failure; the updater modifies the system prompt based on patterns.
- Dynamic context: the system prompt isn’t static — it’s regenerated each session based on accumulated performance data.
- Continuous learning without retraining: all improvement comes from context changes, not weight updates. This is fast and reversible.
- Self-observation: the agent writes a “post-task reflection” that gets added to the performance log — structured introspection.
- Limitation: context changes can only fix surface-level failures. Deep reasoning failures require model retraining.
Connection to Other Sources
Complements Factory Signals (external signals driving context updates) and mem-agent (RL-trained memory management).
Concepts
- Agent Memory — dynamic context as performance-informed memory
- Coding Agents — self-improvement loop for coding agents
- Context Engineering — dynamic context updating