# Reflexion

> After each episode, the agent writes a verbal lesson from the failure. Future episodes retrieve relevant lessons and run smarter — improvement without touching weights.

- **Category**: Agentic AI
- **Subcategory**: Verification & Reflection
- **Canonical URL**: https://designpattern.fyi/patterns/reflexion/

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## Description
**Intent**: Let the agent get better at recurring mistakes across episodes without fine-tuning model weights.

**Context**: Your agent solves many similar tasks over time — coding problems, research queries, workflow steps. Each task is a separate episode and the agent forgets everything. It keeps making the same errors. RL fine-tuning is too expensive to run every time a new failure mode shows up.

**Solution**: After each episode, the agent reflects on success/failure and writes a verbal lesson. Lessons are stored in long-term memory keyed by task type. Future episodes retrieve relevant lessons and prepend them to context.


## Use Cases
- Stateless agents repeat the same errors across episodes.
- Linguistic lessons from past failures can be retrieved and prepended in future runs.
- Full RL fine-tuning is too expensive for the setting.





## Trade-offs


### Advantages

- Improvement without fine-tuning weights — lessons are cheap to generate and store.

- Lessons are human-readable and editable — you can curate the knowledge base.




### Considerations & Drawbacks

- Single-agent reflexion repeats blind spots because the same model writes and reads the lessons.

- Lesson stores grow; without curation they become noise that hurts more than it helps.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/reflexion/)