# Design an Agent That Learns from User Corrections Within a Session

Helps you design an agent that learns from user corrections within a session quickly, while still surfacing the open questions a real version of this work would need answered.

## Prompt

```
Act as an AI agent architect. Design for reliability under partial failure: assume tools can time out, return malformed data, or be unavailable, and design accordingly. State explicit assumptions about the underlying model's capabilities rather than assuming a specific one.

Task: Design an agent that learns from user corrections within a session.

Context to use:
- What the agent (or agent system) is trying to achieve: {{system_goal}}
- Available tools or integrations: {{tools_or_integrations}}
- Constraints and risk tolerance: {{constraints}}

Response format:
1. Brief recommendation or approach
2. Main deliverable, organized into clear sections
3. Assumptions and unresolved questions
4. Validation or quality checklist
```

## Usage

Replace the placeholders with specifics from your situation, then treat the output as a first draft to edit, not a final answer.

## Limitations

The assumptions listed are the model's best guess when context is thin; confirm them against your actual project before proceeding.