Generates initial contract summary
AI Models & Assistants / Knowledge library
Reflection Loop
Reviews a draft or trajectory, identifies shortcomings, and uses feedback for another attempt
Why learn Reflection Loop?
It can improve complex outputs by making detected errors actionable in a subsequent iteration.
The problem it solves
It provides a correction path when a first-pass answer or plan is incomplete.
Where it came from
Reflection loops add a critique step after generation or action. Feedback may come from the same model, a separate model, tests, or environment signals; self-critique alone can repeat the same blind spots.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define Reflection Loop and identify its place in an agent architecture.
- Choose a task where Reflection Loop improves an agent system.
- Diagnose one failure caused by misusing Reflection Loop.
- Interpret a trace or state transition involving Reflection Loop.
- Explain one operational tradeoff of Reflection Loop.
Documentation and sources
Connect the ideas
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Real World Example
Automated Legal Contract Compliance Review
Corporate legal departments managing high-volume vendor contract renewals.
The business problem
Manual contract review is prone to oversight, leading to missed compliance clauses and inconsistent risk assessment across thousands of documents.
How this topic solves it
The Reflection Loop framework enables an AI agent to draft a compliance summary, receive environment feedback (e.g., missing mandatory clauses), and perform a self-reflective critique to refine the summary before final submission.
- Generate an initial compliance summary based on the raw contract text.
- Execute a validation script to check for required legal clauses and mandatory terminology.
- Pass the validation error logs as linguistic feedback to the agent.
- Instruct the agent to analyze the feedback and generate a revised, compliant summary.
- Store the successful reflection and final summary in an episodic memory buffer for future similar contract types.
The flow shows how an agent iterates on a task using feedback.
Checks for missing clauses
Provides error logs to agent
Stores successful strategies
- Drafting Agent → submits draft → Validator
- Validator → identifies errors → Feedback Loop
- Feedback Loop → provides critique → Drafting Agent
- Drafting Agent → saves refinement → Memory Buffer
Expected business outcome
Improved accuracy in identifying missing compliance requirements without requiring expensive model fine-tuning.
Limitations and checks
The system may suffer from 'reflection drift' where the agent over-corrects based on feedback, potentially introducing new errors; this is checked by monitoring the delta between consecutive draft versions.