Reflection Pattern

The reflection pattern is one of the most powerful techniques in agentic AI. It enables an agent to critique its own output and iteratively improve it—much like how a human writer revises their draft.

What is Reflection?

Reflection is when an AI agent evaluates its own response, identifies weaknesses, and generates an improved version. This self-critique loop can dramatically improve output quality.

# Basic reflection pattern
def reflect_and_improve(initial_response, task):
    critique = llm.generate(f"""
    Review this response for the task: {task}
    Response: {initial_response}

    Identify:
    1. What's missing?
    2. What could be clearer?
    3. Any factual issues?
    """)

    improved = llm.generate(f"""
    Original: {initial_response}
    Critique: {critique}

    Generate an improved response addressing the critique.
    """)

    return improved

Why Reflection Works

Research shows that LLMs often produce better results when asked to:

1. Self-evaluate before finalizing
2. Consider alternatives they might have missed
3. Check for errors in reasoning or facts

Real-World Applications

Use Case	Reflection Approach
Code generation	Review for bugs, edge cases, best practices
Content writing	Check tone, accuracy, completeness
Analysis	Verify assumptions, consider counterarguments
Problem solving	Validate logic, explore alternatives

Key Considerations

• Iteration limit: Set a maximum number of reflection cycles (2-3 is often optimal)
• Stopping criteria: Define when improvement is "good enough"
• Cost awareness: Each reflection cycle consumes tokens

Next, we'll explore how agents extend their capabilities through tool use. :::