Reflection - joehubert/ai-agent-design-patterns GitHub Wiki

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Classification

Core Processing Pattern

Intent

The Reflection pattern implements explicit introspection steps where AI agents evaluate their own reasoning, identify flaws, and refine their approach before finalizing responses, leading to higher quality outputs and reduced errors.

Also Known As

Self-Evaluation, Metacognitive Processing, Self-Review, Introspective Reasoning

Motivation

Large Language Models often generate initial responses that contain errors, logical inconsistencies, or incomplete reasoning. Traditional approaches might require multiple user interactions to refine outputs or rely on external validation.

The Reflection pattern addresses this by building in deliberate "thinking about thinking" phases where the agent steps back from its initial reasoning, critically examines it, identifies potential issues, and refines its approach. This mimics human metacognition, where we often catch our own mistakes by reconsidering our initial thoughts.

For example, when providing financial advice, an agent might first generate recommendations, then reflect on whether it has considered all relevant factors like tax implications, risk tolerance, and time horizons before finalizing its response to the user.

Applicability

When to use this pattern:

  • When tasks require complex reasoning with multiple steps
  • For high-stakes decisions where errors could have significant consequences
  • When initial responses are likely to contain errors or oversights
  • For problems that benefit from different perspectives or angles of analysis
  • When teaching or explaining complex concepts where clarity is essential
  • When the system needs to demonstrate transparency in its reasoning process
  • When enhancing response quality is more important than response speed

Structure

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Components

  • Initial Reasoning Engine: Generates the first-pass solution or response based on user input and available knowledge
  • Reflection Prompt: A specially designed prompt that instructs the agent to critically evaluate its reasoning
  • Error Detection Module: Identifies potential logical fallacies, factual errors, or gaps in reasoning
  • Alternative Perspective Generator: Considers the problem from different angles to uncover blind spots
  • Refinement Mechanism: Integrates insights from reflection to improve the initial response
  • Meta-Reflection Monitor: Optionally tracks the quality of reflections themselves to prevent infinite recursion or diminishing returns

Interactions

  1. The agent receives a user query and generates an initial response using its reasoning capabilities
  2. Before presenting this response to the user, the agent activates its reflection mechanism
  3. The reflection process examines the initial reasoning for errors, omissions, or alternative approaches
  4. The agent compares its initial response against identified improvements
  5. If significant issues are found, the agent reformulates its answer integrating the reflection insights
  6. For complex problems, multiple reflection cycles may occur
  7. The final, refined response is presented to the user, potentially along with a summary of the reflection process for transparency

Consequences

Benefits:

  • Significantly reduces reasoning errors and logical inconsistencies
  • Improves response quality without requiring additional user input
  • Increases user trust through more reliable and thoughtful responses
  • Enables handling of more complex reasoning tasks
  • Makes the agent's thought process more transparent and explainable
  • Can identify when additional information is needed before providing a final answer

Limitations:

  • Increases latency and computational cost due to additional processing steps
  • May introduce overthinking or second-guessing that degrades simple responses
  • Can create circular reasoning patterns if not properly constrained
  • Reflection quality depends heavily on the design of reflection prompts
  • May still miss errors that are outside the agent's knowledge domain

Performance implications:

  • Typically doubles or triples the token consumption of standard responses
  • Increases response time, which may affect user experience for time-sensitive applications
  • Creates additional load on the underlying model infrastructure

Implementation

Guidelines for implementing the pattern:

  1. Design specific reflection prompts that direct the agent to examine different aspects of its reasoning
  2. Consider implementing tiered reflection based on task complexity - light reflection for simple tasks, deeper for complex ones
  3. Set clear stopping criteria to prevent infinite reflection loops
  4. Track effectiveness by measuring error rates with and without reflection
  5. Combine with other patterns like Chain-of-Thought to maximize reasoning quality
  6. For multi-agent systems, consider dedicated "critic" agents that specialize in reflection
  7. Provide mechanisms to skip reflection when immediate responses are required

Common pitfalls:

  • Reflection that merely restates the original reasoning without critical evaluation
  • Excessive reflection that introduces new errors or overthinking
  • Reflection prompts that are too generic to catch domain-specific errors

Code Examples

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Variations

  • Guided Reflection: Uses specific prompting templates to focus reflection on particular aspects (logical consistency, factual accuracy, ethical considerations)
  • Multi-Stage Reflection: Implements progressive layers of reflection with different focuses at each stage
  • Collaborative Reflection: Distributes reflection across multiple specialized agents with different expertise
  • Red-Team Reflection: Explicitly attempts to find flaws or weaknesses in the initial reasoning
  • User-Guided Reflection: Incorporates user feedback to direct the reflection process toward aspects the user considers important

Real-World Examples

  • OpenAI's Constitutional AI uses reflection processes to align responses with defined principles
  • Anthropic's Constitutional AI framework uses reflection to improve safety and alignment of responses
  • Claude models use a form of self-critique during reasoning to improve response quality
  • AutoGPT and similar autonomous agent frameworks incorporate reflection cycles to improve task planning
  • Research tools like Elicit.org use reflection to improve the quality of research summaries and analyses

Related Patterns