Reflection in AI Agents

Overview

Reflection is a key design pattern in agentic AI workflows. It enables an LLM to evaluate its own outputs by analyzing its past steps, along with observations from tools or the environment, to assess the quality of its actions. This process can lead to improved performance through re-planning, refining, or re-evaluating the task at hand.

In essence, reflection allows an LLM to act as its own critic, spotting errors or inefficiencies in its responses and iteratively improving its output. This pattern is simple to implement yet delivers surprising performance gains in many use cases.

How Reflection Works

  1. Initial Output Generation: The agent generates an initial response or performs an action based on the given task.

  2. Critical Evaluation: The agent evaluates its output by reflecting on its correctness, style, and efficiency. It identifies areas for improvement or errors in the response.

  3. Feedback Incorporation: The agent uses the constructive feedback from its reflection process to revise and improve the output.

  4. Iterative Refinement: The reflection-feedback loop can be repeated to achieve further improvements in the output, ensuring higher-quality results.

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Example Use Case

Consider an LLM tasked with writing code for a specific function:

  1. The agent generates the initial code.

  2. It reflects on the output, asking itself:

    • Is the code correct and efficient?

    • Are there improvements to be made in style or performance?

  3. The agent provides constructive feedback on its own code and uses it to rewrite the code.

  4. After iterating through this process, the agent produces a refined and optimized solution.

This reflection process can also incorporate external tools like unit tests to validate the code’s correctness before producing the final output.

Exercise: Reflection in Agents - Practical Example

Let’s see the Reflection in Agents in Action!

From the agentic-workshop/lab-materials/05 folder, please open the notebook called 5.5-reflection-agents.ipynb and follow the instructions.

Advanced Reflection: Multi-Agent Approach

Reflection can be enhanced by leveraging a multi-agent framework:

  • Generator Agent: Responsible for producing the initial output.

  • Critic Agent: Focused on critically evaluating the Generator Agent’s output and providing constructive feedback.

This interaction between agents mimics a collaborative review process, leading to more reliable and polished results. The discussion between the agents allows for deeper analysis and improvement of the output.

Benefits of Reflection

  • Improved Quality: Enables the agent to identify and correct its own mistakes, leading to better results.

  • Iterative Refinement: The process of repeated self-evaluation drives continuous improvement.

  • Tool Integration: Reflection can incorporate external tools, like unit tests or web searches, to validate outputs.

  • Versatility: Applicable to tasks like code generation, text writing, and question answering.