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Hi everyone, Shivam Goyal here! This blog series, based on Microsoft’s AI Agents for Beginners repository, continues with an exciting topic: Metacognition in AI Agents. In previous posts ([links to parts 1-8 at the end]), we’ve covered fundamental concepts and design patterns. Now, we’ll explore how to equip AI agents with the ability to “think about thinking,” enabling them to evaluate, adapt, and improve their own cognitive processes.

What is Metacognition?

Metacognition, often described as “thinking about thinking,” refers to higher-order cognitive processes that involve self-awareness and self-regulation of one’s cognitive activities. In AI, this means enabling agents to evaluate their actions, identify errors, and adjust strategies based on past experiences. This self-awareness allows AI agents to make better decisions, adapt to new situations, and improve performance over time.

Why is Metacognition Important for AI Agents?

Metacognition is crucial for:

• Self-Reflection: Agents can assess their performance and pinpoint areas for improvement.


• Adaptability: They can modify strategies based on experiences and changing environments.


• Error Correction: Agents can detect and correct errors autonomously.


• Resource Management: They can optimize resource usage (time, computation) through planning and evaluation.

Key Components of an AI Agent with Metacognition

An AI agent incorporating metacognition builds upon these basic components:

• Persona: Defines the agent’s personality and interaction style.


• Tools: Represents the agent’s capabilities and functions.


• Skills: Encompasses the agent’s knowledge and expertise.

These components work together to form an “expertise unit,” but metacognition adds a layer of self-awareness and adaptability.

Metacognition in Action: A Travel Agent Example

Consider a “Travel Agent” AI assisting users with vacation planning. It can leverage metacognition to provide more personalized recommendations by:

• Analyzing user feedback to determine what was well-received and what wasn’t.


• Adapting to user preferences (e.g., avoiding crowded places if a user previously expressed a dislike for them).


• Correcting past errors (e.g., rigorously checking hotel availability after previously recommending a fully booked hotel).

Planning in Agents

Planning is a key aspect of AI agent behavior, involving outlining the steps required to achieve a goal while considering the current state, resources, and potential obstacles. The Travel Agent follows these steps:

1. Gather User Preferences


2. Retrieve Information (flights, hotels, attractions)


3. Generate Recommendations


4. Present Itinerary to User


5. Collect Feedback


6. Adjust Based on Feedback


7. Final Confirmation


8. Book and Confirm Reservations


9. Provide Ongoing Support

Corrective RAG Approach

Corrective Retrieval-Augmented Generation (RAG) focuses on correcting errors and improving accuracy. This involves:

• Prompting Technique: Using specific prompts to guide information retrieval.


• Tool: Implementing algorithms to evaluate the relevance of retrieved information.


• Evaluation: Continuously assessing the agent’s performance and making adjustments.

Compared to Pre-emptive Context Load, which is what happens if we load travel destinations into the agent system.

Demonstrating Metacognition in Code

To demonstrate metacognition, let’s create a simple agent that reflects on its decision-making process. We’ll use a hotel recommendation agent as an example. It starts with the goal of picking cheapest hotel but the result aren’t of good quality, so it uses best quality instead.

The key aspects of this code illustrating metacognition are: the ability to evaluate previous choices, and adjust it’s strategies as a result.

Summary and Further Exploration

Metacognition is a powerful tool for enhancing AI agents, making them more intelligent, adaptable, and efficient. Explore the provided code examples and additional resources to further delve into this fascinating area.

Further Resources and Learning

• ai-agents-for-beginners/09-metacognition at main · microsoft/ai-agents-for-beginners


• AI Agents for Beginners Repository

Catch up on the series:

• Part 1: Introduction to AI Agents


• Part 2: Exploring Agentic Frameworks


• Part 3: Agentic Design Principles


• Part 4: Tool Use Design Pattern


• Part 5: AI Agents: Mastering Agentic RAG


• Part 6: Building Trustworthy Agents


• Part 7: Planning and Orchestration with the Planning Design Pattern


• Part 8: Multi-Agent Design Pattern

If you have any further questions or would like to connect for more discussion, feel free to reach out to me on and continue your learning and discussions on the Microsoft Azure AI Foundry Discord.