#013: Building Reasoning Agents with Agno: A Comprehensive Tutorial

The code examples that you are going to get in this post are going to be absolutely mindblowing!

Imagine an AI agent that doesn't just respond with the first solution it finds or based on first tool it uses, but methodically works through complex problems—step by step, validating its work, and catching errors before they happen and calling more tools when needed.

Reasoning agents represent a significant advancement in AI capabilities, using simple tweaks and tool use to make models to work through complex problems methodically before producing final outputs. This tutorial explores how to build reasoning agents using the Agno framework, drawing inspiration from Anthropic's "think" tool approach.

First a friendly reminder…

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What Are Reasoning Agents?

According to Agno's documentation, reasoning is the ability to think and validate before responding or taking action. Reasoning agents are AI systems designed to:

• Break down complex problems into logical steps

• Think through problems systematically before responding

• Employ tools when necessary to gather information

• Backtrack, correct, and validate reasoning steps

• Make consistent decisions across multiple attempts

As described by Ashre Puri, creator of Agno, reasoning agents implement a form of "agentic reasoning" where the agent "works through a problem step by step before presenting a response to the user." This approach combines Chain of Thought reasoning with tool use and adds the ability to backtrack, correct, and validate reasoning steps as needed.

Agno supports three approaches to reasoning:

1. Reasoning Models

2. Reasoning Tools

3. Reasoning Agents

The "Think" Tool Concept

Anthropic's research on the "think" tool provides valuable insights for implementing reasoning agents. The "think" tool creates dedicated space for structured thinking during complex tasks, allowing models to:

1. Stop and consider whether they have all necessary information

2. Process external information gathered from tools

3. Perform focused reasoning on new information

4. Navigate policy-heavy environments with detailed guidelines

5. Make sequential decisions where each step builds on previous ones

The core idea is to provide a dedicated space for the model to think through problems before taking actions or responding to users.

Ready to transform your AI assistant from a fast-but-flawed responder into a careful, methodical thinker that rivals human experts? Let's dive in.