> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> Introducing Agent Compass
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**Agent Compass** is an intelligent error analysis system that points AI agent development teams in the right direction. It is capable of automatically identifying issues, group similar ones, learning from mistakes, and providing actionable guidance. Developers can leverage this system to course-correct by identifying what's going wrong and how to fix it.
## What does agent compass do?
* **Error Detection & Direction**: Automatically identifies and categorizes errors in agent execution, points out possible root causes and immediate fixes
* **Learning-Based Recommendations**: Uses episodic memory from past agent runs and semantic memory from error patterns to recommend better solutions in future
* **Comprehensive Issue Tracking**: Stores analysis results, error patterns, and improvement insights to track development progress over time
* **Pattern-Based Guidance**: Automatically detects recurring problems in agent behavior and provides confidence-scored recommendations for resolution
* **Development Intelligence**: Delivers detailed statistics and real-time insights that helps you understand where your agents are failing and how to improve
## Supported Integrations
The following integrations are currently supported
## LLM Models
## Orchestration Frameworks
## Other
## Configuring agent compass
You need absolutely **zero** configuration for using Agent Compass in your observe projects. Once you start sending traces to FutureAGI, the compass picks traces according to the [sampling rate](/future-agi/products/agent-compass/quickstart#sampling-rate) and generates meaningful insights
The next section exhibits a walkthrough on setting up an observe project using the [Google ADK integration](/future-agi/integrations/google_adk) to get insights from Agent Compass
## What does agent compass do?
* **Error Detection & Direction**: Automatically identifies and categorizes errors in agent execution, points out possible root causes and immediate fixes
* **Learning-Based Recommendations**: Uses episodic memory from past agent runs and semantic memory from error patterns to recommend better solutions in future
* **Comprehensive Issue Tracking**: Stores analysis results, error patterns, and improvement insights to track development progress over time
* **Pattern-Based Guidance**: Automatically detects recurring problems in agent behavior and provides confidence-scored recommendations for resolution
* **Development Intelligence**: Delivers detailed statistics and real-time insights that helps you understand where your agents are failing and how to improve
## Supported Integrations
The following integrations are currently supported
## LLM Models