> For the complete documentation index, see [llms.txt](https://ai-security-docs.akto.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-security-docs.akto.io/akto-argus-agentic-ai-security-for-homegrown-ai/agentic-ai-discovery/concepts/agent-architecture-and-trace-analysis.md).

# Context Graph & Trace Analysis

## Overview

Context Graph & Trace Analysis helps you visualise how components inside your agentic collection interact and execute during runtime.

Using architecture graphs and execution traces, you can understand:

* How requests flow through your system
* Which models, tools, and MCP servers are involved
* How agents interact during execution
* What happens during individual request runs

This gives you end-to-end visibility into your agentic workflows.

## Context Graph

The Context Graph provides a visual representation of your collection architecture and execution relationships between components.

It helps you understand how your agentic system is structured and how requests move across connected components.

<div data-with-frame="true"><figure><img src="https://3128331180-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Ftog5ODwYfqPOf4eQhsOC%2Fuploads%2Fgit-blob-664835666b77ecbb26f2a671b1722b514cea8efa%2Fimage.png?alt=media" alt="" width="563"><figcaption></figcaption></figure></div>

### What You Can See

The graph includes all major components inside your collection, such as:

* AI Agents, LLMs, MCP Servers, Tools, Webhooks, External integrations

Each node represents a component, while edges represent execution or communication flow between components.

{% hint style="info" %}

## Why It Matters

Using the Context Graph, you can:

* Understand your orchestration architecture
* Identify dependencies between agents and tools
* Inspect model and MCP integrations
* Analyse request execution paths
* Validate workflow connectivity before running scans
  {% endhint %}

### Component Relationships

The graph helps you visualise:

* Agent-to-model communication
* Tool invocation paths
* MCP server interactions
* Input and output routing
* Chained execution flows

## Trace Analysis

Trace Analysis helps you inspect how individual components executed during runtime.

Each component can contain multiple traces, where every trace represents a separate execution instance triggered by a request.

<div data-with-frame="true"><figure><img src="https://3128331180-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Ftog5ODwYfqPOf4eQhsOC%2Fuploads%2Fgit-blob-6438b10a272b892dd03a5adfb0b81e4514d83349%2Fimage.png?alt=media" alt="" width="563"><figcaption></figcaption></figure></div>

### Trace Overview

For every component, Akto captures multiple execution traces generated from separate request runs.

Each trace contains:

<table><thead><tr><th width="196.80859375">Field</th><th>Description</th></tr></thead><tbody><tr><td>Trace Name</td><td>Identifier for the execution trace.</td></tr><tr><td>Agent Name</td><td>Agent associated with the execution run.</td></tr><tr><td>Root Span ID</td><td>Primary span identifier for the trace.</td></tr><tr><td>Total Spans</td><td>Total execution spans captured in the request lifecycle.</td></tr><tr><td>Execution Sequence</td><td>Ordered flow of component execution for the request.</td></tr><tr><td>Component Outputs</td><td>Runtime outputs generated at each execution stage.</td></tr></tbody></table>

You can navigate between traces to compare execution behavior across runs.

### Execution Graph

Each trace includes a visual execution graph that maps the complete request lifecycle for that execution run.

The graph helps you inspect:

* Trigger source
* Agent execution
* LLM calls
* Tool invocations
* Response generation

Each node contains the runtime output associated with that stage.

{% hint style="info" %}

## Why Trace Analysis Matters

Using traces, you can:

* Debug agent workflows faster
* Compare behavior across executions
* Validate tool responses
* Verify LLM interactions
* Investigate orchestration failures
* Analyze runtime execution end-to-end
  {% endhint %}

## Common Use Cases

1. **Debug Agent Execution**\
   Inspect how an agent processed a request and which tools were invoked during a specific run.
2. **Compare Multiple Traces**\
   Analyse behavioural differences across multiple executions for the same component.
3. **Validate Tool Responses**\
   Verify whether connected tools returned expected outputs.
4. **Analyse Model Interactions**\
   Review model-generated responses and downstream execution behavior.
5. **Troubleshoot Failures**

   Identify where execution failed across spans or connected components.