AI Agent Debugger is a visual debugging tool for developers building AI agents. Instead of inspecting only the final model input and output, you can trace the full agent execution: dialogue turns, model calls, tool invocations, intermediate results, errors, timing, token usage, and final output.
If you've ever asked why did the agent call that tool?, why did this response take so long?, or why did this run consume so many tokens?, an AI Agent Debugger gives you the data needed to answer those questions.
Why AI Agents Are Hard to Debug
Debugging AI agents is different from debugging deterministic application code. The failure may come from the prompt, model behavior, tool selection, API integration, orchestration logic, or a combination of all of them.
1. Non-Deterministic Behavior
LLMs can produce different outputs for the same prompt. A tool call may work in one run and fail in another, even if the code and prompt did not change.
To debug this, you need to compare multiple runs instead of relying on a single execution.
2. Long Reasoning Chains
Agents often plan, call tools, inspect results, and iterate. A mistake in step 3 may only become visible in step 10.
Without an execution trace, you only see the final failure, not the step that caused it.
3. Black Box Model Decisions
You cannot set a breakpoint inside the model. When an agent chooses an unexpected action, you need visibility into:
- The exact prompt sent to the model
- The tool definitions available at that moment
- The model response
- Any intermediate reasoning supported by the model
- The tool call parameters generated by the model
4. Tool and API Complexity
Agents frequently interact with APIs, MCP servers, local commands, and custom functions. Failures can come from:
- Wrong tool selection
- Incorrect parameters
- Authentication issues
- Invalid response formats
- Network or server errors
5. Error Attribution
When an agent fails, you need to identify whether the root cause is:
- Prompt design
- Model choice
- Tool schema
- API behavior
- MCP server configuration
- Authentication
- Agent orchestration
An AI Agent Debugger helps isolate the failing component by making each step visible.
What an AI Agent Debugger Shows
An AI Agent Debugger provides a structured execution trace for each run.
Complete Execution Trace
Use the trace to inspect:
- User prompts and system prompts: the exact context sent to the model
- Model calls: every LLM request and response
- Thinking process: model reasoning when supported
- Tool calls: MCP tools, built-in tools, or custom functions invoked by the agent
- Tool inputs and outputs: exact parameters and returned results
- Errors and exceptions: failed steps and error details
- Final output: the response returned by the agent
Session Metrics
Track runtime and cost-related data:
- Response time: total time and per-step timing
- Token consumption: input tokens, output tokens, cached tokens
- Estimated cost: cost per session
- Dialogue rounds: number of conversation turns
- Execution steps: total operations performed
Model Comparison
Run the same task with different models and compare:
- Which model completed the task in fewer steps?
- Which model selected the correct tools?
- Which model had lower latency?
- Which model consumed fewer tokens?
- Which model cost less?
Practical Use Cases
1. Debug Tool Call Chains
When your agent calls tools incorrectly, inspect:
- Which tools were called
- The order of tool calls
- The parameters passed to each tool
- The response from each tool
- The step where the chain failed
This is especially useful for agents using MCP (Model Context Protocol) servers, where tool integration issues are common.
2. Compare Model Performance
Use the same prompt and tool configuration across multiple models.
Compare:
- Execution steps
- Tool selection accuracy
- Response quality
- Token consumption
- Latency
- Estimated cost
3. Reduce Token Usage
Token visibility helps you identify expensive agent behavior.
Look for:
- Overly long system prompts
- Unnecessary context sent to the model
- Repeated tool outputs
- Verbose model responses
- Multi-step workflows that can be simplified
4. Validate MCP Server Integration
For MCP-based agents, verify:
- The MCP server connects successfully
- Tools are exposed correctly
- Authentication is configured
- Tool schemas match expected parameters
- Tool responses are parsed correctly
5. Iterate on System Prompts
Small prompt changes can significantly alter agent behavior.
Use the debugger to test prompt variants and compare:
- Tool usage
- Number of steps
- Final response quality
- Error frequency
- Token usage
Step-by-Step Guide: Using Apidog's AI Agent Debugger
Apidog provides a built-in AI Agent Debugger for inspecting agent execution traces.
Step 1: Create a New Agent Debug Session
- Open the Apidog desktop client.
- Go to AI Agent Debugger from the top tab bar.
- Configure your model in the upper section.
Model configuration includes:
- Provider: select a model provider, such as OpenAI or Anthropic
-
Model: select a specific model, such as
gpt-4oorclaude-sonnet-4-6 - Base URL: automatically matched based on the provider selection
Step 2: Configure Your Prompts
Open the Prompts tab.
Configure:
- Clear after Send: enable this if you want the input box to clear after each run
- User Prompt: the task you want the agent to execute
- System Prompt: the agent's role, constraints, and tool usage rules
Example user prompt:
Why is my POST /users endpoint returning 500 when I send a valid JSON payload?
Example system prompt:
You are a code assistant that helps developers debug API issues.
Use the available tools to fetch API responses, search documentation,
and provide actionable solutions.
A good system prompt should define:
- What the agent is responsible for
- When it should call tools
- What it should avoid doing
- How it should format the final answer
Step 3: Configure Available Tools
Open the Tools tab to select which tools the agent can use.
Built-in Tools
Apidog provides several built-in tools:
| Tool | What It Does |
|---|---|
bash |
Execute commands in a persistent shell session |
web_fetch |
Fetch web content and convert it to Markdown, text, or HTML |
read |
Read text, image, or PDF files |
edit |
Perform precise string replacement on files |
write |
Create or overwrite files |
grep |
Search file content using regular expressions |
glob |
Find files using glob patterns |
kill_shell |
Reset the current shell session |
Enable only the tools required for the task. Disabled tools are not available during execution.
MCP Tools
To connect external tools via MCP:
- Click Add MCP Server in the Tools tab.
- Choose a connection method:
- STDIO: launch a local MCP server process
- HTTP: connect to an MCP server via Streamable HTTP
- SSE: connect via Server-Sent Events
- Configure authentication if required:
- Request headers
- OAuth 2.0 authorization
- After the connection succeeds, select which tools to expose to the agent.
Step 4: Configure Skills Optional
Open the Skills tab to add reusable skills for your agent.
Skills are useful when you want to:
- Provide fixed workflows inside a project
- Reuse operation specifications for common tasks
- Avoid repeating long instructions in system prompts
During execution, relevant skills are loaded as needed based on the task.
Step 5: Configure Authentication and Model Parameters
Use the Authentication tab to add credentials required by model services or MCP services.
Use the Settings tab to configure runtime parameters:
- Temperature: controls randomness; lower values are more deterministic
- Max Tokens: limits response length
- Top P: controls nucleus sampling
- Other parameters may vary by model provider
For debugging, start with a lower temperature to reduce variability between runs.
Step 6: Run the Agent and Inspect the Trace
Click Run in the upper-right corner.
After execution, inspect the three main areas.
Session List
Each run creates a session record, for example:
Session 3
1 turn · 1 step · 10s · 3.1k tokens · $0.02
gpt-4o
Use sessions to compare different runs, prompts, tools, and models.
Turns Panel
The middle panel shows dialogue turns.
If the agent has multiple back-and-forth exchanges, each round appears here. Click a turn to inspect its trace.
Traces Panel
The Traces panel shows the complete execution flow in order:
- Prompts: exact user and system prompts
- Model calls: LLM requests and responses
- Thinking process: model reasoning if supported
- Tool calls: MCP tools and custom skills executed
- Tool details: input parameters, output results, timing, and errors
- Final output: the agent's final response
Step 7: Debug Failed Tool Calls
When a tool call fails, inspect the corresponding trace entry.
Check:
- Input parameters: did the agent pass valid values?
- Output result: did the tool return an error?
- Error message: what failed?
- Timing: did the call timeout or take longer than expected?
- Tool availability: was the tool enabled and connected?
Common causes include:
- MCP server not connected
- MCP server disconnected during execution
- Parameter format does not match the tool schema
- Incorrect authentication configuration
- Invalid OAuth token, API key, or request header
- Local STDIO startup command unavailable or incorrect
Step 8: Compare Model Performance
To choose the right model for a workflow:
- Configure the same prompts and tools.
- Run the task with Model A, such as GPT-4o.
- Run the same task with Model B, such as Claude Sonnet.
- Compare the sessions.
Focus on:
- Number of steps
- Tool selection accuracy
- Response time
- Token consumption
- Estimated cost
- Final answer quality
AI Agent Debugger vs Traditional Debugging
| Aspect | Traditional Debugging | AI Agent Debugger |
|---|---|---|
| Focus | Code logic, variables, call stack | Model calls, tool invocations, prompts |
| Visibility | Step through code line by line | Inspect the full execution trace |
| Non-determinism | Code behavior is usually reproducible | Compare multiple runs and identify patterns |
| Black boxes | Inspect variables and runtime state | Inspect model inputs and outputs, not internal weights |
| Tool integration | Debug each API separately | View all tool calls in one trace |
| Cost visibility | Not usually relevant | Track token consumption and estimated cost |
Common Questions
Why didn't my agent call the expected tool?
Check:
- Is the tool enabled in the Tools tab?
- Does the system prompt clearly explain when to use the tool?
- Is the MCP server connected?
- Is the tool exposed and not disabled?
- Does the trace show any model reasoning or tool call attempts?
- Does the selected model support tool calling?
My MCP tool calls keep failing. What should I check?
Open the failed tool call in the Traces panel and inspect:
- Input parameters: confirm the format matches the tool schema
- Output result: read the exact error returned by the tool
- Connection status: confirm the MCP server is still connected
- Authentication: verify API keys, OAuth tokens, and request headers
- STDIO command: confirm the local server startup command is valid
Why should I run the same task multiple times?
Agents are non-deterministic. Multiple runs help you:
- Observe behavior variance
- Compare execution paths
- Identify unstable prompts
- Evaluate model consistency
- Tune temperature, tools, and system prompts
Getting Started
AI Agent Debugger is available in Apidog, an API development platform.
To start debugging your AI agents:
- Download the latest Apidog desktop client
- Open AI Agent Debugger from the top tab
- Configure your model, prompts, and tools
- Run the agent
- Inspect every model call, tool invocation, error, token, and output
The Bottom Line
AI Agent Debugger turns agent debugging into a traceable engineering workflow. Instead of guessing why an agent behaved unexpectedly, you can inspect what happened at each step: prompts, model calls, tool invocations, errors, timing, token usage, and final output.
As agents rely on more tools, APIs, and MCP integrations, this level of visibility is essential for building reliable and cost-effective agent systems.
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