---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 7b5fcc2a5625111235bc2e3955a9c9d25a24810db8e0838c2e0fca61d92c1898
  pageDirectory: concepts
  sources:
    - tracing-ag2-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - agent-tool-registration-pattern
    - ATRP
  citations:
    - file: tracing-ag2-databricks-on-aws.md
title: Agent Tool Registration Pattern
description: A design pattern in AG2 where a function is registered twice — once for the LLM to call (register_for_llm) and once for execution (register_for_execution) — decoupling tool discovery from secure execution.
tags:
  - ag2
  - tool-use
  - agent-patterns
timestamp: "2026-06-19T23:08:09.271Z"
---

# Agent [Tool Registration Pattern](/concepts/tool-registration-pattern.md)

The **Agent Tool Registration Pattern** is a design approach for equipping an AI agent with callable tools in a multi‑agent workflow. It separates the definition of a tool function from its declaration to the language model (LLM) and its registration for actual execution, ensuring that the LLM learns about the tool’s interface while the runtime system knows how to invoke it. This pattern is demonstrated in the AG2 (AutoGen 0.2) framework with Databricks [MLflow](/concepts/mlflow.md) tracing. ^[tracing-ag2-databricks-on-aws.md]

## Pattern Overview

1. **Define the tool function** – Write a Python function that performs the desired operation, including type hints and a description of its parameters (e.g., using `typing.Annotated` to provide a parameter description).  
2. **Register the tool for the LLM** – Use a method such as `register_for_llm` to inform the LLM of the tool’s name, its expected parameters, and a human‑readable description. This step enables the LLM to request the tool by name during a conversation.  
3. **Register the tool for execution** – Use a method such as `register_for_execution` on the agent that will actually run the tool. This step associates the tool’s name with its callable implementation, allowing the agent to fulfil the LLM’s request.  
4. **Enable tracing** – Activate auto‑tracing (e.g., `mlflow.ag2.autolog()`) to capture the tool calls, their inputs and outputs, and the overall conversation for observability and debugging.  
5. **Initiate the multi‑agent conversation** – Start the conversation between agents; the LLM can now decide to invoke the registered tool, and the execution agent will run it. ^[tracing-ag2-databricks-on-aws.md]

## Code Example

The following snippet illustrates the pattern with a simple calculator tool: ^[tracing-ag2-databricks-on-aws.md]

```python
# Define the tool function
def calculator(a: int, b: int, operator: str) -> int:
    if operator == "+": return a + b
    if operator == "-": return a - b
    if operator == "*": return a * b
    if operator == "/": return int(a / b)
    raise ValueError("Invalid operator")

# Register for LLM usage (name + description)
assistant.register_for_llm(
    name="calculator",
    description="A simple calculator"
)(calculator)

# Register for execution on the tool‑executing agent
user_proxy.register_for_execution(name="calculator")(calculator)
```

After registration, the LLM can call `calculator` during the conversation, and the tool‑executing agent runs the actual function.

## Benefits

- **Separation of concerns** – The LLM only needs to know the tool’s signature and purpose; the execution agent handles the actual implementation.  
- **Observability** – Auto‑tracing (e.g., [MLflow](/concepts/mlflow.md)) records every tool invocation, making it easy to debug and audit agent decisions.  
- **Extensibility** – New tools can be added by following the same three‑step pattern without changing the core agent logic. ^[tracing-ag2-databricks-on-aws.md]

## Related Concepts

- [Multi-Agent Workflow](/concepts/multi-agent-workflow-termination.md) – Coordinated conversations between multiple agents that can use tools.
- [LLM Tool Calling](/concepts/llm-function-calling.md) – The general capability of large language models to invoke external functions.
- AG2 (AutoGen 0.2) – The framework that provides `register_for_llm` and `register_for_execution`.
- [MLflow Tracing](/concepts/mlflow-tracing.md) – A mechanism for capturing and inspecting agent interactions and tool calls.

## Sources

- tracing-ag2-databricks-on-aws.md

# Citations

1. [tracing-ag2-databricks-on-aws.md](/references/tracing-ag2-databricks-on-aws-417b54da.md)
