AI Agent Tool Use Architecture: Function Calling, ReAct Loops & Structured Outputs

Large language models become dramatically more useful when they can take actions — reading files, querying databases, calling APIs. Tool use (also called function calling) is the mechanism that turns a chatbot into an agent.

The Core Idea#

Without tools, an LLM can only generate text. With tools, it can:

User: "What's the weather in Tokyo?"

Without tools → "I don't have real-time data..."
With tools    → calls get_weather("Tokyo") → "It's 22°C and sunny in Tokyo."

The model doesn't execute the tool itself. It requests a tool call, the runtime executes it, and the result is fed back into the conversation.

Tool Definitions#

Every tool use system starts with tool definitions — structured descriptions the model uses to decide when and how to call a tool.

{
  "name": "search_database",
  "description": "Search the product database by query string. Returns top 10 matches.",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "The search query"
      },
      "category": {
        "type": "string",
        "enum": ["electronics", "clothing", "books"],
        "description": "Optional category filter"
      }
    },
    "required": ["query"]
  }
}

Key principles for good tool definitions:

1. Clear descriptions — the model reads these to decide when to use the tool
2. Precise parameter types — enums, required fields, format hints
3. Scoped responsibility — one tool does one thing well
4. Example values in descriptions help the model generate correct inputs

The ReAct Loop#

ReAct (Reasoning + Acting) is the dominant pattern for agentic tool use. The model alternates between thinking and acting:

Step 1: THINK  → "I need to find the user's order status. I'll search by email."
Step 2: ACT   → call search_orders(email="user@example.com")
Step 3: OBSERVE → [Order #1234, shipped, tracking: XYZ789]
Step 4: THINK  → "Found the order. Now I need tracking details."
Step 5: ACT   → call get_tracking("XYZ789")
Step 6: OBSERVE → [In transit, arriving March 30]
Step 7: RESPOND → "Your order #1234 is in transit, arriving March 30."

Each iteration the model sees the full history of thoughts, actions, and observations. This lets it plan multi-step workflows dynamically.

Tool Selection Strategies#

When an agent has dozens of tools available, selection becomes critical:

Relevance Filtering#

# Pre-filter tools based on the query before sending to the model
relevant_tools = [t for t in all_tools if is_relevant(t, user_query)]
# Send only relevant tools to reduce confusion and token cost

Hierarchical Tool Organization#

Top-level tools:
  - database_tools    → search, insert, update, delete
  - communication     → send_email, send_slack, create_ticket
  - file_operations   → read_file, write_file, list_directory

The model first selects a category, then gets specific tools within it.

Tool Selection via Embeddings#

For large tool inventories (100+), embed tool descriptions and retrieve the top-k most similar tools based on the user query. This scales better than sending all definitions.

Claude Tool Use API#

Claude's tool use follows a structured message flow:

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=[{
        "name": "get_weather",
        "description": "Get current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }],
    messages=[{"role": "user", "content": "Weather in Paris?"}]
)

# Response contains tool_use content block
# Execute the tool, then send result back

Claude returns a tool_use block with the tool name and input. You execute it and send back a tool_result message. The model then generates its final response.

GPT Function Calling API#

OpenAI's approach uses a similar pattern with slightly different structure:

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Weather in Paris?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }]
)

The model returns tool_calls in the response message. You execute them and append tool role messages with results.

Parallel Tool Calls#

Both Claude and GPT support parallel tool calls — requesting multiple tool executions in a single response:

User: "Compare weather in Tokyo and London"

Model response (single turn):
  → tool_call_1: get_weather("Tokyo")
  → tool_call_2: get_weather("London")

This is faster than sequential calls because both execute concurrently. The runtime collects all results and sends them back together.

When parallel calls help:

• Independent data fetches (weather in two cities)
• Gathering context from multiple sources simultaneously
• Batch operations where order doesn't matter

When to force sequential:

• Second call depends on first call's result
• Write operations that must happen in order
• Transactions requiring consistency

Structured Outputs#

Tool use naturally produces structured data, but you can also force structured outputs for the model's final response:

{
  "type": "json_schema",
  "json_schema": {
    "name": "analysis_result",
    "schema": {
      "type": "object",
      "properties": {
        "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]},
        "confidence": {"type": "number"},
        "key_topics": {"type": "array", "items": {"type": "string"}}
      },
      "required": ["sentiment", "confidence", "key_topics"]
    }
  }
}

Structured outputs guarantee the response matches your schema — no parsing regex needed.

Error Recovery#

Tools fail. Networks time out. APIs return errors. Robust agents handle this gracefully:

Retry with Backoff#

def execute_tool_with_retry(tool_call, max_retries=3):
    for attempt in range(max_retries):
        try:
            return execute_tool(tool_call)
        except ToolError as e:
            if attempt == max_retries - 1:
                return {"error": str(e)}
            time.sleep(2 ** attempt)

Fallback Tools#

Primary: search_vector_db(query)
  ↓ fails
Fallback: search_keyword_db(query)
  ↓ fails
Final: return "I couldn't find relevant results"

Error Context for the Model#

When a tool fails, return the error to the model. Good agents adapt:

Tool result: {"error": "Rate limited. Retry after 30 seconds."}
Model thinks: "I'll try a different approach — let me use the cached data tool instead."

Security Considerations#

Tool use introduces real-world side effects. Guard against:

1. Prompt injection — malicious inputs that trick the model into calling dangerous tools
2. Over-permissioning — give tools minimum necessary permissions
3. Confirmation gates — require human approval for destructive operations (delete, send email)
4. Input validation — validate tool inputs before execution, not just after
5. Rate limiting — cap tool calls per conversation to prevent runaway loops

Architecture Pattern: Tool Use Runtime#

┌─────────────┐     ┌──────────┐     ┌───────────┐
│   User       │────▶│  Agent   │────▶│  LLM API  │
│   Input      │     │  Runtime │◀────│  (Claude)  │
└─────────────┘     │          │     └───────────┘
                    │  ┌───────┴──────┐
                    │  │ Tool Registry │
                    │  │ - search_db   │
                    │  │ - send_email  │
                    │  │ - read_file   │
                    │  └───────┬──────┘
                    │          │
                    │  ┌───────▼──────┐
                    └──│  Tool Results │
                       └──────────────┘

The runtime orchestrates the loop: send messages to the LLM, parse tool calls, execute tools, feed results back, repeat until the model generates a final response.

Key Takeaways#

1. Tool definitions are the interface — invest in clear descriptions and precise schemas
2. ReAct loops let models reason about multi-step problems dynamically
3. Parallel tool calls reduce latency for independent operations
4. Structured outputs eliminate parsing headaches
5. Error recovery is essential — tools fail, good agents adapt
6. Security is non-negotiable when tools have real-world side effects

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