Building an Autonomous Claude Agent

Throughout this course, you mastered the fundamentals of tool integration with Claude: creating tool schemas, understanding Claude's tool use responses, and executing single tool requests. However, the approach you've learned so far has a significant limitation — it only handles one tool call per conversation turn. While this works perfectly for simple tasks, many real-world problems require multiple sequential steps, and often the number and nature of these steps cannot be determined in advance.

In this lesson, we'll work together to transform Claude from a single-turn tool user into an autonomous agent capable of iterative problem-solving. We'll build an agent class that can call tools, analyze results, decide what to do next, and continue this process until complex multi-step tasks are completed. This represents a fundamental shift from reactive tool usage to proactive, intelligent problem-solving that mirrors how humans approach complex challenges.

Before we start coding, let's understand how autonomous agents operate through action-feedback loops in which each tool execution provides information that influences the next decision. This iterative process mirrors human problem-solving: we take an action, observe the result, decide what to do next, and repeat until we reach our goal. The action-feedback loop consists of four key phases that repeat until task completion:

1. Decision Phase: Claude analyzes the current situation and determines the next action, which may include calling one or more tools.
2. Action Phase: Our agent executes the requested tool(s) based on Claude's instructions.
3. Feedback Phase: The results from the tool execution(s) are captured and added to the conversation history.
4. Evaluation Phase: Claude reviews the new information, decides whether the task is complete, or if additional steps are needed, and the loop continues.

This loop structure enables complex problem-solving because each iteration builds upon previous results. For example, when solving a quadratic equation, Claude might first calculate the discriminant, then use that result to determine if real solutions exist, then calculate the square root of the discriminant, and finally compute the two solutions. The key insight is that Claude doesn't need to plan all steps in advance — it can adapt its approach based on intermediate results, just like a human mathematician working through a problem.

Now let's start building our agent class to make this iterative process possible.

Let's begin by creating the foundation of our autonomous agent. We need to establish the core structure that will manage extended conversations, tool execution, and decision-making loops. We'll start with the class definition and constructor:

import json
import anthropic

class Agent:
    # Base system prompt to be used for all agents
    BASE_SYSTEM_PROMPT = (
        "You are an autonomous agent that can take multiple tool-calling steps when helpful. "
        "The user only sees your response when you stop using tools, not your tool usage or reasoning steps. "
        "When you provide your answer without calling tools, make it complete and standalone.\n"
        "Additional instructions:\n"
    )

    def __init__(
        self,
        name,
        system_prompt="You are a helpful assistant.",
        model="claude-sonnet-4-6",
        tools=None,
        tool_schemas=None,
        max_turns=10
    ):
        self.client = anthropic.Anthropic()
        self.name = name
        self.model = model
        self.system_prompt = self.BASE_SYSTEM_PROMPT + system_prompt
        self.max_turns = max_turns

        # Avoid shared mutable defaults and protect against external mutation
        self.tools = {} if tools is None else dict(tools)                # name -> Python function
        self.tool_schemas = [] if tool_schemas is None else list(tool_schemas)  # list of {name, description, input_schema}

Our agent's foundation relies on key design decisions that enable autonomous behavior while maintaining flexibility for different use cases:

• BASE_SYSTEM_PROMPT: Explicitly tells Claude that it can make multiple tool calls and that users won't see the intermediate steps — only the final result. We're combining this with a custom system_prompt to allow for domain-specific instructions while maintaining the autonomous behavior.

• Constructor parameters: Provide flexibility for different scenarios while ensuring some safe defaults:

  • name: Provides a clear identifier for the agent, useful for debugging, logging, and when working with multiple agents in complex systems.
  • system_prompt allows customization for specific domains like math or data analysis
  • model specifies which Claude model to use for agent interactions
  • tools and tool_schemas:
    • Default to None to avoid shared mutable defaults
    • Copied via dict(...) and list(...) so each agent gets its own independent registry and schema list, and to prevent later external mutations from affecting the agent
    • Accept any mapping/iterable-compatible inputs for convenience
  • max_turns prevents infinite loops by limiting the number of iterative steps

This architecture separates concerns cleanly while preparing us to implement the core functionality that will make our agent truly autonomous.

As our agent works through complex problems, we need to manage conversation state properly. Let's add two essential helper methods that will support our main loop:

def _extract_text(self, content):
    # Return a joined string of all text blocks from content
    return "".join(
        block.text for block in content
        if getattr(block, "type", None) == "text"
    )    

def _build_request_args(self, messages):
    # Create a dictionary with the basic request arguments
    request_args = {
        "model": self.model,
        "system": self.system_prompt,
        "messages": messages,
        "max_tokens": 8000,
    }

    # Add tool schemas only if they exist
    if self.tool_schemas:
        request_args["tools"] = self.tool_schemas

    # Return the complete set of arguments to use for the API call
    return request_args

These helper methods might seem simple, but they're essential for maintaining clean separation between the complex orchestration logic we're about to write and the details of message handling:

• _extract_text: Safely extracts and combines text blocks from Claude's responses, which could contain one or more text blocks mixed with other content like tool use blocks. This ensures we return clean, readable final responses to users.

• _build_request_args: Centralizes how we construct API requests, ensuring consistent parameters across all agent interactions. Notice how we conditionally include tool schemas — this prevents API errors when we create agents without tools while still supporting full tool integration when needed.

These helper methods might seem simple, but they're essential for maintaining clean separation between the complex orchestration logic we're about to write and the details of message handling.

Now let's add the method that handles individual tool executions within our agent loop. This method needs to be robust because tool failures shouldn't break our entire autonomous process:

def call_tool(self, tool_use):
    # Get the tool name, input, and id
    tool_name = tool_use.name
    tool_input = tool_use.input or {}
    tool_use_id = tool_use.id

    # Display which tool is being called
    print(f"🔧 Tool called: {tool_name}({tool_input})")
    
    try:
        # Execute the tool with the input the given input
        result = str(self.tools[tool_name](**tool_input))
    except KeyError:
        # Return an error message if the tool is not found
        result = f"Error: Tool {tool_name} not found"
    except Exception as e:
        # Return an error message if the tool fails
        result = f"Error: {str(e)}"

    # Return the tool result
    return {
        "type": "tool_result",
        "tool_use_id": tool_use_id,
        "content": result
    }

This method handles the individual tool executions that will happen within our larger iterative loop. Here's how it manages the execution flow:

1. Extract tool information: Gets the tool name, input parameters, and unique ID from Claude's tool use request
2. Debug tracking: Prints which tool is being called with what parameters — invaluable for debugging and understanding how our agent thinks
3. Execute with comprehensive error handling: Attempts to run the tool, catching both missing tools (KeyError - equivalent to checking if the tool exists in our dictionary) and execution failures (Exception)
4. Return structured results: Converts both successful results and errors into properly formatted tool result objects

The error handling ensures that tool failures don't break the entire agent loop. Instead, errors are converted into tool results that Claude can understand and potentially work around. This robustness allows our agent to continue operating even when individual tools encounter problems, making the whole system much more resilient.

Now we're ready to implement the heart of our autonomous agent: the run method. This method will manage the iterative loop that enables multi-step problem-solving. Let's start by understanding how our agent handles conversation state:

def run(self, input_messages):
    # Create a copy of the input messages to avoid modifying the original
    messages = input_messages.copy()

The input_messages.copy() is important because it ensures our agent remains stateless. Just like normal LLM API calls where you pass the complete conversation history each time, our agent doesn't store any conversation state between calls. Each time you call agent.run(), you provide the full context through input_messages, and the agent processes only that specific conversation without any memory of previous interactions.

By copying the input messages instead of modifying them directly, we preserve the original conversation and allow the same agent instance to handle multiple independent conversations. This design also gives you complete control over context management — you can decide exactly what conversation history to include, filter out irrelevant messages, or combine conversations as needed before passing them to the agent.

Now let's add the basic loop structure that will enable our agent's iterative problem-solving:

def run(self, input_messages):
    # Create a copy of the input messages to avoid modifying the original
    messages = input_messages.copy()

    # Initialize turn counter to track iterations
    turn = 0

    # Loop until the model returns a final answer or the max turns is reached
    while turn < self.max_turns:
        # Increment the turn
        turn += 1

        # Ask the model for a response
        response = self.client.messages.create(**self._build_request_args(messages))

        # Add Claude's response to messages exactly as returned (text + tool_use blocks)
        messages.append({"role": "assistant", "content": response.content})

We're starting with a controlled loop that will continue until Claude provides a final answer or we reach our maximum turn limit. Each iteration represents one complete action-feedback cycle where Claude makes a decision (potentially including tool calls), and we capture that decision in our conversation history. The turn counter prevents infinite loops while allowing sufficient iterations for complex problems.

Now let's add the logic for handling tool calls within our loop. This is where the magic of autonomous behavior happens:

def run(self, input_messages):
    # Create a copy of the input messages to avoid modifying the original...

    # Loop until the model returns a final answer or the max turns is reached
    while turn < self.max_turns:
        # Increment the turn...
        # Ask the model for a response...
        # Add Claude's response to messages...
        
        # Check if Claude wants to use any tools
        if response.stop_reason == "tool_use":
            # Initialize a list to store tool results
            tool_results = []
            # Execute each tool use
            for content_item in response.content:
                # Check if the content item is a tool use
                if content_item.type == "tool_use":
                    # Execute the tool with the input the given input
                    tool_result = self.call_tool(content_item)
                    # Add result to tool results list
                    tool_results.append(tool_result)

            # Add all tool results to messages
            messages.append({
                "role": "user",
                "content": tool_results
            })

When Claude decides to use tools, we handle the execution through a systematic process:

1. Execute all requested tools: Claude might call multiple tools in a single turn, and we need to execute each one to gather all the information it needs for its next decision.

2. Collect results systematically: We iterate through all content items in Claude's response, identify tool use blocks, and execute each tool while collecting the results in a list.

3. Feed results back as user message: By adding the tool results as a "user" message, we maintain the proper conversation flow that Claude expects, ensuring the results become part of the context for the next iteration.

Each tool result influences Claude's subsequent reasoning, allowing it to build upon what it just learned and make more informed decisions in the next turn.

Finally, let's complete our loop with the logic for handling final responses and error conditions:

def run(self, input_messages):
    # Create a copy of the input messages to avoid modifying the original...

    # Loop until the model returns a final answer or the max turns is reached
    while turn < self.max_turns:
        # Increment the turn...
        # Ask the model for a response...
        # Add Claude's response to messages...
        
        # Check if Claude wants to use any tools
        if response.stop_reason == "tool_use":
            # Execute each tool use and add results to messages...
        else:
            # Extract the text from the response
            response_text = self._extract_text(response.content)

            # Return the agent history and final output
            return messages, response_text

    # If the max turns is reached, raise an exception
    raise Exception("Max turns reached")

When Claude reaches a final answer, we handle the completion through a structured return process:

1. Detect completion: When Claude doesn't want to use tools (indicated by a different stop_reason), it signals that it has reached a final answer and no further iterations are needed.

2. Extract clean response: We use our _extract_text helper to pull out the readable text content from Claude's response, filtering out any non-text blocks.

3. Return complete state: We return both the full conversation history (messages) and the final response text (response_text) to maintain our stateless design - the caller receives everything needed to understand what happened and can use the conversation history for follow-up questions or multi-turn interactions.

4. Safety net for runaway loops: The exception for reaching max turns prevents infinite loops if something goes wrong. You can control this limit through the max_turns parameter, or alternatively implement a mechanism to force Claude to provide a final answer when approaching the limit rather than raising an exception.

This dual return approach reinforces our stateless architecture by giving the caller complete control over conversation state while providing both the detailed interaction history for continued conversations and the clean final answer for immediate use.

Here's how our complete run method looks when put together:

def run(self, input_messages):
    # Create a copy of the input messages to avoid modifying the original
    messages = input_messages.copy()
    # Initialize turn counter to track iterations
    turn = 0
    # Loop until the model returns a final answer or the max turns is reached
    while turn < self.max_turns:
        # Increment the turn
        turn += 1
        # Ask the model for a response
        response = self.client.messages.create(**self._build_request_args(messages))
        # Add Claude's response to messages exactly as returned (text + tool_use blocks)
        messages.append({"role": "assistant", "content": response.content})

        # Check if Claude wants to use any tools
        if response.stop_reason == "tool_use":
            # Initialize a list to store tool results
            tool_results = []
            # Execute each tool use
            for content_item in response.content:
                # Check if the content item is a tool use
                if content_item.type == "tool_use":
                    # Execute the tool with the input the given input
                    tool_result = self.call_tool(content_item)
                    # Add result to tool results list
                    tool_results.append(tool_result)
            # Add all tool results to messages
            messages.append({
                "role": "user",
                "content": tool_results
            })
        else:
            # Extract the text from the response
            response_text = self._extract_text(response.content)
            # Return the agent history and final output
            return messages, response_text

    # If the max turns is reached, raise an exception
    raise Exception("Max turns reached")

Now let's put our agent to work! We'll create a math-focused autonomous agent and see how it handles a complex quadratic equation. We'll provide more math tools following the same pattern used across the course, so you can easily extend your agent's capabilities as needed:

import json
from agent import Agent
from functions import sum_numbers, multiply_numbers, subtract_numbers, divide_numbers, power, square_root

# Load the schemas from JSON file
with open('schemas.json', 'r') as f:
    tool_schemas = json.load(f)

# Create a dictionary mapping tool names to functions
tools = {
    "sum_numbers": sum_numbers,
    "multiply_numbers": multiply_numbers,
    "subtract_numbers": subtract_numbers,
    "divide_numbers": divide_numbers,
    "power": power,
    "square_root": square_root
}

# Create a stateless autonomous agent
agent = Agent(
    name="math_assistant",
    system_prompt="You are a helpful math assistant.",
    tools=tools,
    tool_schemas=tool_schemas,
    max_turns=15
)

# Initialize conversation with user message
messages = [{"role": "user", "content": "Solve this equation: 2x² - 7x + 3 = 0"}]

# Send message to the stateless agent
messages, result = agent.run(messages)

# Display the response
print("\nFinal response:")
print(result)

When we run this code, our agent demonstrates sophisticated autonomous reasoning:

🔧 Tool called: power({'base': -7, 'exponent': 2})
🔧 Tool called: multiply_numbers({'a': 4, 'b': 2})
🔧 Tool called: multiply_numbers({'a': 8, 'b': 3})
🔧 Tool called: subtract_numbers({'a': 49, 'b': 24})
🔧 Tool called: square_root({'number': 25})
🔧 Tool called: multiply_numbers({'a': 2, 'b': 2})
🔧 Tool called: sum_numbers({'a': 7, 'b': 5})
🔧 Tool called: divide_numbers({'a': 12, 'b': 4})
🔧 Tool called: subtract_numbers({'a': 7, 'b': 5})
🔧 Tool called: divide_numbers({'a': 2, 'b': 4})

Final response:
The solutions to the equation 2x² - 7x + 3 = 0 are:

**x = 3** and **x = 0.5** (or x = 1/2)

You can verify these solutions by substituting back into the original equation:
- For x = 3: 2(3)² - 7(3) + 3 = 18 - 21 + 3 = 0 ✓
- For x = 0.5: 2(0.5)² - 7(0.5) + 3 = 0.5 - 3.5 + 3 = 0 ✓

Our agent systematically applied the quadratic formula by calculating b² ((-7)²), computing 4ac (4×2×3), finding the discriminant (49-24), taking the square root (√25), and finally calculating both solutions through the complete quadratic formula. Each tool call built upon previous results, demonstrating true autonomous reasoning. The agent made 10 tool calls across multiple conversation turns, yet the user only sees the final, complete answer with verification.

Together, we've successfully built an autonomous agent capable of complex, multi-step problem-solving. Our agent class encapsulates conversation management, tool execution, and iterative decision-making in a reusable structure that can tackle problems requiring dozens of sequential operations.

The architecture we created enables Claude to operate as a true autonomous agent: it can assess situations, make decisions, execute tools, learn from results, and continue iterating until complex tasks are completed. This represents a fundamental advancement from simple tool usage to intelligent, adaptive problem-solving.

In the upcoming practice exercises, you'll implement your own autonomous agents, experiment with different system prompts and tool combinations, and tackle increasingly complex multi-step problems. You'll gain hands-on experience with the debugging and optimization techniques needed for production agent systems, building upon the solid foundation we've created together.