Agentic Loop Lifecycle

Core

Design and implement agentic loops for autonomous task execution · Difficulty 2/5

agenticloopscontrol-flowstop-reason

An Agentic Loop is the core pattern for building AI agents: repeatedly calling Claude, executing requested tools, and feeding results back until the task is complete.

The Loop

while True:
    response = call_claude(messages)
    
    if response.stop_reason == 'end_turn':
        break  # Task complete
    
    if response.stop_reason == 'tool_use':
        results = execute_tools(response.tool_calls)
        messages.append(response)
        messages.append(tool_results(results))

Loop Control

Primary signal: `stop_reason field (tool_use = continue, end_turn` = stop)

Safety guardrail: Maximum iteration count to prevent runaway loops

NOT reliable: Parsing natural language for completion signals

Stop Reason Values

Value	Meaning	Action

|-------|---------|--------|

`end_turn`	Claude finished its response naturally	Present response to user
`max_tokens`	Hit the max_tokens limit	Response may be truncated; consider increasing limit
`stop_sequence`	Hit a custom stop sequence	Parse response up to the stop sequence
`tool_use`	Claude wants to use a tool	Execute the tool and return results

Best Practices

Always check `stop_reason` -- it's the definitive loop control signal

Set a reasonable max iteration limit as a safety net (not the primary mechanism)

Log each iteration for debugging

Handle max_tokens stop reason (response may be truncated)

Key Takeaways

✓stop_reason is the ONLY reliable signal for agentic loop control
✓tool_use = continue the loop, end_turn = stop the loop
✓Always include a maximum iteration safety guardrail as a backup, not the primary mechanism
✓max_tokens indicates potential truncation -- handle gracefully

Glossary Terms

Agentic Loop

The core pattern for AI agents: call Claude, check stop_reason, if 'tool_use' execute the requested tools and append results, then call Claude again. Repeat until stop_reason is 'end_turn'. The number of loop iterations is determined dynamically by task complexity.

stop_reason

A field in the Claude API response indicating why the model stopped generating. Values: 'end_turn' (natural completion), 'max_tokens' (hit limit), 'stop_sequence' (hit custom stop), 'tool_use' (wants to call a tool). The primary signal for controlling agentic loops.

end_turn

A stop_reason value indicating Claude finished its response naturally without hitting a limit or requesting a tool. In agentic loops, this signals the loop should stop and the final response should be presented to the user.

Related Concepts

Tool Use Flow & Mechanics

Tool use follows a define -> select -> execute -> return cycle

Multi-Step Workflow Enforcement

Programmatic prerequisites are more reliable than prompt-based ordering

Test Yourself1 of 1

You're implementing the agentic loop for your support agent. After each API call to Claude, you need to determine whether to continue the loop (execute the requested tools and call Claude again) or stop (present the final response to the customer). What determines this decision?