Claude Agentic Loop: How It Reasons, Acts, and Self-Corrects
The previous post introduced the core agent pattern: a loop where Claude calls tools, receives results, and continues until the task is done. But that description understates the sophistication of what is actually happening inside that loop. Claude is not simply executing a predetermined script. It is continuously reasoning about the situation, evaluating whether its approach is working, and revising its plan based on what it observes.
Understanding this deeper layer of the agentic loop is what separates agents that work in demos from agents that are reliable in production. This post covers the mechanics of Claude's reasoning cycle, how error recovery works, when and how to use sub-agents, and the critical orchestration patterns that make agents dependable under real-world conditions.
What is the Claude Agentic Loop?
The Claude agentic loop is a continuous reasoning cycle where the model reviews its full conversation history, decides on the next action, executes a tool call, and evaluates the result — repeating until the task is complete. On every iteration Claude re-evaluates its plan, which means it can detect errors, refine search queries, and adapt to unexpected results without any special error-handling code from you.
What Happens Inside Each Iteration
On each iteration of the agent loop, Claude receives the full conversation history — every message, every tool call, and every tool result from the beginning of the session. Claude processes all of this context together to decide what to do next.
This full context access enables something important: Claude can notice when something is not going right. If a tool returned an error, Claude can try a different approach. If its first search returned irrelevant results, it can refine the query. If it discovers the task is more complex than expected, it can add more steps.
This is what makes Claude an agent rather than a simple automation script — it adapts.
The Five Phases of an Agentic Iteration
Each loop iteration follows this internal structure:
- Context review: Claude reads the full history — goal, previous actions, and their results
- Goal assessment: Is the task complete? If yes, generate the final response. If no, continue.
- Plan update: Given what has happened so far, what is the best next action?
- Tool selection: Select the appropriate tool(s) and construct careful arguments
- Execution: Return the tool_use block(s) to your code for execution
The key insight is step 3 — Claude is not following a fixed plan. It is reassessing its plan on every iteration based on accumulated evidence.
How Claude Self-Corrects
Self-correction in agents happens naturally through the context window. Because Claude sees everything that has happened, it can detect and respond to problems:
You do not write error recovery logic for Claude to follow — Claude observes the error and decides how to respond. Your job is to return errors clearly in tool results rather than silently swallowing them.
Return Errors as Tool Results, Not Exceptions
When a tool call fails — network error, invalid API response, timeout — do not raise an exception that crashes the loop. Instead, return a clear error description as the tool_result content. Claude will see the error, understand it, and decide whether to retry, try a different approach, or inform the user it cannot complete the task. Crashing the loop loses all context from previous successful steps.
Handling Long-Running Agents
For complex tasks that may take many steps, your agent loop needs robust state management:
Orchestrator and Sub-Agent Patterns
For complex tasks, a single agent with many tools becomes hard to manage and can lose focus. A better pattern is an orchestrator agent that delegates specific sub-tasks to specialised sub-agents.
The orchestrator breaks the goal into sub-tasks. Each sub-agent has a narrow set of tools and a clear scope. Results flow back to the orchestrator which synthesises them.
Keep Sub-Agent Scope Narrow and Well-Defined
The most common mistake with multi-agent systems is giving sub-agents too broad a scope. A sub-agent works best when it has 2-4 tools, a single clear objective, and a defined output format. Narrow scope makes sub-agents more reliable, easier to test in isolation, and simpler to debug when something goes wrong.
Prompt Patterns for Reliable Agents
The quality of your system prompt directly determines how reliable your agent is. These patterns consistently improve agent behaviour:
Goal-Process-Output Structure
Explicit Stopping Conditions
Always tell the agent explicitly when to stop:
Managing Context Window in Long Agents
For long-running agents with many tool calls, the context window can fill up. Watch for this and implement compression when needed:
- Summarise tool results: If a search result returns a 5000-word article, extract and store only the relevant 200-word snippet before adding it to the conversation
- Compress intermediate history: For very long agents, periodically summarise the tool call history into a condensed context block
- Use structured state: Maintain a state dictionary outside the conversation and pass only the current state to each iteration, rather than the full raw tool call history
Monitor Context Window Usage
Claude models have context window limits — 200,000 tokens for Opus and Sonnet 4.6. Long agents that make many tool calls with verbose results can approach this limit. Monitor the total token count of your messages array across iterations. When it approaches 80% of the limit, implement compression or start a summary-based handoff to a clean conversation context.
Summary
The agentic loop is more than a while loop with tool calls. It is Claude continuously reasoning about its situation, updating its plan, and adapting to unexpected results. Building reliable agents requires:
- Returning errors as tool results — never silently swallowing failures
- Setting maximum iteration limits with meaningful fallback behaviour
- Using orchestrator/sub-agent patterns for complex tasks with distinct phases
- Writing clear system prompts with explicit goals, process steps, and stopping conditions
- Monitoring and managing context window consumption for long-running tasks
Next: how to connect Claude agents to any tool ecosystem through an open standard — Model Context Protocol (MCP): Connect Claude to Any Tool.
Real-World Agentic Loop Example: Bug Fixer Agent
To see these patterns in action, consider an autonomous bug-fixing agent. The orchestrator receives a failing test report, delegates to a code-reading sub-agent to identify the relevant file, then to a code-editing sub-agent to apply the fix, and finally runs the test suite to verify the outcome. Each sub-agent has 2–3 tools and a single clear objective, which keeps the overall system debuggable and reliable.
Our build autonomous bug fixer agent tutorial walks through the complete implementation. If you want to understand how tool definitions work before building agents, the Claude tool use explained guide covers the tool schema format in detail.
For a broader perspective on how Claude compares to other AI systems for agentic tasks, see AI coding agents compared 2026.
The Anthropic agentic loop documentation provides the official reference for how the loop works, including guidance on human-in-the-loop checkpoints. Anthropic's Building Effective Agents research post is essential reading for understanding the design decisions behind multi-agent architectures.
This post is part of the Anthropic AI Tutorial Series. Previous post: What is an AI Agent? Building Your First Agent with Claude.
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