What is the Agentic Loop?

Traditional chatbots work in a simple pattern: you say something, they reply. One question, one answer, done. But Claude Code is different — it is an Agent. This means it does not just answer questions — it actively executes tasks.

The Agentic Loop is the core runtime mechanism of Claude Code. Every time you give Claude Code a task, it enters a continuous cycle:

This loop keeps running until the task is complete, or Claude determines it cannot proceed.

Think: The Reasoning Phase

At the start of each cycle, Claude analyzes the current context: what has been done, what the results were, and how far it is from the goal. This is not simple pattern matching — Claude reasons over all available information to formulate the next action plan.

Key decisions during the Think phase include:

• Tool selection: Should it read a file, search code, or execute a command?
• Parameter selection: Which file to read? What keyword to search?
• Path planning: Does it need more information before acting?
• Completion assessment: Is the task already done?

Act: The Execution Phase

After deciding what to do, Claude calls the appropriate tool. It calls one (or a batch of parallel) tools at a time — this is important because Claude needs to see the result of each step before deciding the next one.

Common actions include:

• Using Read to read source code files
• Using Grep to search for code patterns
• Using Edit to modify file contents
• Using Bash to run tests or execute commands
• Using Glob to find file paths

Observe: The Observation Phase

After a tool executes, it returns results — file contents, command output, search results, or error messages. Claude carefully reads these results, incorporates them into the context, and uses them as input for the next thinking cycle.

This is the power of the Agentic Loop: the result of every step influences the decision of the next step. Claude is not executing a predefined script — it dynamically adjusts its strategy based on actual outcomes.

A Real Example

Suppose you say: “Fix the failing test.” Claude’s Agentic Loop might run like this:

Round 1 - Think: Need to find out which test is failing
       Act:   Bash("npm test 2>&1 | tail -50")
       Observe: TestSuite > validateEmail > should reject invalid email
                Expected: false, Received: true

Round 2 - Think: validateEmail has a bug, need to read source
       Act:   Grep("validateEmail", "src/")
       Observe: Found src/utils/validator.ts:23

Round 3 - Think: Read the function implementation
       Act:   Read("src/utils/validator.ts")
       Observe: Regex is missing validation for ".."

Round 4 - Think: Fix the regex
       Act:   Edit("src/utils/validator.ts", ...)
       Observe: File updated

Round 5 - Think: Verify the fix works
       Act:   Bash("npm test")
       Observe: All tests passed ✓

Round 6 - Think: Task complete, return results
       → Output fix report to user

Throughout this process, Claude went through 6 cycles. Each cycle made decisions based on the observations from the previous one. If the test still failed in Round 5, Claude would continue looping, trying a different fix.

Error Recovery

A key advantage of the Agentic Loop is automatic error recovery. When a tool call fails — a file doesn’t exist, a command errors out, an edit conflicts — Claude does not stop and wait for human intervention. It will:

1. Read the error message: Understand why it failed
2. Adjust strategy: Try a different approach
3. Retry: Execute again with new parameters or a different tool

For example, if Claude tries to edit a file but finds the match string is not unique, it automatically expands the context range, providing more surrounding code to precisely locate the edit position.

Pseudocode

The core logic of the Agentic Loop can be expressed in pseudocode:

function agenticLoop(task: string, tools: Tool[]) {
let messages = [systemPrompt, userMessage(task)];

while (true) {
  // Think: Claude analyzes context, decides next step
  const response = await claude.generate(messages);

  // Check if done
  if (response.type === "final_answer") {
    return response.content;  // Task complete
  }

  // Act: Execute tool call
  if (response.type === "tool_call") {
    const result = await executeTool(
      response.toolName,
      response.toolArgs
    );

    // Observe: Add result to context
    messages.push(assistantMessage(response));
    messages.push(toolResultMessage(result));
  }

  // Safety limits
  if (messages.length > MAX_TURNS) {
    return "Maximum turn limit reached";
  }
}
}

How This Differs from Simple Q&A

Understanding the Agentic Loop requires contrasting it with traditional Q&A:

• Traditional chatbot: User input → Model output → End. The model cannot execute any operations — it can only generate text.
• Agentic Loop: User input → Think → Execute tool → Observe result → Think again → … → Return final result. The model can actively explore, experiment, and iterate.

This is why Claude Code can handle open-ended tasks like “fix this bug” — it does not need you to tell it what to do at every step. It finds the path on its own.

Termination Conditions

The Agentic Loop does not run forever. The following conditions trigger termination:

• Claude determines the task is complete
• Maximum conversation turn limit is reached
• Claude determines it cannot complete the task (e.g., insufficient permissions)
• User manually interrupts (Escape key)
• Context window is nearly exhausted (triggering context compaction)

The Agentic Loop is the key mechanism that evolves Claude Code from a “language model” into an “agent.” It is this loop that allows Claude to work like a real developer — facing a problem, analyzing, trying, verifying, and iterating until the problem is solved.