Claude API Autonomous Agent Loop Design Guide

2026-10-22 · 5 min read · SubToAPI Team

Claude API Autonomous Agent Loop Design

An autonomous agent loop is the control structure that lets Claude keep working on a task without a human approving every step: it calls a tool, reads the result, decides what to do next, and repeats until the task is done or a stop condition fires. If you're searching for how to design one, the core answer is: build a while loop around the Messages API that appends tool results to the conversation, enforces a hard iteration cap, and treats every tool call as untrusted input that needs validation before execution.

This isn't a model problem — Claude is good at deciding when to stop and when to call a tool. It's an engineering problem: state management, cost control, error recovery, and knowing when to kill the loop before it burns your budget or does something destructive. Below is a practical design you can implement directly.

The basic loop shape

At minimum, an agent loop has four parts: a growing message history, a tool-calling step, a tool-execution step, and a termination check. In pseudocode:

let messages = [{ role: "user", content: task }];
let iterations = 0;
const MAX_ITERATIONS = 20;

while (iterations++ < MAX_ITERATIONS) {
  const response = await callClaude(messages, tools);
  messages.push({ role: "assistant", content: response.content });

  if (response.stop_reason === "end_turn") {
    return response; // task finished, no more tool calls
  }

  if (response.stop_reason === "tool_use") {
    const toolResults = await executeTools(response.content);
    messages.push({ role: "user", content: toolResults });
    continue;
  }

  // max_tokens, stop_sequence, or anything unexpected
  break;
}

The important detail is the stop_reason check. Claude tells you explicitly why it stopped generating — end_turn means it believes the task is complete, tool_use means it wants to call a tool, max_tokens means it ran out of room. Your loop logic should branch on this value rather than guessing from the text content. See /docs/tools for the exact shape of tool-use blocks and how to return results.

State and context management

The message history grows every iteration, and Claude's context window is finite. Two practical mitigations:

Summarize old turns. Once the history passes a token threshold, replace the oldest N tool-result pairs with a short summary message. Keep the original task and the last 2-3 turns verbatim.
Truncate tool outputs. A tool that returns a 50,000-row JSON dump will blow your context and your bill. Cap tool results at a sane size (a few KB) and let the tool itself paginate or filter.

If you're running this through SubToAPI, usage metadata comes back on every response (input/output tokens, cache stats), so you can log per-iteration cost and cut the loop off on a budget threshold rather than just an iteration count. That's often a better stop condition than a fixed number of turns, especially for tasks with unpredictable tool costs.

Termination conditions that actually matter

"Stop when Claude stops calling tools" is not enough for production. You need at least these layered conditions:

Hard iteration cap — always have one, even if you expect tasks to finish in 3-4 turns. 15-30 is a reasonable default.
Token/cost budget — sum tokens across the loop and abort if you exceed a threshold (e.g., 50k output tokens for a single task).
Wall-clock timeout — some tool calls hang. Wrap each tool execution in its own timeout, separate from the overall loop timeout.
Repetition detection — if Claude calls the same tool with the same arguments twice in a row, it's probably stuck in a loop. Hash the tool call and compare to the last one or two calls.
Explicit "done" signal — for complex agents, it helps to give Claude a mark_complete tool it must call to finish, rather than relying on end_turn alone. This makes completion deterministic and auditable.

function isRepeating(history, call) {
  const last = history.slice(-2);
  return last.some(h => h.tool === call.tool &&
    JSON.stringify(h.input) === JSON.stringify(call.input));
}

Tool execution: validate before you run

The agent loop's biggest practical risk isn't Claude being wrong — it's executing a tool call with bad arguments. Before running anything:

Validate the tool input against a JSON schema, not just "does it parse."
Whitelist which tools are callable per task type (a research agent shouldn't have access to a delete_file tool).
Return structured errors as tool results instead of throwing — Claude can often recover from "file not found" far better than your loop recovers from an unhandled exception.

async function executeTools(contentBlocks) {
  const results = [];
  for (const block of contentBlocks) {
    if (block.type !== "tool_use") continue;
    try {
      const output = await runTool(block.name, block.input);
      results.push({ type: "tool_result", tool_use_id: block.id, content: JSON.stringify(output) });
    } catch (err) {
      results.push({ type: "tool_result", tool_use_id: block.id, content: `Error: ${err.message}`, is_error: true });
    }
  }
  return results;
}

Running the loop against SubToAPI

If you're already using SubToAPI to turn your Claude access into an HTTPS API, the loop above drops in unchanged — you're calling the same Messages-shaped endpoint with your sub_live_ key:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4",
    "max_tokens": 1024,
    "tools": [...],
    "messages": messages
  }'

Because usage is tracked per API key on the dashboard, a long-running agent loop that calls the API dozens of times per task shows up clearly — you can see which workflows are expensive before they surprise you in a monthly bill. For teams running multiple agents with different budgets, separate sub_live_ keys per agent (/pricing has Team and Scale tiers with per-seat keys) make it easy to attribute cost without extra instrumentation.

Logging and observability

An agent loop that fails silently is worse than one that crashes loudly. Log, at minimum: the tool called, its input, its output (truncated), the stop reason, and cumulative token count — for every iteration. When something goes wrong three layers deep in a 20-turn loop, this log is the only way to reconstruct what happened. Store it against a task ID so you can replay or debug a specific run, and check /docs/messages for the exact response fields available to log.

questions

How many iterations should an autonomous Claude agent loop allow? There's no universal number — most task-specific agents converge in 3-10 turns. Set a hard cap around 2-3x your expected maximum (commonly 15-30) and treat hitting the cap as a failure state to investigate, not a normal exit.

How do I stop an agent loop from calling the same tool repeatedly? Compare each new tool call (name + arguments) against the last 1-2 calls in the history. If it matches, inject a message telling Claude the action already ran and ask it to try a different approach or conclude.

Can I build an agent loop without a dedicated agent framework? Yes — the loop shown above is the entire pattern. Frameworks add convenience (built-in memory, tool registries) but the core logic is just a while loop over the Messages API with tool-result feedback, which you can implement in under 100 lines.

Claude API Autonomous Agent Loop Design Guide