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These five conditions are an engineering synthesis, not an official Anthropic framework. Anthropic’s response stop reasons—including end_turn, tool_use, max_tokens, and pause_turn—provide useful control-flow signals, but the application still has to decide what to do with them. Confirm the reason strings supported by the model and API version you deploy.
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1. Stop when the assistant has finished
When a response ends with end_turn, treat it as a completed assistant turn rather than a request to keep calling tools. By contrast, tool_use means the assistant is handing control to a tool; it does not mean the user’s request is complete. Anthropic describes these response states in its stop-reason guidance.
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2. Stop or retry deliberately when a tool fails
For each client-side tool call, correlate the result to the originating request with its tool_use_id. Return the result after the assistant’s tool-use message, mark it with the tool_result type, and use is_error when the operation failed. This keeps failures distinguishable from successful tool output and lets the assistant interpret the right result. See Anthropic’s tool-use implementation documentation.
Whether to retry is an application policy, not a universal Anthropic retry rule. Classify errors by whether repeating the operation could plausibly succeed and whether repetition is safe. Set a retry cap, and stop on terminal failures; do not let the model repeatedly request the same operation without a bounded recovery path.
3. Stop when the run reaches a budget
Bound unattended work with a maximum number of model/tool turns. Add time, token, or monetary limits where those are relevant to your service objectives. These controls address different costs: a turn cap limits iteration, a deadline limits elapsed time, and token or monetary budgets constrain consumption.
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Claude Code CLI
Claude Code’s CLI reference documents --max-turns for limiting agentic turns in print mode; the command exits with an error when it reaches the limit. The reference lists no default limit, so set one explicitly when the bound matters. This is a Claude Code CLI feature, not a setting automatically applied to every Messages API integration. Consult the Claude Code CLI reference.
Messages API integrations
A Messages API loop needs its own equivalent bound in the calling application. Count turns consistently, stop before starting work that would exceed the limit, and surface the ceiling as a constrained or incomplete run rather than presenting it as normal completion. The appropriate limit depends on the actions, latency tolerance, and cost model of your application; Anthropic’s cited documentation does not prescribe a universal value.
4. Stop before an unauthorized action
Model intent is not authorization. Check permissions in the application before executing a requested tool operation, especially when it can change data, spend money, contact other people, or affect external systems. A valid-looking tool call should still be rejected or held if the caller lacks permission.
Claude Code documents permission modes and unattended behavior, including cases where prompts may be denied when nobody is available to answer. In an unattended run, define what happens when approval is unavailable: fail closed, report that approval is needed, and return control to an authorized person or system rather than silently proceeding. See the Claude Code permissions documentation.
5. Treat truncation and pauses as distinct states
max_tokens: incomplete output
A max_tokens stop reason indicates that generation reached its token limit; do not treat the resulting text as a successful, complete answer. Decide whether to request a continuation or return an incomplete result, based on the task and remaining budget. If continuing, make sure the partial output is not mistaken for a complete instruction or a finished tool result.
pause_turn: managed continuation
A server-tool response may use pause_turn. Handle it through the documented continuation flow only if the operation remains authorized and the run remains within its limits. A pause is not the same as a final answer, but it is also not permission to continue indefinitely. Check the current stop-reason guidance for the supported flow and confirm exact reason values for your deployed model and API version.
Put the stop conditions into the control loop
A robust loop makes one decision at a time: interpret the response state, validate any requested action, execute only what policy allows, and return a correlated result. A simplified outline is:
- Inspect the response’s stop reason. If it is
end_turn, return the completed response. If it is an incomplete or pause state, use the specific handling path rather than treating it as success. - If the response requests
tool_use, check the turn and resource budgets before proceeding. Validate the tool name and input, then enforce authorization in the application. - Execute the permitted operation. Correlate the result to the original
tool_use_id; mark failures explicitly withis_errorwhere applicable. - Classify failures under your retry policy. Retry only eligible failures, within the cap; otherwise stop and report the terminal outcome.
- Send the tool result back to Claude and inspect the next response from the beginning of the loop. Stop if a budget is reached or further continuation is not authorized.
Keep tool output bounded, too
Tool output is another input boundary: oversized results can consume context and make later decisions harder. Claude Code’s MCP documentation reports a warning above 10,000 tokens and a default maximum of 25,000 tokens for applicable tool output. For oversized successful text results, Claude Code can move the content to a file; long error results are truncated to retained leading and trailing text. These are Claude Code-specific documented behaviors, not universal limits for Claude API clients. Check the current MCP documentation for the behavior applicable to your version.
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Choose continuation rules to fit the risk
| Condition or signal | Default control-loop action | When continuation can be appropriate |
|---|---|---|
end_turn |
Stop and return the final response. | Not applicable; the assistant has finished. |
tool_use |
Validate the request, budget, and authorization before running it. | When the operation is valid and permitted; return a result linked to its request ID. |
| Tool error | Mark the result as an error and classify it. | Only for failures your policy deems retryable, within a defined cap. |
| Turn or resource ceiling | Stop and report that the run reached its limit. | Only after an explicit policy decision to start a new or extended run. |
| Authorization unavailable or denied | Do not execute; return control for approval or report the block. | After an authorized person or system grants permission. |
max_tokens or pause_turn |
Handle as incomplete output or a managed pause, not ordinary success. | Only through the supported continuation flow and within policy and budget. |
The key design choice is how much automatic recovery to allow. More retries and continuations may improve completion in some cases, but they also add latency and cost; for consequential actions, returning control for approval is often safer than automatic continuation. Set retry caps, budgets, and approval thresholds for the actual operation and service objectives—there is no universal value established by the cited documentation.
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