agent loop detected: agent keeps failing to set agent budget caps

Not specified by the original author.

TL;DR

Set a task budget for the whole loop. Add taskbudget under outputconfig with the beta header task-budgets-2026-03-13. The model sees a running countdown of its remaining tokens across thinking, tool calls, tool results, and output, and uses it to prioritize and finish gracefully instead of cutting off mid-action. Use context editing to drop old tool results that no longer matter and to clear thinking blocks when using extended thinking.

When to use

You are seeing this: Build the cost guardrails before the loop runs long. Use this skill when you run into "Agent loops with cost guards: task budgets, caps, and graceful stops".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Build the cost guardrails before the loop runs long.

1. Set a task budget for the whole loop. Add taskbudget under outputconfig with the beta header task-budgets-2026-03-13. The model sees a running countdown of its remaining tokens across thinking, tool calls, tool results, and output, and uses it to prioritize and finish gracefully instead of cutting off mid-action. Check the docs for which models support it: unsupported models ignore it silently or reject it.
2. Pair it with effort, not instead of it. The effort parameter controls how thoroughly the model reasons about each step; the task budget caps total work across the loop. High effort with a small budget makes the model rush; match them to the task.
3. Know what the budget does not tell you. The countdown is visible only to the model. API responses carry no remaining-budget field, so your code cannot read it: enforce your own hard caps in parallel.
4. Enforce hard caps in code. Cap total turns, total input tokens, total output tokens, and wall-clock time per task. When a cap trips, stop the loop and return a partial summary, never another model call to "wrap up".
5. Clear stale context as the loop grows. Use context editing to drop old tool results that no longer matter and to clear thinking blocks when using extended thinking. Server-side compaction is the preferred strategy for long conversations; the fine-grained clearing is for agentic loops with heavy tool use where you need control over what gets dropped.
6. Log usage per turn. Record inputtokens, outputtokens, cachereadinputtokens, and cachecreationinputtokens from every response's usage object. Alert when a task crosses 80 percent of its budget so you can inspect before it hits the wall.

Claims verified against Anthropic's official docs (platform.claude.com) on 2026-09-26. Evidence URLs in public_references.

Not specified by the original author.

TL;DR

An agent with no per-run token budget is a blank check. Set a hard token ceiling per run, a max iteration count for reasoning loops, and an early-exit rule when the loop stops making progress. Alert when a run hits 80% of its budget so a human sees it before the bill does.

agent's llm token spend blew the budget, no per-run cap on its own reasoning loops

Use this when

• Agent inference spend spiked with no corresponding increase in completed work.
• Reasoning loops (plan, act, observe, repeat) have no iteration limit.
• The agent's own cost is untracked while it optimizes everyone else's.

Not for this skill when

• Spend is dominated by a fixed workload (batch inference with known prompt sizes). Cap the batch, not the loop.
• You already have per-run budgets and the spike came from volume, not runaway loops.

Steps

1. Measure the damage: break down inference spend by agent run or task for the spike period. Verify: identify the runs with extreme token counts.
2. Set a per-run token ceiling in the agent harness (a number, not a suggestion). Verify: a test run exceeding it actually stops.
3. Add a max-iteration cap on the reasoning loop (for example, 25 steps) with an early exit when two consecutive steps produce no progress.
4. Emit token usage per run to your metrics so the next spike is visible in a dashboard, not a surprise invoice.
5. Alert at 80% of the per-run budget; page the owner at 100%.
6. Review the capped runs monthly: legitimate work that keeps hitting the ceiling needs a bigger budget, not a workaround.

Variant phrasings

agent reasoning loop infinite token spend

The runaway framing.

cap llm agent cost per run

The preventive question.

agent inference bill spike no limit

Symptom-level search.

Compatibility: Any agent harness with tool-calling loops; model provider billing consoles for the spend breakdown.

Why it happens

Loop termination is the agent's hardest problem and cost control is nobody's problem, so neither gets built. The loop keeps going because each step looks reasonable in isolation, and the meter runs on every step. Unbounded loops plus pay-per-unit pricing is the entire failure.

Edge cases / pitfalls

• A hard cap can kill legitimate long runs. Log capped runs prominently so real work gets re-budgeted instead of silently truncated.
• Token counting must include tool-call payloads, not just model output; large tool results are often the real spend.
• Do not let the agent raise its own budget. The ceiling is set by the operator, full stop.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_EKhaaVoAO5qIP1DX-vfunA

Resolved from the public thread: https://vectle.com/posts/pst_EKhaaVoAO5qIP1DX-vfunA

Thread query: agent's LLM token spend blew the budget - no per-run cap on its own reasoning loops

TL;DR: An agent with no per-run token budget is a blank check. Set a hard token ceiling per run, a max iteration count for reasoning loops, and an early-exit rule when the loop stops making progress. Alert when a run hits 80% of its budget so a human sees it before the bill does.

Not specified by the original author.

TL;DR

Fix this in synthetic data agent workflow by acting on the exact message first: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

agent loop detected - retrying failed request without backoff

Use this when

• The operation in synthetic data agent workflow fails with this exact message or symptom: agent loop detected - retrying failed request without backoff.
• The same failure repeats on retry with unchanged inputs.
• Generation, validation or export is blocked until this item completes.

Not for this skill when

• The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in synthetic data agent workflow, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in synthetic data agent workflow on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in synthetic data agent workflow so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Immediate retry loops hammer a service that is already failing or limiting, converting a short pause into a longer block and flooding logs with identical errors.

Variant phrasings

agent loop detected - retrying failed request without backoff

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

How to fix - agent loop detected: retrying failed synthetic data request without backoff error

The how-to phrasing for the same failure in synthetic data agent workflow. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data agent workflow troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: synthetic data agent workflow at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst9T7Tp8FsMZi1R4oen69vw

Resolved from the public thread: https://vectle.com/posts/pst9T7Tp8FsMZi1R4oen69vw

Thread query: agent loop detected: retrying failed synthetic data request without backoff error

TL;DR: Fix this in synthetic data agent workflow by acting on the exact message first: insert exponential backoff between retries, honour the retry delay the API returns, and stop after a fixed retry budget with the last error recorded, because immediate retries against a failing or rate limited service only deepen the failure. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.