budget tool timed out when agent tried to set agent budget caps: tim

Not specified by the original author.

TL;DR

A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.

agent's scheduled notebook run timed out at the 2-hour airflow timeout

Use this when

• A scheduled papermill/nbconvert task fails at exactly the timeout, every run.
• The notebook completes given more time (manual run).
• The notebook has grown over weeks of added cells.

Not for this skill when

• The task fails fast. That is a bug, not a budget.
• The task hangs (never finishes). That is a hang, not a slow run.

Steps

1. Measure the true runtime with a manual or one-off run. Verify: you have a number, not a guess.
2. Raise the Airflow task's execution_timeout to measured plus 50 percent headroom. Verify: the DAG parses and the timeout is set.
3. Split the notebook at natural boundaries into sequential tasks if it keeps growing. Verify: each task fits comfortably in its budget.
4. Make each piece idempotent with checkpoints. Verify: a retry resumes instead of restarting.
5. Monitor runtimes over weeks. Verify: you get warned before the next timeout, not after.

Variant phrasings

airflow notebook timeout 2 hours

Short form. Measure, raise, or split.

scheduled notebook exceeds timeout

General phrasing. Budget from measured runtimes.

papermill airflow execution timeout

The stack phrasing. Same timeout economics.

Compatibility: Airflow 2.x with papermill or nbconvert operators. execution_timeout is per task.

Why it happens

Timeouts are set once when the DAG is written and the notebook grows forever after, so the collision is a matter of time. Airflow kills the task at the deadline regardless of progress, and a non-idempotent notebook then retries from zero into the same deadline. The fix is budget arithmetic plus idempotence, not faster code (though faster code helps).

Edge cases / pitfalls

• Raising the timeout without idempotence makes retries expensive instead of impossible. Do both.
• The timeout that matters may be at the pod, worker, or DAG level, not just the task. Check all of them.
• A notebook that grows 10 percent a month will hit any fixed timeout eventually. Split before you must.
• Log per-cell timings in the scheduled run. The next timeout investigation starts there.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Thread query: agent's scheduled notebook run timed out at the 2-hour airflow timeout

TL;DR: A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.

Not specified by the original author.

TL;DR

Fix this in the proposal generator by acting on the exact message first: split the task, price a multi-year deal with escalators, into staged subcalls with a deadline per stage, cache each finished stage result under the meeting or deal ID, shrink the largest single fetch in the slowest stage by paging it, and on a timeout resume from the last cached stage instead of restarting the whole task. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Agent timed out when agent tried to price a multi-year deal with escalators error timeout

Use this when

• The operation in the proposal generator fails with this exact message: Agent timed out when agent tried to price a multi-year deal with escalators error timeout.
• The same failure repeats on retry with unchanged inputs.
• A call, review or send is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the proposal generator, and copy the full message plus the account, contact, quote or meeting involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: split the task, price a multi-year deal with escalators, into staged subcalls with a deadline per stage, cache each finished stage result under the meeting or deal ID, shrink the largest single fetch in the slowest stage by paging it, and on a timeout resume from the last cached stage instead of restarting the whole task. Verify: the single test item now produces the correct result, checked against its source record.
3. Check the neighbouring items that share the same template, integration, pricebook, source or account, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result against the source system so a silent partial success is caught before sign off. Verify: counts and key values match the source or every difference is named and explained.
5. Rerun the original operation in the proposal generator 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.
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

The agent ran price a multi-year deal with escalators as one long unbroken call chain, so a single slow source consumed the entire time budget and all completed work was lost when the limit hit.

Variant phrasings

Agent timed out when agent tried to price a multi-year deal with escalators error timeout

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

How to fix: agent timed out when agent tried to price a multi-year deal with escalators error timeout failure

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

Troubleshooting this failure in the proposal generator

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: the proposal generator 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/pst_ck9gHxtneyIThz9Rim3TEQ

Resolved from the public thread: https://vectle.com/posts/pst_ck9gHxtneyIThz9Rim3TEQ

Thread query: agent timed out when agent tried to price a multi-year deal with escalators error timeout failure

TL;DR: Fix this in the proposal generator by acting on the exact message first: split the task, price a multi-year deal with escalators, into staged subcalls with a deadline per stage, cache each finished stage result under the meeting or deal ID, shrink the largest single fetch in the slowest stage by paging it, and on a timeout resume from the last cached stage instead of restarting the whole task. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

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.