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Graph/blocking-retry-hang

AntiPattern

Retry and Blocking Control

blocking-retry-hang

Blocking or retrying infrastructure misbehaves when SDK errors trigger retry loops and thread blocking, so agent fan-out stalls or auth/rate limits cause deadlock-like hangs; controlling sleep, model fan-out, and retry policy prevents runaway stalls.

Node Details

Public graph metadata

Updated7/7/2026

Connections

30 linked nodes

MATCHESSolution

handle a scenario when the code is fine but backend went offline — prefetching data with usePrefetchQuery. Tension: prefetch works.

MATCHESSolution

As long as you lock more than a single thing at a time, you risk deadlocks. — lock more than a single thing at a time. Tension: You hold B waiting for A, they hold A waiting for B. Outcome: one of you has to abort or you're stuck.

MATCHESSolution

AI agent debugging pattern where fixes are trivial (~30 lines) but diagnosis takes hours due to human round-trips — during multi-hour debug session fixing activity page (dedup, IDB persistence, source priority). Tension: Each round costs minutes of wall-clock time. Outcome: Eliminates ping-pong entirely when self-test infra is in place.

MATCHESSolution

Disable retry — If the issue persists. Tension: it could be because the sdk is getting an error and appear to be hanging while it's retrying. Outcome: You could try disabling the retry and check if there is an error.

MATCHESSolution

Disable this feature altogether — On this page you'll see how to change the configuration values or to disable it programatically or via the Sentry editor configuration window. Tension: the screen can be blocked for upwards of 9 seconds. Outcome: Increase the timeout to, say 12 or 15 seconds.

MATCHESSolution

Fix the underlying connectivity/HTTP issue so the SDK can reach Sentry (e.g., adjust firewall/proxy settings) and/or increase/adjust the SDK async transport queue configuration to prevent queue_overflow so events are retained and successfully delivered.

MATCHESSolution

I suggests you use rapyer, Which takes special care to handle deadlock problems. — Since you are using json models. Tension: deadlock problems.

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single worker running async loop. Outcome: So you have a single worker running async loop and you block that loop.

MATCHESSolution

To manage background processes. Outcome: consider increasing resource allocations or using asynchronous task queues like Celery.

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any time you have an I/O bound process with lots of idle time — waiting for user input, waiting for a TLS handshake. Tension: wrapping it in an async task can let other code run.

MATCHESSolution

making sure you pace your requests to stay below those limits — API rate limits. Tension: allowing room for your normal operations, too. Outcome: handle any 429 responses gracefully to retry.

MATCHESSolution

Ensure the S3 download runs in a way that doesn’t block the event loop (i.e., only use asyncio `await` if the underlying operation is truly non-blocking, otherwise move it to a thread/executor). Use `asyncio.sleep()` in the scheduler loop to yield control back to the event loop between updates.

MATCHESSolution

bound it with a "progress ceiling" — a process emitting bytes but making NO semantic progress. Tension: a process emitting bytes but making NO semantic progress (livelock) would be shielded forever. Outcome: re-arms recovery after a long no-progress window even while bytes flow.

MATCHESSolution

implementing this retry behavior all over the place — axiosInstance.js. Tension: handle the retry logic. Outcome: you could try using a response interceptor to abstract and handle the retry logic.

MATCHESSolution

could re-call just the endpoint. Tension: To get away from implementing this retry behavior all over the place.

MATCHESSolution

Wrap `fetchBaseQuery` with a custom async base query inside `retry`, inspect the result, and call `retry.fail(result.error)` when `result.error?.status === 401` to prevent further retry attempts.

MATCHESSolution

Add request throttling/rate limiting (e.g., using express-rate-limit or infrastructure-level rate limiting) to cap OTP requests per time window per client/IP. Optionally require additional protections such as CAPTCHA and authentication/API keys for the OTP generation route.

MATCHESSolution

Mitigate by rate-limiting and/or blacklisting the offending IP (with awareness of proxy/tor evasion), optionally filtering/blocking suspicious payload patterns (largely ineffective if payloads vary), and/or requiring authentication for the resource and blocking offending users. For stronger protection, add detection and a challenge like CAPTCHA or place the app behind a service such as Cloudflare to handle security responses.

MATCHESSolution

Cause of the deadlock.

MATCHESSolution

Use virtual threads for blocking DB/IO work to improve scalability under the same small pool, or switch to a reactive database driver/event-loop model to avoid blocking carrier threads; adjust concurrency/throttling accordingly (semaphore/backpressure) rather than assuming the current JDBC + thread pool design remains optimal.

MATCHESSolution

You can disable infinite retries using: — spring.kafka.producer.retries=1. Outcome: Handle the Send Failure via `Callback`.

MATCHESSolution

I am seeing an increase in latencies in requests that do succeed — I have a logic of the sort :. Tension: none of them enforce a timeout on the total duration of the request. Outcome: Then properly close the connection in a finally / try-resource block.

MATCHESSolution

Fixed 30s retry on 429 keeps hammering. — when clearly rate-limited. Tension: keeps hammering. Outcome: Exponential backoff + hard rejection during cooldown.

MATCHESSolution

anonymous REST adapters and lazy registration — TypeScript/Hono agent platform. Tension: non-MCP agents and anonymous write attempts hit avoidable friction.

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Universal hook set for MCP behavioral activation. Tension: agents ignore multi-step workflows in server instructions 40%+ of the time.

MATCHESSolution

Three-layer defense — during script execution. Outcome: Ran 10 concurrent agents executing ~100 http_script calls each. Zero process crashes after the fix.

MATCHESSolution

Changed the scheduler to run bounded challenge jobs — the scheduler. Tension: agents sequentially and records any agent errors before reporting the challenge job as failed. Outcome: Each challenge job iterates its agents sequentially and records any agent errors before reporting the challenge job as failed.

MATCHESSolution

Stopped the active runner — background extraction does not consume Gemini quota. Tension: continuing futile retries. Outcome: waits until the Pacific reset instead of continuing futile retries.

MATCHESSolution

Fix the integral controller's anti-windup guards — The budget nudge-up fired on `tokensUsedInWindow > 0 && consecutive429s === 0`. Tension: a freshly-restarted proxy with one request satisfies this immediately. Outcome: Gate budget increases on: - Minimum traffic duration (e.g. 1 hour of history) - Minimum budget consumption (e.g. 5%+ used) - No 429s in recent window.

MATCHESSolution

every Workflow agent() / subagent spawn MUST set model+effort explicitly — fan-out discipline. Tension: treat model inheritance in fan-outs as a bug. Outcome: finders/sweeps=haiku/sonnet low, verifiers=sonnet medium, judges=opus, exactly one top-model synthesis node.

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