Input: API payload, callback URL, and signing rule
Output: an async job plus webhook events
Aspect: per channel
Build log
1. Set the rule
I opened the a spreadsheet or API payload of task rows, locked the API payload and task ID mapping, and wrote down what could not change.
2. Run one minimum version
I made one small an async job plus webhook events first. The callback arrived twice, so I added an idempotency key and logged the payload ID.
3. Build the focused variant
I reused the same reference and changed only one webhook signature and one retry path.
4. Review and export
I checked payload, task ID, callback, retries, and logs at full size, then exported the final varies by channel.
Complete execution record
Task boundary and source gate
Treat AI video webhook pipeline as a deliverable, not a definition. The job receives API payload, callback URL, and signing rule and owes an async job plus webhook events in per channel. Write the rejection reason first, then turn it into the first hard constraint.
Require the schema, idempotency key, retry policy, timeout, storage path, failure webhook, cost ceiling, and the row that owns each asset. Do not send customer-owned media to a new route without a privacy check, and do not start a batch where task IDs cannot be stored.
The imgmov route
Validate one payload before any batch: model ID, reference URL, aspect ratio, duration, callback URL, signing rule, and task-ID mapping. Run a five-row batch before a catalog-wide job.
The documented imgmov API surface is one generate endpoint, one task endpoint, one SSE stream, and one webhook registration endpoint. Build batches as a controlled client loop over that contract; do not invent a batch parameter. Reserve a monthly budget before the loop, store each returned task ID with the source row, and reconcile estimated and actual credits after the response.
Lock the variables that cannot drift
Lock request shape, prompt template version, model route, ratio, duration, callback contract, storage key, and retry count. A retry must never create a second paid asset unnoticed.
Put the locked fields at the top of the prompt, not at the end: The practical move is to lock the API payload and task ID mapping and change only one webhook signature and one retry path. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Set the rule: I opened the a spreadsheet or API payload of task rows, locked the API payload and task ID mapping, and wrote down what could not change. Leave one checkable artifact from this step; do not start the next until it exists. 2) Run one minimum version: I made one small an async job plus webhook events first. The callback arrived twice, so I added an idempotency key and logged the payload ID. Leave one checkable artifact from this step; do not start the next until it exists. 3) Build the focused variant: I reused the same reference and changed only one webhook signature and one retry path. Leave one checkable artifact from this step; do not start the next until it exists. 4) Review and export: I checked payload, task ID, callback, retries, and logs at full size, then exported the final varies by channel. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
The log must connect task ID, request hash, model, credits, asset URL, callback receipt, and final QA state.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
Drop duplicate callbacks by idempotency key. Throttle queue pressure instead of disabling retries. Quarantine bad rows with their payload.
Cost control is part of the code path. Agnes Video 2.5 Flash is 2 credits/second, so a 5-second probe is 10 credits. Seedance 2.5 is 18 credits/second at 480p and 39 at 720p. Store the task ID before increasing concurrency; an untagged retry is the fastest way to pay twice for one row.
Versioning and handoff
Release notes record model version, prompt template, sample outputs, cost per 1,000 tasks, rollback prompt, and the approver.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
This is where imgmov differs from a prompt playground. The asset library keeps the reference, Canvas keeps the dependency, generation history keeps the task, and the API path keeps the task ID and callback. A generic chat box can write a sentence; it will not reconcile a retry, a failed row, and the exact asset that shipped.
Do not send customer-owned media to a new route without a privacy check, and do not start a batch where task IDs cannot be stored. If the request only asks what ai video webhook pipeline means, a search page is faster. This page is useful when someone must deliver an async job plus webhook events under real constraints. If there is no place to store task IDs, do not start a batch.
Frequently asked questions
What is the one rule I keep repeating?
The practical move is to lock the API payload and task ID mapping and change only one webhook signature and one retry path.
The workflow is specific about its stopping point: it starts with API payload, callback URL, and signing rule and stops at an async job plus webhook events. In imgmov, upload the source, lock it as a reference, set per channel, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
What should I check before release?
Payload, task ID, callback, retries, and logs.
Cost control is part of the code path. Agnes Video 2.5 Flash is 2 credits/second, so a 5-second probe is 10 credits. Seedance 2.5 is 18 credits/second at 480p and 39 at 720p. Store the task ID before increasing concurrency; an untagged retry is the fastest way to pay twice for one row.
What makes this different from a generic generator?
I start from API payload, callback URL, and signing rule and keep an async job plus webhook events consistent, instead of inventing a new scene each run.
The check is not “does it look AI-nice?” It is: The log must connect task ID, request hash, model, credits, asset URL, callback receipt, and final QA state. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
What should I do if the first render drifts?
Cut the motion, lock the reference, and rerun one small version before batching.
Drop duplicate callbacks by idempotency key. Throttle queue pressure instead of disabling retries. Quarantine bad rows with their payload. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.