AI agents are inherently unreliable. They call external APIs, run long inference chains, and depend on models that can timeout, return unexpected outputs, or simply be unavailable. Building an agent workflow that handles all of this gracefully requires more than just try/catch blocks.
Synchronous calls are the root of most failures
When an agent step calls an API synchronously, it ties up a server thread for the duration of that call. If the call takes 30 seconds — common for complex inference — you've tied up a thread for 30 seconds. Under load, this compounds quickly: threads are exhausted, new requests queue up, and the whole system grinds to a halt.
The alternative is to make each step a job. The agent enqueues the work, releases the thread, and the result arrives via callback when ready.
Callbacks as the coordination layer
HTTP callbacks are the natural glue between job steps. When step A completes, the queue calls your endpoint with the result. Your endpoint processes it, potentially enqueues step B, and returns 200. This creates a chain of steps without any single thread holding the entire chain.
- Idempotency — callbacks can be retried; processing the same result twice should be safe
- Fast response — return 200 quickly; do heavy processing asynchronously
- Signature verification — verify the callback came from your queue before acting on it
HMAC verification in practice
TaskFlow Queue signs every callback with HMAC-SHA256 using your API key as the secret. The signature arrives in the X-TaskFlow-Signature header. Verifying it is a few lines:
const sig = request.headers.get("X-TaskFlow-Signature");
const expected = createHmac("sha256", apiKey)
.update(await request.text())
.digest("hex");
if (sig !== expected) return new Response("Forbidden", { status: 403 });Dead-letter queues catch what retries miss
Retries handle transient failures. But some failures aren't transient — a malformed payload, a bug in your callback handler, or an AI model returning something unexpected can cause repeated failures. After a configured number of retries, TaskFlow moves the job to a dead-letter queue instead of discarding it.
You can inspect dead-letter jobs, fix the underlying issue, and re-enqueue them with a single API call. No data is lost.
Observability matters more than you think
In production, you need to know: did this job run? When? What did the callback respond with? Webhook delivery logs give you a record of every callback attempt — status code, response body, timestamp.