Not long ago, most webhook receivers behaved similarly. An event arrived, a small piece of application logic validated it, updated a database, and returned a 200 OK within a few milliseconds. As the sender, you could safely assume that if the endpoint acknowledged the request, the work was either complete or well underway.

That assumption is becoming less reliable.

Imagine you're an API provider sending webhooks to thousands of customers. One customer has replaced their traditional webhook handler with an AI agent that reviews incoming events, gathers context from internal systems, reasons about the request, and only then decides what action to take. Depending on the task, that process might finish in a few seconds or take much longer.

Your webhook dispatcher doesn't know any of that.

After waiting for the usual timeout window, it marks the delivery as failed. Your retry policy kicks in and sends the event again. The agent is still processing the first request, so now it receives the same event twice. Hours later, your support team gets a ticket asking why duplicate actions happened and why your dashboard showed failed deliveries even though the customer's agent eventually completed the work.

Nothing is actually broken. Your delivery infrastructure simply assumed every receiver behaves like a fast, deterministic application when it no longer does.

AI agents change the assumptions behind webhooks

The rise of AI agents changes what lives behind a webhook endpoint.

Instead of immediately updating a record or triggering a workflow, the receiver may need to retrieve documents, query multiple systems, invoke language models, validate outputs, ask follow-up questions, or coordinate several tools before reaching a decision. The amount of work varies from request to request, so processing time becomes unpredictable.

We're already seeing this pattern elsewhere. Model providers such as OpenAI, Anthropic, and Google increasingly offer webhook-style callbacks for long-running asynchronous operations because polling becomes inefficient once completion time is unpredictable. Likewise, the Model Context Protocol (MCP) was designed around synchronous request and response interactions. It does not provide a built-in way to say, "Notify me when this long-running task finishes," which naturally pushes more agent workflows toward webhook-based callbacks.

The webhook itself has not changed. What has changed is what happens after the request arrives.

This becomes the sender's problem

It is tempting to think this is something the customer's engineering team should solve.

After all, they built the AI agent.

But the operational impact lands squarely on the sender.

Your delivery system decides when a webhook has failed. Your retry logic decides when another copy gets sent. Your delivery logs are what your support engineers rely on when customers report missing or duplicate events.

If your infrastructure assumes every receiver responds within five or ten seconds, then slow reasoning gets interpreted as failure. From your perspective, a healthy customer suddenly appears unavailable.

That creates three problems: legitimate deliveries get marked as failures simply because the receiver needed more time; retries fire while the original request is still in flight, raising the chance of duplicate downstream actions; and your team can no longer tell a genuinely unavailable endpoint from one that is simply slow. When a customer asks why an event failed, a timeout in the logs is not enough of an answer.

None of these problems are solved by making the AI agent smarter.

They are solved by making outbound webhook delivery smarter.

Outbound webhook infrastructure needs to adapt

As more webhook traffic lands on AI-powered receivers, senders need to rethink several long-standing assumptions.

The first is how acknowledgements and timeouts are handled. A timeout should not automatically be treated as proof that the receiver is unhealthy. Sometimes it simply means the receiver is performing work that naturally takes longer than traditional request processing.

The second is retry strategy. Fixed retry intervals work reasonably well when failures are caused by temporary network issues. They work much less well when the receiver is already busy processing the original request. Intelligent backoff reduces unnecessary pressure on slow receivers instead of repeatedly sending identical events while meaningful work is already happening.

Idempotency also becomes more important. Retries will always exist because networks fail, services restart, and outages happen. What changes is that retries become more likely to overlap with legitimate in-flight processing. Giving receivers stable event identifiers or idempotency keys allows them to recognize duplicate deliveries and avoid repeating expensive or irreversible actions.

Finally, delivery observability becomes essential. Platform teams need visibility into every delivery attempt, every retry, response timing, and delivery outcome. When a customer opens a support ticket, the question is no longer simply, "Did the webhook fail?" The more useful question is, "Was the receiver unavailable, or was it still processing when our timeout expired?" Those are very different operational situations, and your tooling should make that distinction obvious.

The webhook gateway becomes part of your delivery strategy

The growing use of AI agents does not mean webhooks are becoming obsolete. If anything, they are becoming more important because they fit naturally with asynchronous work.

What is changing is the responsibility of the sender.

Sending a POST request is no longer enough. Reliable delivery now means accounting for receivers that reason instead of reacting immediately. It means retrying thoughtfully instead of aggressively, supporting idempotent processing, and giving your own engineers the visibility they need to understand what is happening when deliveries take longer than expected.

That is exactly where a webhook gateway belongs. Rather than treating delivery as a simple HTTP request, it becomes the layer that manages retries, delivery policies, observability, and operational reliability across every customer endpoint, whether the receiver is a traditional application or an AI agent.

If you're building outbound webhook infrastructure for API customers, Convoy provides that gateway layer, available as both a cloud service and a self-hosted deployment.