AI agent incident response: contain, investigate, and recover at the API gateway

An AI agent that has been quietly summarising order data for three months starts issuing POST requests against a payments endpoint at 2:14 on a Tuesday morning. Maybe a prompt injection redirected it. Maybe its credential leaked into a public repo. Maybe a workflow change upstream simply confused it. The cause matters later. What matters right now is the question every AI agent incident response plan has to answer in the first five minutes: can you cut this one agent off immediately, without breaking anything else, and can you then reconstruct exactly what it did?

Most teams running AI agents against production APIs today cannot do either. They can kill the agent process, which does nothing about a stolen credential. They can rotate a shared API key, which breaks every other consumer using it. And when the security team asks what the agent actually called, the honest answer is a grep through backend logs that never recorded which caller was the agent in the first place.

This post is a practical runbook for AI agent incident response built on the gateway: how to contain a misbehaving agent in seconds, investigate its full request history, rotate its credentials, and restore access with a tighter scope. Every step uses real Zerq screens, fields, and log structures.

Why the usual responses fail

The instinctive response to a misbehaving agent is to stop the agent. That works only if the agent's process is the problem. If the credential is the problem, because it leaked or was extracted through a prompt injection, stopping your copy of the agent does nothing: whoever holds the token keeps calling your APIs, and from the backend's point of view nothing has changed.

The second instinct is to revoke the credential. Here the architecture most teams inherited works against them. Agents were wired up quickly, usually with a static API key borrowed from an existing integration or shared across several agents. Revoking that key takes out every consumer that shares it, so the on-call engineer hesitates, and hesitation during an incident is exactly the cost of not having per-agent identity. Gateways like Kong or AWS API Gateway can model per-consumer keys, but in practice agent traffic was bolted on through a hand-rolled MCP server or a service account, and the gateway never saw the agent as a distinct caller.

The third failure comes after containment: investigation. Backend access logs record that requests arrived from the MCP server's IP or the service account. They do not record which agent, with which profile, sent which payload. Without that, you cannot answer the questions that decide the rest of the incident: what data did the agent read, what did it write, when did the behaviour start, and did anything succeed that should not have.

AI agent incident response needs the gateway as the control point

Zerq treats an AI agent as a first-class API consumer. Each agent gets its own client with its own client ID, and each environment or mode of that agent gets its own profile: the runtime access contract holding the auth type, allowed HTTP methods, IP restrictions, and an active toggle. Agents connect over Gateway MCP and send X-Client-ID and X-Profile-ID headers with every call, so every MCP tool invocation lands in the same request log as your REST traffic, tagged with the agent's identity.

That structure is what makes the following runbook possible. If your agents share credentials today, splitting them into one client per agent type and one profile per environment is the preparation step that everything below depends on.

Step 1: contain in seconds by disabling the profile

In the management UI, open Clients in the sidebar and select the agent's client.
Navigate to Profiles and open the profile the suspicious traffic is using. If you are unsure which profile, the request log entry shows the profile ID (step 2 below).
Toggle the profile to inactive and save.

From the next request, everything using that profile is rejected. The configuration is untouched: auth settings, method restrictions, and IP allowlists all survive, so restoring access later is a single toggle, not a rebuild. This is why disable-first is the recommended containment move rather than deleting anything.

If the whole client is suspect, not just one profile, toggle the client itself to inactive on the client detail page. Every request from any of its profiles then returns 403 Forbidden. Other clients, including other agents, are unaffected. That is the blast-radius payoff of one agent, one client: revocation is surgical.

Two softer containment options sit between "running" and "off". You can edit the profile's Allowed Methods down to GET only, which turns a read-and-write agent into a read-only one; any other method returns 405 Method Not Allowed. Or you can set IP Restrictions to the CIDR range where your agent infrastructure actually runs; requests from anywhere else are rejected with 403 before authentication is even attempted, which shuts out a stolen token being replayed from an attacker's machine while your legitimate agent keeps working.

Step 2: investigate with request logs

With the bleeding stopped, open Logs in the sidebar. Every request the agent ever made through the gateway is here, because Zerq logs every request with full identity context, including every MCP execute_endpoint call. Build the investigation view with filters:

Filter by Client ID, using the UUID from the client detail page. This isolates the agent's entire history.
Set the time range. Start with the last 24 hours, then widen with a custom range to find when the behaviour began.
Filter by Status code to separate what succeeded (2xx) from what the gateway or backend blocked (4xx, 5xx).
Use the Payload filter to full-text search request and response bodies at once, for example for a customer identifier you suspect was accessed.

Click any entry to open the full detail. A suspicious entry from our 2:14 incident looks like this:

{
  "request_id": "9f2c1a44-7b1e-4c02-b8f3-2d6a90e15c77",
  "timestamp": "2026-09-01T02:14:31Z",
  "method": "POST",
  "path": "/payments/v1/charges",
  "target_endpoint": "https://payments.internal/v1/charges",
  "status_code": 403,
  "latency_ms": 4,
  "client_id": "0b7d3e12-...",
  "profile_id": "prod-support-agent-readonly",
  "collection": "payments",
  "client_ip": "203.0.113.87"
}

Each field carries incident-relevant meaning. The status_code of 403 with 4 ms latency means the gateway rejected the call before it reached the backend; a 2xx here would mean the write went through and you have a data incident, not just an attempted one. The client_ip tells you whether the call came from your agent infrastructure or somewhere new, which distinguishes a confused agent from a stolen credential. The profile_id confirms which access contract was in play. And because the full request body is stored, you can see exactly what the agent tried to write, not just where.

The gateway's policy statuses give you a fast triage vocabulary across the whole log: 401 means a bad or expired credential, 403 means an attempt outside the approved scope, 405 means a blocked method, and 429 means the agent exceeded its throughput budget. A burst of 403s from an agent that normally never generates them is one of the clearest early signals of either compromise or prompt-driven scope drift, which is why reviewing denied-access trends regularly is worth building into your routine, not just your incidents.

Because all active filters are written into the browser URL, the exact filtered view you built is a shareable link. Paste it into the incident channel and the security team sees precisely the same evidence, no screenshots and no export step.

For the administrative side, the audit log answers a different question: did anyone change the platform configuration around the time of the incident? A user with the Auditor role can filter audit logs by resource type client or credential and action UPDATE or CREATE to check whether the agent's scope was widened, or an unexpected credential was minted, before the traffic anomaly started. Every audit entry records the actor ID, source IP, user agent, and the full request body of the change.

Step 3: rotate the credential

Whether or not you confirmed a leak, rotation after an incident is cheap insurance:

Open the agent's profile detail page.
Click Rotate credentials. A new token is generated and the old one is invalidated immediately, not at some future expiry.
Set a rotation expiry in hours if you want the new token to be short-lived while the investigation continues, forcing another rotation on your schedule.
Copy the new token, along with the X-Client-ID and X-Profile-ID header values, from the same page and update the agent's MCP configuration.

Because the rotation is scoped to this one profile, no other consumer is touched. The hesitation that plagues shared-key environments disappears: rotating costs you nothing except updating one agent's config.

Step 4: restore with a tighter contract

Before re-enabling, reshape the profile to match what the agent actually needs, informed by what the logs showed it doing during normal operation. A support agent that only ever read order status has no business holding POST access to a payments collection. Concretely: review the client's assigned collections and remove any it does not need, set Allowed Methods to the minimum set, add IP Restrictions for your agent infrastructure, and attach a rate limit policy, for example 100 requests per minute with a daily quota, so a future runaway loop hits a 429 ceiling instead of your backends. Then toggle the profile back to active.

Verify the restored contract from the outside, the same way the agent will use it:

curl -i https://gateway.example.com/mcp \
  -H "Authorization: Bearer $MCP_TOKEN" \
  -H "X-Client-ID: ai-agent-prod" \
  -H "X-Profile-ID: mcp-prod" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":"tools-1","method":"tools/list","params":{}}'

The agent should see exactly four tools: list_collections, list_endpoints, endpoint_details, and execute_endpoint, and list_collections should now return only the collections you left assigned. Run one deliberately out-of-scope execute_endpoint call and confirm it returns 403; a containment control you have never seen fire is a control you are trusting on faith.

An incident readiness checklist

You can test your current setup against this incident before it happens. For each question, "no" marks the gap to close:

Does each AI agent have its own credential, distinct from every app and every other agent?
Can you disable one agent's access in under a minute without affecting any other consumer?
Can you produce every request a given agent made in the last 30 days, with payloads, filtered by that agent's identity?
Can you tell, per request, whether the gateway blocked it or the backend served it?
Can you rotate one agent's credential without coordinating with any other team?
Do method, IP, and rate limit restrictions exist per agent, so restored access can be narrower than it was before the incident?
Is there an audit trail showing who changed the agent's access scope, and when?

What this looks like in practice

A fintech platform team ran a support agent over Gateway MCP with access to their orders and customers collections. One morning the dashboard's status distribution chart showed a 4xx spike, and the client metrics table put their support agent at the top of the error ranking, a position it had never occupied. Auto-refresh was on a 30-second interval, so the anomaly surfaced within a minute of starting.

The on-call engineer opened the agent's client, toggled the active profile off, and the spike stopped. Filtering request logs by the agent's client ID showed a run of execute_endpoint calls targeting payments endpoints the agent had never previously touched, all rejected with 403 because the payments collection was never assigned to that client. The client_ip on the suspicious entries matched the agent infrastructure, which pointed away from a stolen token and toward the agent itself being steered. The trigger turned out to be a prompt injection embedded in a customer support ticket the agent had ingested.

Total exposure: zero successful out-of-scope calls, confirmed from the logs rather than assumed. The team rotated the credential anyway, restricted the profile to GET, re-enabled it, and shared the filtered log URL with their security team as the incident record. The gateway architecture meant the entire response happened in one system, with every action itself captured in the audit log.

What the gateway gives you that process cannot

Per-agent identity makes revocation surgical instead of disruptive. The disable toggle makes containment a one-click action that preserves configuration for recovery. Identity-tagged request logs with full payloads turn "what did it do" from speculation into evidence. Scoped rotation removes the hesitation that shared keys create. Method, IP, and rate limit restrictions let restored access be narrower than pre-incident access. And one platform holds the whole loop, so your incident runbook is a sequence of screens that exist, not a wishlist.

Zerq is an enterprise API gateway built for regulated industries — one platform for API management, AI agent access, compliance audit, and developer portal, running entirely in your own infrastructure. See how it works or request a demo to walk through your specific requirements.