Give every AI agent its own identity and budget at your API gateway, make every write it can call idempotent, and return errors that tell it whether to retry, change the request or stop. Agents fan out, retry and loop in ways human users never do, so internal APIs built for people need these controls before an agent goes live.
Why agent traffic is different
Internal APIs are usually sized and secured for two kinds of caller: people clicking through an application and batch jobs that run on a schedule. An AI agent is neither. One user request can turn into a plan with many steps, each step can call several tools, and a tool can call another agent. The load arrives in bursts that follow the agent's reasoning, not a user's pace.
Agents also retry by design. When a call fails or times out, the model often tries again, sometimes with a slightly different request. An API that was safe when a person pressed a button once can now receive the same write several times in quick succession. If the endpoint creates an order, a refund or a ticket, each retry can create another one.
Finally, agents read error messages literally. A vague error or an HTML error page gives the model nothing to act on, so it guesses. A guess on a read is a wasted call; a guess on a write can be an incident.
Give each agent its own identity and budget
Shared service accounts hide which agent caused which load. Issue each agent its own credential, and where it acts for a person, carry that person's identity too, as covered in how an AI agent authenticates on behalf of a user. Then set limits at the gateway per agent, per user and per tool:
- Request rate and concurrency so one runaway plan cannot starve other callers.
- Write limits that are tighter than read limits, because writes change the business.
- Delegation depth where agents call other agents, so a loop stops at the gateway rather than in the database.
- Cost or token budgets tied to the same identity, which links API protection to controlling what an AI agent costs to run.
Keeping an inventory of these identities, as described in keeping track of every AI agent your company runs, means you can switch one agent off without touching the others.
Make every agent-facing write idempotent
An idempotency key lets the API recognise a repeated request and return the original result instead of acting twice. The caller sends a unique key with each write; the API stores the key with the outcome and replays that outcome if the same key arrives again.
For agents, generate the key in your orchestration code from the task and step, not inside the model. That way a retry of the same step reuses the key, while a genuinely new step gets a new one. Decide how long keys are kept based on how long your agents can run, and log every replayed request so you can see which agents retry most.
What about endpoints you cannot change?
Legacy systems often cannot accept a new header. Put a thin adapter in front of them that checks for an existing record before writing, or route the agent through a queue that removes duplicates. This is the same pattern used when retrofitting agents into legacy systems.
Return errors an agent can act on
Every error an agent might see should answer three questions: can it retry, when, and what should change. A rate-limit response should include a Retry-After header. A validation error should name the field and the rule it broke. A permission error should say clearly that retrying will not help. The IETF draft Agent-Friendly HTTP API Profile proposes pairing rate-limit responses with a machine-readable problem-details body for this reason, though it is an informational draft rather than a standard.
On the agent side, the orchestration layer should respect these signals: back off when told, stop on errors marked final and hand the task to a person when it cannot proceed. What an AI agent should do when a tool call fails covers that half of the contract.
Test the API under agent behaviour before launch
Load tests built from human traffic patterns will miss what agents do. Before launch, replay realistic agent traces against a staging copy of the API: bursts from one plan, repeated writes after a timeout, and a loop between two agents. Check that the gateway limits fire, that duplicate writes are absorbed and that alerts reach the team that owns the agent. Then roll out gradually, as in rolling out an AI agent in stages, and watch the API's error and replay logs alongside the agent's own metrics.
If you are connecting agents to systems that were never designed for them, talk to CodeDTX about putting these controls in place.
Frequently asked questions
Is a rate limit at the API gateway enough on its own?
No. A rate limit protects capacity, but it does not stop a single retried write from creating a duplicate order or payment. You also need idempotent writes, so repeats are absorbed, and clear errors, so the agent knows when to stop. Treat the gateway limit as the outer fence and idempotency as the lock on each door that changes data.
Should the model generate the idempotency key?
It is safer for the orchestration code to generate it. The model may produce a new key on each attempt, which defeats the purpose, or reuse a key for a different action. Deriving the key from the task and the step in code makes retries of the same step match and keeps genuinely new actions separate, regardless of how the model phrases the request.
Do read-only endpoints need the same protection?
They need limits but not idempotency. Reads cannot create duplicate records, but an agent in a loop can still call a search or reporting endpoint heavily enough to slow it for everyone else. Give reads their own rate and concurrency limits per agent, cache common answers where the data allows it, and watch for agents that read the same data repeatedly within one task.
Who should own these controls, the agent team or the API team?
Both, with a clear split. The API team owns the gateway limits, idempotency and error formats, because those protect every caller. The agent team owns how the agent responds to those signals and the budgets it requests. Agree the limits together before launch and review them when the agent gains new tools or users, so neither side is surprised in production.
