Broadcast APIs for AI agents: safe execution

An agent sends a schedule change. The request times out. Did the server reject it, or did the reply disappear after the change was accepted? Broadcast APIs for AI agents need an explicit answer to that uncertainty before anyone enables automatic retries.

An agent can help interpret an overrun, investigate conflicting information and propose a revised schedule. The API must enforce what it can actually change. The following designs are illustrative engineering patterns, not descriptions of existing Evrideo endpoints.

Give broadcast APIs for AI agents a narrow remit

Separate reading a schedule, editing a draft and publishing an approved change. Give each integration only the permissions it needs, and restrict where its token can be used. OAuth security guidance recommends minimal privileges and audience restrictions, with resource servers checking the permitted resource and action on every request. RFC 9700 explains those boundaries.

A valid token still needs object-level checks. The server must verify that this caller can edit this tenant’s channel, including asset identifiers nested inside the request. Keep credentials outside the model’s prompt and expose small, typed operations rather than a general-purpose HTTP or shell tool.

Bind approval to the actual change

For a high-impact action, store approval in trusted server code. Bind it to the proposed change, target channel, expected schedule revision, approving identity and expiry. A changed proposal needs fresh evaluation. A model-generated approved: true cannot establish permission.

OWASP’s excessive-agency guidance recommends human approval for high-impact actions and downstream authorization. Apply that policy to queued workers too. An approval that expires while a command waits in a queue needs an explicit execution policy.

Agent proposalNamed channel, exact change and expected revision

Trusted policy serviceCheck permissions, approval and current state

Broadcast executorTrack the job, apply the change and report the result

The agent proposes an operation. Server-side controls remain responsible for authorization, including at execution time.

Protect the schedule revision

Suppose an agent prepares a replacement after reading revision 42. A scheduler then inserts an urgent bulletin, creating revision 43. Publishing the agent’s older proposal could erase that work.

An API can expose a strong ETag and require If-Match on a write to that resource. A failed precondition normally produces 412 Precondition Failed. The client should retrieve the current schedule and reconcile its proposal before seeking approval again. HTTP Semantics defines conditional requests and distinguishes acceptance from completion.

For asynchronous execution, checking the revision when a job enters the queue is insufficient. Define a reservation or an atomic version check when the change is applied. The approval and schedule revision must still match the operation being executed.

Make retries repeat the same operation

Idempotency lets a client repeat a logical operation without duplicating its intended effect. Keep the same operation key and unchanged payload for a supported retry. Define the key’s tenant boundary, payload matching, retention period and handling of concurrent duplicates.

Stripe’s API v1 idempotency contract is a useful implementation example: after execution begins, repeated keys return the saved result, and different parameters are rejected. Its retention and error-replay rules belong to that API; a broadcast integration needs its own documented contract.

Deduplication must extend to the executor. A database record followed by an untracked queue message leaves a crash window. Document how the operation identity reaches downstream systems and how recovery determines whether an effect occurred. A cache of request keys alone cannot guarantee exactly-once execution.

1. 1
   Agent sends operation K

   The API checks the proposal and records job J against the operation key.

2. 2
   The reply is lost

   The client times out. Job J may still be running.

3. 3
   Client resolves the outcome

   Query the authenticated operation record, or retry K under the documented contract. Keep the payload unchanged.

4. 4
   Verify each stage

   Read job status, confirm the applied configuration and check the output when the change should be on air.

An unavailable status record can leave the outcome unknown. Escalate for reconciliation when the API cannot safely resolve it.

The diagram’s job identifiers are examples. They show why generating a fresh key after every timeout is dangerous: the server may treat each attempt as a separate instruction.

Set a deadline for uncertainty

AWS’s guidance on timeouts, retries and jitter explains that a timeout does not prove side effects were avoided. Configure finite attempts, capped backoff and jitter, plus an overall deadline that includes SDK retries and waiting time. Retrying independently at several layers can multiply load.

Retry only documented transient failures. Permission failures, invalid proposals and stale revisions require correction. Define the last safe execution time for a broadcast change; a late successful command can still be operationally wrong.

Stopping retries does not cancel an accepted job. Cancellation needs its own status and race-handling rules. When the result remains unknown, preserve that state and hand the operation to an engineer rather than inventing a success or failure.

Prove the result before closing the job

202 Accepted confirms acceptance for processing. Track acceptance, configuration applied and output observed separately, with failure and expiry states. A saved playlist cannot establish that the intended programme reached the output. Specify the acknowledgement and timestamped observation required for each stage.

Record the acting identity, approval reference, request fingerprint, schedule revisions and job transitions without logging secrets. Test simultaneous duplicate requests, changed payloads, stale approvals, crashes after acceptance and replies lost after application. Check that an agent cannot escape those controls through another endpoint.

Broadcast APIs for AI agents need these contracts wherever a proposed decision becomes a channel change. Start with one bounded workflow, exercise its failure paths and agree who handles uncertain outcomes. Explore Evrideo Broadcast or talk to our team about the workflows your channel needs to support.