Triages integration issues from live customers and separates a configuration problem from a defect, so engineering only sees the second kind.
An AI agent for API integration support handles the issues that arrive once an integration is live and something stops working. The reports look identical whatever the cause: an endpoint returning errors, data not arriving, a webhook that fired once and never again. The underlying reason is usually one of a small number of configuration problems — an expired credential, a changed endpoint, a permission that was revoked on the customer's side, a payload that stopped matching the schema after a change nobody mentioned. The agent works through the checks your team would run, resolves what it can, and escalates what remains as a defect with the diagnostic work already done rather than as a report to be reproduced. The distinction that makes this workable is that configuration causes are checkable and defects are not. Anything that can be confirmed by looking at credentials, permissions, endpoints or schema belongs in the first pass; anything requiring a hypothesis about the code belongs with engineering. Built on Agentplace: the agent runs on its own page, so a visitor finishes the whole request in the conversation.
Runs the checks first, escalates what survives them.
Collects the error, the endpoint and the timing of the failure
Works through the configuration checks your team would run
Identifies expired credentials and revoked permissions
Resolves the configuration causes it can confirm
Escalates the rest with the diagnostic work attached
Never guesses at a cause it has not confirmed
An integration issue reported to engineering starts with reproduction, and reproduction is expensive: it needs the customer's context, their configuration and often their data. A large share of that work ends with the discovery that a credential expired. Running the configuration checks first changes what reaches engineering from a report into a diagnosis — and for the majority of issues it resolves them without engineering being involved at all. The customer benefit is larger than the internal one. An integration failure is usually blocking something operational on their side, and the difference between a resolution in twenty minutes and one in two days is not a support metric, it is whether their business ran that day.
Collect the failure, run the checks, escalate what survives.
The error, the endpoint, when it started and what changed on either side.
Credentials, permissions, endpoints and schema — the checks your team would run first anyway.
With the checks already documented, so engineering starts from a diagnosis rather than from reproduction.
Nine issues, two of which were defects.
Scenario: a team sampled a month of integration issues and found that most had consumed engineering time before resolving as configuration. Over one week the agent handles nine reports. Four are expired or rotated credentials, identified from the error and resolved with the customer directly. Two are permissions revoked during the customer's own access review, which the agent identifies by checking what the token can currently do and which the customer fixes on their side. One is a payload that stopped matching the schema after the customer changed a field, found by comparing what arrived against what the endpoint expects. That leaves two. Both escalate to engineering with the credential, permission and schema checks documented as already ruled out, and both turn out to be genuine defects. Engineering spent its time on the two rather than on all nine.
Anybody whose engineers reproduce configuration problems.
Live integration failures block the customer's operations.
Your team investigates issues that were never defects.
Reproduction is most of the cost and most of it is wasted.
Integration reports look identical whatever the cause.
Resolution time here is felt operationally, not as a metric.
Recurring configuration failures are a design signal.
Where the checks run and what escalates.
Holds the check sequence and what each customer's integration uses.
Takes the report and confirms what has been ruled out.
Receives escalations with the diagnostic work attached.
Works with the customer on configuration fixes on their side.
Links a confirmed defect to the code that owns it.
Reports issue causes and how many reach engineering.
The integration failures worth triaging first.
Questions about triaging integration issues.
An AI agent for API integration support collects the specifics of an integration failure, works through the configuration checks your team would run — credentials, permissions, endpoints, schema — resolves what it confirms, and escalates the rest to engineering with the diagnostic work already documented.
In most teams the large majority. Credentials, permissions and schema changes account for the bulk of live integration failures, and all three are checkable without an engineer.
It can identify the cause and tell them precisely what to change. Making changes inside a customer's environment is a different level of access and a decision worth making deliberately rather than by default.
Because it changes what engineering receives. A report needs reproducing; a diagnosis with three causes ruled out can be worked immediately, which is most of the time saved.
No. An unconfirmed cause sends the customer to change something that was not wrong, which costs more than saying the checks were inconclusive and escalating.
Rarely why teams do it. The volume moves off engineering and resolution time drops, which matters more because these failures are usually blocking something on the customer's side.
Which failures recur. Credentials expiring silently across many customers is a product problem rather than a support one, and it is only visible in aggregate.
It runs on Agentplace. Agentplace is an AI agent platform where the agent gets its own page, talks to your visitors there, and carries the request through to the end instead of handing it to a form. You can open this template and change any step before you publish it.
Triages integration issues from live customers and separates a configuration problem from a defect, so engineering only sees the second kind. Open it in Agentplace and change any step before you publish.