What is an output contract for an AI feature?

An output contract is the defined shape and constraints the model's response must satisfy before the surrounding system may use it — a JSON schema, required fields, allowed values, length bounds. It turns the model's output from free-form text your code hopes to parse into a validated interface with a pass/fail check. The reason it matters is that a model will eventually return the wrong shape — an extra field, prose where you expected JSON, a value out of range — and without a contract that is validated, your code either fails or, worse, silently passes a malformed result into a system of record. With a contract, an off-spec response is caught at the boundary and handled, instead of flowing downstream as if it were valid.

How do you handle AI failures in a product?

By assuming the model will fail and designing the failure path as deliberately as the success path. The layers are: validate the output against a contract so malformed responses are caught; retry (often with a corrective instruction) when validation fails; fall back to a safe alternative — a simpler model, a cached result, a defined manual route — when retries do not work; and never let a failed AI call take down the process around it. The goal is graceful degradation: when the AI part fails, the process does something sensible and honest rather than breaking or, worst of all, passing a confident wrong answer downstream as if it were correct.

How do you handle hallucinations in an AI product?

You cannot fully prevent them, so you design so they do the least damage: constrain the model with an output contract and grounding data so there is less room to invent; validate what you can (a cited source that does not exist, a value out of range, an account that does not resolve); keep a named person accountable for high-stakes outputs rather than auto-acting on them; and be honest about confidence rather than presenting every answer as certain. The mindset is that a hallucination is a failure mode to contain, not an edge case to ignore — the dangerous version is the confident wrong answer that looks exactly like a right one, so the defences are about catching or bounding it, not pretending it will not happen.

AI Output Contracts and Failure Handling

The model will fail. Not might — will: the wrong shape, an invented value, a confident answer that is simply wrong. The only question is what the process around it does when that happens. Most AI features are built for the happy path — the demo where the model returns exactly what was hoped for — and have no considered answer for the response that is malformed, off-spec, or plausibly wrong. That is why so many AI builds feel like demos: the first weird output either breaks the feature or, worse, flows downstream as if it were valid. In an enterprise landscape, "downstream" is not a screen someone can shrug at. It is a posting, a payment run, a master data change, a queue somebody works on Monday. Designing the failure path as deliberately as the success path is most of what separates a capability that can go live from one that stays a pilot. Here is how I think about it.

Treat the model's output as untrusted input

The mental shift: the model's response is untrusted input, like a file from a third party or a form from a user. No integration team takes an inbound file and posts it unvalidated — you check the format, the fields and the totals, and you have a defined behaviour for the day it arrives wrong. A model's output deserves at least that much suspicion, because it can be wrong in more ways than a file ever could: wrong shape, wrong values, wrong facts, or perfectly-formed nonsense.

So the boundary between "the model returned something" and "our systems act on it" needs a checkpoint. That checkpoint is the output contract.

Output contracts: define what "valid" means

An output contract is the shape and constraints a response must satisfy to be usable: a schema, required fields, allowed values, bounds. It turns free-form text you hope to parse into a validated interface with a pass/fail. Three things it buys you:

A clean failure signal. An off-spec response fails validation at the boundary instead of breaking three functions deep or, worse, being silently processed as valid.
A place to enforce constraints. Not just "is it JSON" but "is this value in range, does this field exist, does this code resolve" — the semantic checks that catch a plausible-but-wrong output.
Something two teams can point at. When the model side and the receiving system are owned by different people, the contract is what makes "the behaviour changed" a testable claim rather than an argument in a call.

The contract is also the thing that breaks silently when a prompt or model changes — which is why it is worth versioning and validating, not assuming. A model update that shifts the output format should be caught by a failing contract, not discovered by whoever works the exception queue.

The failure ladder: validate, retry, fall back, degrade

When the contract fails — and it will — you want a defined ladder, not an exception. Each rung is a more graceful response to a worse situation:

Rung	When	What happens
Validate	Every response	Check against the contract; pass → use it
Retry	Validation failed	Try again, often with a corrective instruction
Fall back	Retries exhausted	A simpler model, a cached/default result, a manual route
Degrade	No safe result possible	An honest "we could not do that," not a crash or a guess

The rung people skip is degrade — the honest failure. When nothing works, the right answer is a clear, truthful "this did not work, and here is what was not done," routed to whoever needs to act on it. Not a spinner forever, not a silent skip, and above all not a fabricated result presented as real.

One rung applies to all of them in a controlled environment: every step should leave a record. Someone will eventually ask why a specific record was changed at 03:12 on a Tuesday, and "it retried and recovered" is only an answer if the retry was logged.

The most dangerous AI output is not the one that errors — it is the confident wrong answer that looks exactly like a right one. Every part of failure handling exists to catch or bound that, because it is the failure that ships silently and is discovered by someone downstream.

Hallucinations: contain, do not pretend

You cannot fully prevent a model inventing things, so you design so it does the least damage:

Constrain. An output contract plus grounding data (the model works from your systems' data, not its memory) leaves less room to invent.
Validate what you can. A cited source that does not exist, a value out of range, a reference that does not resolve — catch the checkable lies.
Keep a named person accountable for high stakes. The model proposes and a person disposes — the same principle as using AI without letting it decide — and the usual control holds: the identity that produces the proposal should not be the one that approves it. How much of this you need depends on where the capability sits, and that placement question comes before the accuracy question.
Be honest about confidence. Do not present every output as certain. An interface that signals "this is a proposal, check it" is both more truthful and more robust than one that implies the machine is always right.

Never let the AI step sink the process around it

A structural rule worth stating plainly: a failed AI call should never take down the process it sits in. Isolate it — the enrichment fails, but the document still saves; the summary fails, but the case still routes; the model is unreachable, but the batch still completes and raises the untouched items as exceptions. The process degrades to its pre-AI baseline instead of breaking. This is ordinary resilience engineering, routinely forgotten for AI calls because they get bolted onto an existing flow as if they always succeed. The test is cheap: turn the model off and watch what the process does.

What I would decide

Design the failure path first, not last — treat the model's output as untrusted input and put a validated contract at the boundary. Build the ladder: validate, retry with correction, fall back to something safe, and degrade honestly when nothing works, because "we could not do that" beats a confident lie every time. Log each rung, because a control environment asks about one specific transaction, not about averages. Contain hallucinations instead of pretending they will not happen, and never let a failed AI call sink the process around it. The model failing is not the edge case — it is the case you build for, and doing it well is most of what makes an AI capability something an operations team will actually trust.