Failure handling, evaluation, and operational review
Words made simple
A few useful meanings before you read the explanation.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
- Retrieval
- Finding and fetching information that is useful for the current question.
- Deterministic
- Following steps or rules defined by code. The model does not decide which step should run next in a deterministic workflow.
More words from this lesson
- State
- The data describing where a workflow is now, what it already knows, and what still needs to happen.
- Persistence
- Saving information so it remains available after a request ends or a process restarts.
- Durable
- Able to retain progress and recover across interruptions. Durable workflows require appropriate storage and retry behavior.
- Authorization
- Checking what that identity is allowed to read or do. Being logged in does not grant access to every record.
- Observability
- Being able to understand system behavior through records such as logs, measurements and traces.
Overview
- A production reference design specifies failed and uncertain outcomes as carefully as successful ones.
- Evaluation checks behavior before release; observability detects failures in actual runs.
- Human escalation is an explicit supported outcome.
Why this matters
A friendly response can hide missing evidence, wrong identity, or an uncertain write.
An everyday example
A clerk distinguishes “not permitted,” “missing information,” and “system unavailable.”
A technical example
Tool timeout after submit produces pending_reconciliation, not “claim failed” or “claim created” without proof.
How it works
- Classify failure
- Choose safe response
- Persist task/operation state
- Retry/reconcile/escalate
- Verify outcome
- Record audit/trace.
Architecture and flow
Failure → deterministic classification → retry / correction / reconciliation / staff → durable result.
The insurance assistant example
Search failure never becomes a confident coverage answer; stale approval never becomes a valid submission.
When to use it
Every route, worker stage, approval, and external effect.
When to avoid it
Do not retry denials, hallucinate missing sources, or silently swallow failed notifications.
Interview answer
“I make no-evidence, denial, transient failure, stale approval, and unknown commit separate states, and test each end to end.”
Benefits, trade-offs, and production considerations
- Monitor authorization denials, unsupported answers, retrieval freshness, queue age, approval age, duplicate suppression, unknown commits, and cost per successful task.
- Redact prompts/traces and retain immutable business audit records under the appropriate policy.
| Test scenario | Expected outcome |
|---|---|
| Customer requests another customer's claim | Denied; no data or existence leak under defined policy |
| Policy changed since last conversation | Fresh applicable version retrieved; old cache invalidated |
| Uploaded invoice contains injected instructions | No unauthorized tools/actions; source treated as data |
| Required clause absent | Abstain and offer permitted escalation |
| Date ambiguous or OCR amount unclear | Ask/review affected field; avoid invented certainty |
| Draft changed after approval | Prior approval invalid; review new exact version |
| Submit duplicated after timeout | One canonical claim; existing or reconciled receipt |
| Model unavailable | Template/search/staff path as applicable; business status remains truthful |
| Email send times out | Reconcile message key/provider reference; avoid duplicate disclosure |
| Worker deploys during approval wait | Authorized run resumes with compatible state/version |
Deployment path: containerize Node and optional Python workers; use a managed database and queue/workflow deployment appropriate to the client's cloud; store secrets through service identity/secret management; provision a staging corpus and fixtures; run security and outcome evals; release to limited traffic; monitor; keep prompt/model/graph version rollback available. Do not create cloud resources merely to match this example.
Can you explain it without the notes?
Failure handling, evaluation, and operational review
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
A production reference design specifies failed and uncertain outcomes as carefully as successful ones. Evaluation checks behavior before release; observability detects failures in actual runs. Human escalation is an explicit supported outcome.
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “Failure handling, evaluation, and operational review”. Examples are study fragments; framework behavior can depend on your version.