Reliability harness
AI reliability engineering for the moment a model times out, hits a rate limit or returns something unusable: retry, fall back to another model, or fail cleanly, instead of leaving the workflow broken.
- Policy harness
- Model routing harness
- Data transfer harness
- Guardrails harness
- Security harness
- Evaluation harness
- Observability harness
- Cost and token harness
- Reliability harness
- Audit harness
What it controls
This harness governs failure handling for every model call: retries with sensible backoff, fallback to an alternate model when the primary is unavailable or degraded, and a defined clean-failure path when neither works. It also covers rate limits and timeouts, which are routine operating conditions for AI systems, not edge cases.
The goal is that a single model having a bad moment does not become an outage for the workflow depending on it.
Why it matters
Every model, from every provider, has bad days: rate limits, latency spikes, outright outages. That is a property of running on infrastructure you don't fully control, not a flaw in any one model. Treating it as a surprise each time it happens is what turns a routine blip into a production incident.
The model is not the villain when it times out. The villain is a system with no plan for what happens when it does.
What good looks like
- Retries with backoff for transient failures, not an immediate error to the user
- A defined fallback model or path when the primary is unavailable
- A clean failure mode when nothing else works — a known state, not a crash
- Rate limits and timeouts treated as routine conditions to handle, not exceptions to log
- Reliability behavior consistent across every model a team uses
How it fits the other nine
Reliability leans on the model routing harness to know which model is an acceptable fallback, and on the observability harness to know when a failure is happening in the first place. The audit harness then records what failed and what the system did about it, so a reliability event is explainable after the fact, not a mystery.
In practice
Real patterns on the Resources page that lean on this harness:
Frequently asked questions
Is this the same as uptime monitoring?
It's related but not the same. Uptime monitoring tells you something is down; the reliability harness is what the system does automatically in response, retry, fall back, or fail cleanly, before a person has to intervene.
Does a fallback mean switching to a worse model?
Not necessarily. The fallback is whichever model or path is defined as acceptable for that workload, which may be a different frontier model, an open-weight model, or a narrower small model, depending on what the task needs.
What does 'clean failure' mean in practice?
A failure the rest of the system can recognize and handle, a clear error state and a stopped workflow, rather than a silent wrong answer or a hang that nobody notices until a user complains.
Questions about your AI rollout?
Talk to us about which harnesses fit your models and data.