Observability harness
AI observability means every call to a model, every tool it invokes and every hop in between leaves a trace, so your teams see what actually happened instead of guessing from a support ticket.
- 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
The observability harness instruments the path a request takes: which model answered, which tools or data sources it touched, how long each step took, and what came back. That trace is captured the same way whether the call succeeds, fails, or comes back wrong.
It is separate from the audit harness, which is the compliance-grade record of what happened. Observability is the operational view: the one your platform and engineering teams watch day to day to keep things running.
Why it matters
AI systems fail in ways that are easy to miss until a customer or a regulator notices first: a model drifting toward worse answers, a tool silently timing out, latency creeping up for one team but not another. Without a trace of what happened, the first sign of trouble is often the complaint, not the system.
The model is not the villain here. A model that degrades, times out, or gets routed badly is doing exactly what it was asked to do; the problem is that nobody could see it happening. Observability is the harness that keeps that invisible.
What good looks like
- Every request traceable end to end: model, tools, data touched, latency per hop
- Quality signals captured alongside performance, not just uptime and response time
- Dashboards and alerts your platform team actually watches, not logs nobody reads
- The same tracing whether a request succeeds, fails, or returns a bad answer
- Traces usable by both engineering (debugging) and the business (what is this costing us, where)
How it fits the other nine
Observability feeds the reliability harness its evidence for what is actually breaking, feeds the cost and token harness its usage data, and feeds the audit harness the raw material it formalizes into a compliance record. On its own it tells you what is happening; paired with the others, it is what makes the rest of the system possible to run.
In practice
Real patterns on the Resources page that lean on this harness:
- Real-time tracking for physical assets
- AI-assisted assessment grading with human override
- Real-time contact-centre intelligence
Frequently asked questions
What's the difference between observability and audit?
Observability is the operational trace your teams use to keep the system running day to day. Audit is the compliance-grade record of prompts, data access and outputs that you can hand to a regulator. They often draw on the same underlying events, but they serve different readers.
Does this slow down requests?
Tracing is designed to run alongside the request, not block it. The specifics depend on how it's deployed in your environment; this is a description of the control, not a performance claim about a shipped feature.
Does observability cover cost?
It captures the usage data the cost and token harness tracks against budgets, but cost control itself — limits, alerts, per-team spend — is handled by that harness, not this one.
Questions about your AI rollout?
Talk to us about which harnesses fit your models and data.