Evals and observability: how we keep AI agents reliable
Reliability isn't a model choice, it's a discipline. The two practices that separate agents you can trust from agents you hope work.
Ask why an AI project failed and the answer is rarely “the model wasn't smart enough.” It's usually that nobody could tell whether it was working, or catch it when it drifted. Two practices fix that: evals and observability.
Evals: measured behaviour
An eval is a repeatable test of the agent against real tasks with known-good outcomes. It turns “it seems fine” into a number you can track over time. When you change a prompt, a tool or a model, the evals tell you immediately whether you helped or hurt.
Without evals, every change is a guess and every release is a gamble. With them, improvement becomes engineering.
Observability: see every step
Observability means you can replay exactly what an agent did: the inputs, the reasoning, every tool call, the cost, the result. When something goes wrong — and at scale, something always does — you can find the cause in minutes instead of guessing.
- Traces of every decision and tool call.
- Metrics on success rate, latency and cost per run.
- Alerts when behaviour drifts from the baseline.
Why this is the real work
Anyone can wire a model to a tool. Keeping that wiring trustworthy as data, models and usage change is the actual job — and it's why we run agents as a managed service rather than handing over a clever demo and walking away.