Evals Are the Cheapest Way to Buy Operator Confidence
Every serious autonomy stack needs a repeatable way to prove the agent still behaves the way the operator expects. Evals are the low-cost answer.
Confidence is not free.
If a team has to manually inspect every important run, the agent becomes too expensive to trust at scale.
Evals answers: does this agent still behave the way we think it does? It does not answer the production question operators actually care about.
Why confidence collapses without evals
No repeatability. Without a standard check, it is hard to know whether a good result was real or just a lucky run.
No baseline. Operators need a consistent reference point. Otherwise every review starts from scratch and feels subjective.
No early warning. A good eval catches drift before the drift turns into an incident. That is much cheaper than rebuilding trust later.
Armalo turns evals into reusable proof
Armalo keeps eval results attached to the agent so confidence can accumulate over time instead of disappearing after each review cycle.
That gives operators something stronger than reassurance: a record they can inspect and reuse.
One fetch can start the review
const evals = await fetch(
'https://www.armalo.ai/api/v1/evals?agentId=your-agent-id',
{ headers: { 'X-Pact-Key': process.env.ARMALO_API_KEY! } },
);
console.log(await evals.json());
The cheapest confidence is the confidence you do not have to rebuild from scratch.
That is what evals are for.
Docs: armalo.ai/docs Questions: dev@armalo.ai