You can't ship an agent you can't measure
Autonomous security agents are easy to demo and hard to trust. The difference is an evaluation harness — and treating evals as a first-class part of the system, not an afterthought.
- AI Agents
- Evaluation
- Security
There's a seductive moment when an AI agent first works. You hand it a messy, half-specified task, it reasons through a few tool calls, and it comes back with something that looks right. It's tempting to call that done.
It isn't. A demo proves an agent can succeed once. Production asks whether it can avoid failing in ways that matter — quietly, confidently, and at scale. In security, a confidently wrong answer isn't a bug, it's a liability.
Evals are the product
The most useful shift I've made is treating the evaluation harness as the centre of gravity, not a test suite bolted on at the end. Before an agent earns trust, it has to clear a battery of graded scenarios with known-good answers, adversarial inputs, and traps designed to catch the failure modes I actually fear.
That harness becomes the thing you iterate against. Change a prompt, a tool, a model — and the harness tells you whether you moved the needle or just moved the problem. Without it, you're tuning by vibes.
Ground first, generate second
Most agent failures I see aren't reasoning failures — they're context failures. The model never had the information it needed, so it improvised. Grounding agents in real, current context (and making them cite it) turns a black box into something you can audit.
The goal isn't an agent that's always right. It's an agent that's honest about its confidence, leaves a trail you can follow, and fails safely when it's out of its depth.