Autonomous systems are easy to demo and hard to run in production. An agent that clears a curated benchmark on Tuesday can quietly fail on Thursday’s real workload, and most teams find out from their users. The distance between “it worked in the demo” and “it holds up under load, under drift, under adversarial input” is where most of the engineering actually lives — and it is chronically underserved.
Applied Autonomy Research is an AI-DevOps lab that works on that gap. We do three things.
Applied research
We investigate agentic pipelines, eval harnesses, and autonomy guardrails — not against toy tasks, but against real workloads. The questions we care about are operational ones: how do you know an agent is still doing its job, how do you bound what it can do when it isn’t, and how do you make its behavior observable enough to debug at 2 a.m.
Embedded consulting
Research that never ships is just opinion. We embed with engineering teams to take AI systems from prototype to reliable, observable production — writing the code alongside your engineers rather than handing over a slide deck. The goal is always the same: a system your team can operate without us.
Education
We build workshops and curricula on LLMOps and autonomous tooling for engineers who ship. Not prompt-engineering theater — the operational discipline: evals as regression tests, guardrails as code, deployment patterns that fail safely.
We use what we sell
This site is a small proof of the approach. It was built stage by stage by
an autonomous, contract-driven pipeline: each stage was specified up front,
implemented on its own branch, gated by CI, and adversarially reviewed
before merge. Frozen interface contracts — the content schema this very post
validates against, the API shape of the contact form — keep parallel stages
from breaking each other. The draft flag on posts is the dark-launch
mechanism; the build fails if content doesn’t conform. It is a deliberately
boring pipeline, which is the point: autonomy in production should be
boring.
We are at the beginning. These lab notes are where we will write up what we learn — what worked, what broke, and what we changed — in the same direct terms.
If you are taking an autonomous system to production, or trying to keep one there, we would like to hear about it. Tell us what you’re building via the contact form.