Agents that hold a goal across many steps, not single-turn answers that need a human between every action.
Wired into the systems the work actually lives in — your APIs, databases, ticketing, and internal services.
Specialized agents with clear handoffs, orchestrated toward an objective instead of one model doing everything.
Conversational surfaces that carry context between sessions and know when to ask instead of guess.
Every run scored, every failure captured, and the prompt, tools, and routing tuned against real traffic.
You define the objective and the constraints; the agent finds the path and shows its work.
Two weeks inside the actual process — who touches what, where the exceptions are, and which steps are worth automating at all. Most of the value is decided here.
Tools, memory, and routing built against your systems. A working agent in a sandbox with real data, not a slide deck about one.
An eval suite from your own historical cases, red-teaming for the failure modes that matter, and a measured accuracy bar before anything touches production.
Deployed with monitoring, escalation paths, and your team trained to own it. We'd rather you not need us for version two.
A scored suite built from your historical cases. You see the accuracy bar before the agent is trusted with anything.
Agents get the narrowest tool access that completes the job, with irreversible actions gated behind human approval.
Every run traced: what the agent saw, chose, and called. When something goes wrong you can read exactly why.
Confidence thresholds route the hard cases to a person instead of guessing — and those cases become evals.
Both have to plan under uncertainty, act through imperfect tools, notice when they've failed, and recover. What we learn hardening a warehouse fleet shows up in how we build your agents — and the reverse.
See the robotics work →