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An autonomous agent takes a goal, decides what to do, calls real tools, reads what came back, and keeps going until the job is done — not a chatbot that answers, a worker that acts. Built on the Claude tool-use loop, gated on anything irreversible, and shipped to production.
A ledger of named agents where the line that moved was a task finished end-to-end — triaged, resolved, shipped — not a draft a human still had to do. 6 of 30 shown · ledger updates as agents scale.
Give it a goal and watch the agent run: think about what's needed, call a tool, read the result, decide the next move — looping until it's done. That's the Claude tool-use loop: the model returns tool_use, your code runs the tool, feeds the result back, and the model continues. Switch on the approval gate and it pauses before anything irreversible.
Every autonomous run leaves a trace: a plan, the tool calls it made, what it observed, where it reflected, and how it finished. This is the room we work in — each stage instrumented, each tool chosen for a reason, every side-effect logged.
Before a tool is wired, we pin down what "done" means, where the agent is allowed to act, and what must never happen without a human. An autonomous run is only as safe as the boundary you draw around it.
An agent is its tools. We define each one — name, schema, what it does — so the model can call it cleanly, and expose them over MCP so the same tools work across runtimes. Garbage tools, garbage actions; this is where most agents quietly fail.
The model returns a tool_use block, your runtime executes the tool, the result goes back as context, and the model decides again. Think → Act → Observe, repeating until stop_reason says done — with a hard step budget so it always terminates.
Reading is free; acting is not. Refunds, emails, deletes, writes — anything you can't undo pauses for human approval, or runs only inside a tight permission boundary. The agent proposes; a person (or a policy) disposes.
Run a frozen set of real tasks every change and score it — did it finish, was it correct, did it stay in bounds, what did it cost. No "looked great in the demo." A success rate that moves, or the change doesn't ship.
Live with durable execution for long runs, tracing on every step, and alerts when an agent loops too long or drifts out of bounds. We watch success as the world changes underneath it, and keep the agent finishing the job as your systems evolve.
Every failed run feeds the next: a missed case tightens a tool, a wrong action adds a gate, a wandering loop sharpens the plan. Ship-and-forget agents drift as your systems change underneath them. Evaluated and tended, task success compounds.
The models, runtimes, and tools we actually wire together to plan, call, observe, gate, and finish. No mystery framework — just the kit that keeps an agent finishing the job.
A free agent design session to start — bring one real, repetitive task your team grinds through, and we'll map the loop, the tools, and the gates it would need, then show you a working agent run against your own scenario. A prototype, not a pitch.
Get an agent design session →