Getting a container running with a base model loaded is the well-documented part, and even that takes longer than an afternoon once you count dependencies, drivers, and the models themselves. The actual harness — the part that breaks a task down, routes the pieces to the right sub-agent, and recovers cleanly when one of them fails — is designed, built, and tested against your real tools before it touches production work. That design-and-test cycle is where the real time and expertise goes, not the install.

Running one model isn't orchestration — it's inference. Orchestration is what happens when a task gets broken into pieces, each piece goes to the agent or sub-agent suited to it, and the results get reassembled into one coherent output without a person stitching it together by hand. That's the actual discipline: task decomposition, routing, and synthesis, done reliably enough that it survives a piece failing partway through.

The harnesses worth deploying are the ones that improve with use — Hermes calls this "Self-Evolving Skills," where the agent writes and refines its own capabilities over time. That's also exactly the mechanism an attacker would want to exploit: a poisoned skill or an injected instruction that persists quietly in the agent's own memory. We audit what the agent has learned on a regular schedule, the same way you'd periodically review what a new hire has picked up on the job, and we keep a documented rebuild path so a bad state is a quick fix, not a crisis.

A general-purpose agent can run on a frontier commercial model or a fully local open-source one, and the difference in monthly cost at identical usage can be sixtyfold — a smaller local model can genuinely hold its own, too; recent open-weight models have matched the accuracy of models many times their size while running on a fraction of the hardware. We help you pick based on what actually matters for your use case — speed and capability, or nothing leaving your building — and then configure the details that keep the bill from surprising you: prompt caching, spend caps, and a schedule that doesn't run the agent more often than the job requires.

Handybots doesn't just deploy agent orchestration for clients — we run it internally, every day, to build and maintain our own systems, including this website. We coordinate multiple agents across active projects, version-lock and review every capability they're allowed to use, and hold ourselves to the same monitoring discipline we set up for clients. When we tell you what governance actually looks like day to day, it's because we practice it on our own work first.