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Agents that reach into your real systems, with the tool access, guardrails, logging and escalation paths that let them run unattended. The hard part is never the prompt.
An agent that works in a notebook and an agent that runs on Monday morning without supervision are different artefacts. The second one has to handle a timed-out API, a malformed record, a permission it does not have, and a question outside its remit, and it has to do something sensible in each case.
So most of the engineering sits around the model rather than in it. What tools does the agent get, and what is deliberately withheld. What does it do when a call fails twice. What gets written to the log so somebody can reconstruct a decision three weeks later. Which actions require a human to approve before they happen.
We decide those things during scoping, before any code, because they determine the shape of the build. An agent designed for a demo and then hardened afterwards usually has to be rewritten.
What we build
Agents that run a recurring process end to end: read the queue, act on each item, escalate the ones that do not fit, and report what happened.
Learn more →Agents grounded in your own documents so answers cite something real, with retrieval tuned on your material rather than a public benchmark.
Learn more →Agents that work across an ERP, a CRM and a mailbox, where most of the difficulty is the systems rather than the reasoning.
Learn more →Agents that prepare work and stop for approval where the cost of being wrong is high. Often the right design for anything touching money or customers.
Learn more →We start by writing down what the agent is allowed to do alone, what needs approval, and what it must never touch. That document is short and it settles most of the architecture.
Then we build against the real systems early, because integrations are where estimates go wrong. An agent that has never touched the production API on a bad day is not evidence of anything.
Before launch we agree what success looks like numerically and measure the current state, so the result can be compared rather than claimed.
A chatbot answers. An agent acts: it decides which tool to call, calls it, checks the result, and either continues or hands back to a person. That difference is mostly engineering rather than prompting, because acting on real systems means handling failure, permissions and partial results.
Whichever fits the job, typically Claude, OpenAI or Gemini, and open models where data has to stay on your infrastructure. We keep the model behind an abstraction so it can be swapped, because the pricing and capability ordering changes every few months and you should not have to rebuild when it does.
By constraining what it can reach. Agents get narrow, explicitly granted tools rather than broad system access, destructive actions require confirmation, and everything is logged so a human can reconstruct what happened. Most safety in practice is scoping and permissions, not clever prompting.
A useful first agent on a bounded process typically takes four to eight weeks from scoping to production, depending on how clean the systems it must reach are. The integration work is usually the long pole, not the agent itself.
It should fail visibly and hand to a person, which is a design decision made before anything is built. We define what the agent is allowed to decide alone, what needs review, and what it must never touch. An agent with no defined escalation path is not finished.
The best starting point is a task somebody does repeatedly and dislikes. An hour is usually enough to say whether an agent is the right shape for it.
Scope an agent