AI agents
Assistants that do work instead of just answering. Tools, guardrails, and approval gates included.
Support that resolves, not deflects
An agent that reads the ticket, checks the customer record, fixes what it can, and escalates the rest with the full context attached.
Back-office automation
Data moved between systems that don't talk to each other: orders into accounting, emails into the CRM, reports into spreadsheets.
Real-time voice agents
Booking, intake, and answering lines built on the OpenAI Realtime API. Callers get answers, your team gets structured records.
Research and monitoring
Agents that watch sources, gather what changed, and deliver a digest a human can act on, on a schedule.
Kept on a leash.
Before an agent goes anywhere near production, we settle what it's allowed to do. Every agent we ship comes with three controls.
Scoped permissions
An agent can only touch the tools we give it. Read access is cheap to grant, write access is earned per action.
Approval gates
Anything irreversible, like sending money or deleting records, waits for a human click. The agent prepares, you approve.
Full audit trail
Every tool call and decision is logged. When something looks off, you can replay exactly what happened and why.
Anatomy of an agent we'd ship
Every agent is a loop of model calls and tool calls. The difference between a demo and a production agent is what surrounds that loop.
- A tool layer with the narrowest permissions that do the job
- Memory and state, so it picks up where it left off
- Approval gates before anything irreversible
- Hard budget caps per run, so a bad loop costs cents, not hundreds
- Full logging, so any run can be replayed and explained
- Evals on real cases, so changes get measured before they ship
When we say no
Agents add cost, latency, and new ways to fail. If your workflow has fixed steps and clean inputs, we'll tell you a plain script beats an agent, and build that instead. The agent projects we take on are the ones where messy input or case-by-case judgment makes the loop worth its price.
Questions we actually get
Short answers, no sales language. If yours isn't here, ask an engineer directly.
Ask us anythingWhat stops an agent from doing something wrong?
We design for the mistake instead of hoping it never happens. Agents get the narrowest set of tools that does the job, destructive actions sit behind human approval, and every step is logged. The model will occasionally be wrong, so we build things so that being wrong is cheap.
Agent or automation script, how do you choose?
If the steps are fixed and the input is clean, a script is cheaper and more reliable, and that's what we build. An agent earns its cost when the input is messy human language or the path changes case by case.
What do you build agents with?
The OpenAI Agents SDK, the Claude Agent SDK, and the Vercel AI SDK, chosen per project. All of them come down to a model calling tools in a loop; the engineering that matters is in the tools, guardrails, and evals around it.
What does running an agent cost?
More than a single AI call, because an agent thinks in steps. We design the loop for cost with smaller models on routine steps, caching, and hard budget caps per run, and you see projected costs before anything ships.
Have work an agent could take over?
Describe the workflow. We reply with whether an agent fits, and what it would take.