AI agents that only do what you allow.
An agent is an AI that can take actions: look things up, fill in forms, update records. We build agents with a fixed set of allowed actions, a dry-run mode and human checkpoints, designed for what can go wrong first.
When you need it.
An agent that can do anything will eventually do the wrong thing. We decide what it may do, what needs a person, and what happens when it fails, before we decide how clever it should be.
- Repetitive multi-step workPeople copy information between systems, check it and update records all day.
- Fear of letting AI actThe team likes the idea but worries about an AI changing the wrong record.
- A demo agent that goes off scriptIt works on the happy path and wanders off on real cases.
- No record of what the agent didWhen something goes wrong, nobody can see why.
How it works.
The shape of what we build, from your data to the people who use it.
- A task arrivesTicket, email, request
- PlanPick from allowed actions
- Dry runShow what it would do
- CheckpointA person approves when needed
- Act & logEvery step recorded
What we build.
The parts of a production system, each one tested and handed over.
Bounded tool sets
The agent can only call the actions you approve, each with limits on what it may change.
Dry-run mode
The agent shows what it would do, without doing it, so you can test on real cases safely.
Human checkpoints
Steps above a threshold, or outside the usual pattern, wait for a person to approve.
Failure handling
Timeouts, retries and a safe stop when the agent is unsure or a system is down.
Audit trail
A record of every decision and action, readable by the people who own the process.
Workflow evals
Real cases replayed against the agent to catch regressions before release.
What you get.
- An agent connected to the systems it needs, with only the actions you allow
- A dry-run mode for testing on real cases
- Approval steps where a person must decide
- A full log of every action
- An eval set of real cases run before each release
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Agents
- Tool callingDry-run modeHuman-in-the-loop checkpointsAudit logs
- Models
- OpenAIAnthropicAWS BedrockGoogle Vertex AIAzure OpenAISelf-hosted models
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyWhat makes this different from a chatbot?
A chatbot answers. An agent takes actions in your systems, so we limit what it can do, test it on real cases and log every step.
Can a person stay in control?
Yes. You decide which steps need approval; the agent waits for a person there and records who approved what.
What if the agent gets it wrong?
It can only use the actions you allowed, risky steps wait for a person, and every action is logged so it can be traced and reversed.
Where do we start?
With one workflow that is repetitive, well understood and easy to check. We run it in dry-run mode on real cases before it acts on its own.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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