An agent that does the job, not one that talks about it.
A chatbot answers. An agent acts: it books the slot, drafts the quote, moves the ticket, reconciles the row. That difference is the whole engineering problem, because a wrong answer is a bad experience and a wrong action is a refund, a complaint, or a compliance incident. Everything we build here starts from what the agent is allowed to do without asking.
This is for you if
- A queue of repetitive decisions builds up overnight and someone works through it every morning
- The steps are written down and the exceptions are known, they are just done by hand
- You already have APIs for the systems involved, or could have
- Volume is high enough that a person doing it is a real cost, not a rounding error
It isn't, if
- You want it to handle anything a customer might ask. An agent with an unbounded remit is a liability with a chat interface.
- The action is irreversible and expensive, such as moving money or cancelling contracts. Those stay behind a human, and we will design the review step instead.
- Nobody can describe the current process end to end. An agent built from a half-described process automates the half that was described.
What actually happens.
- 01
Draw the boundary first
What the agent may do alone, what it may propose for a person to approve, and what it must never touch. This is a business decision, not a technical one, and it is made before anything is built.
- 02
Build the tools it uses
Each action becomes a narrow, validated tool with its own permissions rather than a general key to your systems. An agent can only do damage through a tool you gave it.
- 03
Test against the awkward cases
Not the happy path, but the ambiguous request, the missing record, the customer trying it on. The agent's behaviour on those is what determines whether it can be trusted unsupervised.
- 04
Traces, handoff, and rollout
Every run leaves a readable transcript, so a bad outcome can be explained rather than guessed at. It goes live on a slice of the volume first, with a person watching, then widens.
What you get
- Tool-using agents wired into the systems you already run
- Explicit permission boundaries: what it may do alone, what needs a human
- Traces and transcripts for every run, so a bad outcome is explainable
- Handoff to a person built in from the start, not added after the first complaint
Built with
- Anthropic
- OpenAI
- MCP
- LangGraph
- Temporal
The outcome
A queue that clears itself overnight instead of on Monday morning.
Agents that take actions in your systems, booking, quoting, triaging and reconciling, with limits on what they may do unsupervised.
Get a free consultationScope and a fixed price before anything is committed. No obligation to proceed.
No dark periods. No surprise invoices.
A structured engagement from the first call to launch, so you always know what is happening and what it costs.
Week 1 · Discovery
Scope & fixed price
Then, every week after
A working demo.
The ones people actually ask.
What happens when it gets something wrong?
It escalates rather than improvises, and the trace shows exactly what it saw and chose. Designing the failure path is most of the work here, because a system that only behaves well when things go well has not been designed at all.
Can it work with our existing software?
If it has an API, yes. Where it does not, the honest options are a narrow integration layer or leaving that step to a person, and we will say which one your case is before quoting.
How is this different from workflow automation?
Workflow automation follows rules you wrote. An agent decides which step to take, which is worth paying for only when the decision genuinely varies. Where a rule would do, we build the rule. It is cheaper and it never surprises you.
Do you use MCP?
Where it fits. Model Context Protocol gives tools a standard shape, which makes them reusable across agents and easier to audit. It is a means to the permission boundary, not the point of the build.
Often bought alongside
ML & Data Science
Forecasting, scoring and segmentation on your own data, with the pipeline that keeps it accurate after launch.
DevOps Services
Pipelines, environments and observability, so shipping on a Friday stops being a brave decision.
Cybersecurity Services
Finding what an attacker would find first, fixing it, and leaving you with the evidence your customers ask for.
All services · Working on your own career instead? Career services for engineers
What is the most expensive thing your team still does by hand?
Tell us, and we'll tell you honestly whether software can fix it, and roughly what it would cost. No pitch deck.