AI agents consultancy
Agents are useful when they can see the right context, use the right tools, and stop at the right boundary. We design the system that holds it together.
Good fit when
Agents become useful when the job, authority boundary, tools, evals, and human handoff are designed together.
A prototype needs production discipline
Technical and operational buyers usually come to us when the demo works but the risk model is still too vague.
Bounded agents with tool access and review
Useful for CRM follow-up, finance review, support triage, internal knowledge, and similar narrow jobs.
Can we trust an agent with our systems?
Trust comes from narrow permissions, audit logs, refusal patterns, evals, and explicit stop points.
Who this is for
We are most useful to teams who know enough to ask the right questions about authority, logs, and recovery — and who want a partner who will not hand-wave the answers.
You need a framework for where agents are allowed, what they can touch, and how their work is reviewed before the rest of the org starts shipping them.
You have a workflow worth automating. You want to ship it with evals, monitoring, and a clean rollback path — not as a prototype.
You will live with the agent every day. You want to be in the room while the authority boundary and escalation rules are set.
Three agent shapes we ship most
Each of these is a composite drawn from real engagements. Each has a tight job, a defined toolset, and a measurable handoff.
A finance team drowning in reconciliations across two ERPs and a spreadsheet sprawl. They wanted an agent that could surface exceptions without taking action.
What we'd ship
A read-only diagnosis agent that flags reconciliation breaks, drafts journal entries, and queues them for a finance manager to approve. Audit log on every read and every draft. Write-back only via human-approved actions.
A B2B sales team where post-call CRM hygiene was inconsistent and follow-ups slipped through the gaps.
What we'd ship
An agent that listens to recorded calls, drafts CRM updates and follow-up sequences, and queues them in a rep-approval inbox. Anything mentioning pricing or contract scope is escalated immediately. Anything routine is one-click approved.
An operations team needed a way for staff to query internal SOPs without sending sensitive material to public chat tools.
What we'd ship
An agent grounded in approved policy docs and runbooks, with explicit source citations on every answer, refusal patterns for off-corpus questions, and an audit log a head of compliance could sign off on.
How we work
The boundary is the work. If you cannot describe what the agent is not allowed to do, the agent should not ship yet.
A short, sharp brief: what the agent does, what it cannot do, what triggers human review, and what success looks like in measurable terms.
Every tool the agent can call, every system it can read or write, every data boundary it must respect — all designed and documented before any code runs.
A set of scenarios the agent must pass before launch, the regression suite that runs on every change, and the explicit rules for handing back to a person.
Audit trail on every tool call. Live monitoring on key metrics. A weekly review with the operator to triage exceptions and feed improvements back into the eval set.
Questions technical buyers ask us
A system that can use tools toward a defined operational goal, with constraints, logs, and escalation paths. Agents that have no logs or escalation rules are demos, not production systems.
Yes, where permissions, data controls, and integration quality make that appropriate. We design the authority boundary explicitly — including what the agent cannot do.
We narrow the job, ground outputs in approved data, test for expected failures, and keep a human review where the action is irreversible. Hallucination is a design problem, not just a model problem.
Scenario evals against historical examples, regression evals on every prompt or model change, and continuous sampling against live traffic. The eval set is part of the handover.
Sometimes one role becomes redundant; more often the work changes shape — the person becomes the reviewer and the exception handler. We name that in the engagement before the build starts.
What you can read first
We publish the engineering thinking and demo outputs so technical buyers can stress-test the approach before a call.
Primer
Agents 101
Step-by-step walkthrough of what an agent actually is.
Public demo outputs
Sample widgets
Voice qualification, triage, support, meeting intelligence.
Case studies
Composite case studies
How a scoped agent ships inside a real working system.
Built by Arkwright
CallOS
Our own agent product for recruitment agencies — same patterns.
Next step
Bring one workflow you are considering for an agent. We will scope the boundary, the evals, and the handover before we propose work.