AI EngineeringAI agents
AI agents
The repetitive work, handled end to end. Agents that operate inside your systems, take the drudge work, and log every run for a person to check.
An agent reads the queue, does the task, files the result and asks for sign-off. It works inside the systems your team already uses, under your own access controls.
Where a person’s judgment is needed, the agent stops and waits for it.
What we build.
OpenKit builds agents that run inside the systems your team already has, and hands the run logs over with them.
- Document agents
- Read, extract, summarise and file at volume.
- Process agents
- Multi-step workflows across your CRM, inbox and drives.
- Review agents
- First-pass checking with a person signing off.
- Computer-use agents
- For closed systems with no API, the agent works the screen.
Every agent ships with run logs, a review interface, and a measured baseline to judge the outcome against.
Agents are one of four builds under AI engineering, alongside retrieval systems, language-model development and voice.
The work that sits between systems.
The repetitive work that never gets automated, because every pass needs a little judgement before it can move on.
- Document work
- Read, extract, and classify contracts, forms, and reports, then write the result into the right system with a citation back to the source line.
- Cross-system data entry
- Move and reconcile data between a CRM, an ERP, and a spreadsheet without a person re-keying it three times and introducing three chances to get it wrong.
- Compliance and checks
- Run the same rule-based check on every case (completeness, eligibility, policy) and surface only the ones that fail or fall outside the rules.
- Triage and routing
- Read an inbound query, decide where it belongs, draft the reply, and route anything unusual to the person who should actually handle it.
- Closed-system admin
- Where a legacy tool has no API, an agent can drive it on screen under your access controls, the pattern behind the Stow Brothers estate-agency pilot.
Your first agent in four weeks.
- Week 1
Identify
The workflows where the hours actually go
- Workshop with your team
- Workflow mapping
- ROI baseline agreed
- Week 2
Build
Wired into your systems
- First working version
- Edge cases worked through
- Week 3
Prove
Live on real workflows
- Measured against baseline
- Outcomes signed off
- Week 4
Scale
Production rollout
- Team trained to extend
- Next agent scoped
When we will talk you out of it.
Four cases where an agent is the wrong tool, and we say so before anyone spends.
- The task is a single deterministic rule. A formula or a plain integration is cheaper and more reliable than an agent, and does not drift.
- There is no measurable process to point at. Agents automate a workflow; if nobody can describe the current one, the honest first step is an audit, not a build.
- The volume is tiny. If a task runs a handful of times a month, the build rarely pays back: a person doing it is the right answer.
- The cost of a wrong action is catastrophic and unreviewable. If no human can sit in the loop, an autonomous agent is the wrong shape for the risk.
Where an agent build sits.
- Inside an AI Audit and Transformation
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The first agent ships in the four weeks, on a workflow the audit ranked and your team agreed.
How the audit runs - On an agent roadmap with an Embedded AI Lead
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A queue of agents, shipped monthly, with an OpenKit team working inside yours.
How the embedded engagement runs
What an agent may and may not do is agreed before any model is chosen: that is what the AI Charter is for.
“Having completed our second major AI agent development project with OpenKit, I can confidently say they’re the real deal. Our system analyses large, complex and poor quality documents with remarkable accuracy … you can instantly verify every AI output against source documents.”
The Stow Brothers figure comes from a computer-use agent blueprint on a closed-API CRM. All case studies
What teams ask before the first agent.
Which workflows suit an agent first?
High-volume, rule-heavy, low-ambiguity work: the audit ranks yours by hours saved against effort to build.
What stops an agent doing something wrong?
Scope: agents act only inside the systems and steps they’re given, every run is logged, and anything that carries weight waits for a person’s sign-off.
Do agents replace people?
They take the repetitive slice. On the BAiSICS platform a commercial lease review that took 120 minutes now takes 10, and the time that remains goes on checking the extractions against the source document.
Our core system has no API.
That’s what computer-use agents are for: Stow Brothers ran on a closed CRM.
What happens when the process changes?
Your team is trained to adjust and extend the agent; that handover is part of every build.
What does AI agent development cost in the UK?
Published UK market ranges put a single-workflow agent at roughly £10,000 to £30,000, and multi-agent work spanning several systems at £40,000 to £150,000 (OpenKit AI development cost guide). Regulated environments add ten to twenty percent for the security and audit work. OpenKit never quotes from a rate card: we scope each build against a defined outcome first.
How is agent automation different from traditional RPA?
Traditional RPA follows a rigid script that breaks the moment a layout, format, or wording changes. Agent automation uses language models that read context, so it handles unstructured documents, variable formats, and the edge cases a conventional bot fails on. Where a closed system has no API, OpenKit can drive it as a person would, on screen, under access controls.
Are agents secure enough for regulated industries?
They can be, when built for it. OpenKit is ISO 27001 and ISO 9001 certified and holds Cyber Essentials. Agents can be deployed in a UK region or on your own infrastructure, with role-based access, data-residency rules, and a full audit trail of what each agent read, decided, and did. We build that governance in from the first workflow, not as a later add-on.
Do we own the agent, or is it locked to a vendor?
You own it. OpenKit builds systems that run on infrastructure you control, with no per-seat licence that scales against you and no dependency on a platform we resell. You finish the engagement with a working agent and a team able to run it.
Not ready to talk? The free AI readiness check scores where you stand in about five minutes.
Find your first workflow.
We start with a conversation, audit where AI actually pays back, and build the first automation into how your team already works.
We reply within one working day.