Agents that take real actions in your systems, not only chat.
An agent here opens a ticket, checks a record, routes to the right team, or chases a deadline. It plans, calls your tools, recovers when a step fails, and hands over to a person where a wrong action would cost something. Every action is logged.
What it covers
Multi step agents
Agents that hold a whole task in view, decide what to do next, call your APIs and tools, and recover when a step fails instead of stopping.
Tool and system integration
MCP servers and connectors that give the agent controlled access to your CRM, ticketing, ERP or database, with a policy on what it may and may not do.
Human in the loop
Where a wrong action costs something, the agent drafts and a person approves. Where it does not, the agent acts and the log shows what it did.
Multi agent workflows
Several specialist agents that route, delegate and share context, for jobs too wide for one.
How we do it
One job first
We pick the narrowest task that is still worth doing and build the agent for that alone. Wide agents fail quietly; narrow ones can be judged.
Built by people who have built an agent platform
Our Labs team built AgentOS from the ground up: the harness, tool orchestration, memory, guardrails, tracing and role based access. Your agent is built for you, on the stack that fits it, by engineers who have already solved those problems once and know what breaks in production.
Guardrails written in plain language
What the agent may do, what it must refuse, and what it says when it cannot help. Deployed as sentences, running on every interaction.
Judged before it is trusted
Your team marks real runs right or wrong. That marked set is the evaluation suite, and nothing ships without passing it.
- 1
One job first
We pick the narrowest task that is still worth doing and build the agent for that alone. Wide agents fail quietly; narrow ones can be judged.
- 2
Built by people who have built an agent platform
Our Labs team built AgentOS from the ground up: the harness, tool orchestration, memory, guardrails, tracing and role based access. Your agent is built for you, on the stack that fits it, by engineers who have already solved those problems once and know what breaks in production.
- 3
Guardrails written in plain language
What the agent may do, what it must refuse, and what it says when it cannot help. Deployed as sentences, running on every interaction.
- 4
Judged before it is trusted
Your team marks real runs right or wrong. That marked set is the evaluation suite, and nothing ships without passing it.
What you get
- A working agent in your cloud, on the job it was scoped for
- Tool integrations with a written policy on what the agent may do
- An evaluation suite your team marked, run before every change
- Tracing, cost and latency on every run
- Source code, documentation and a handover session
Where we have done it
Life Insurance Direct: the agent reads every sales call on both sides and hands the team a review queue in risk order. 9,218 calls in four months, and it reports nothing to anyone on its own.
Life Insurance DirectTalent Carriage: a voice agent that works out what is wrong, picks one of eight specialist teams, and writes the ticket before the caller hangs up. 40 of the first 41 needed no correction.
Talent Carriage
Questions we get asked
Which model do you use?
The one that does the job most reliably at the price. We test Claude, GPT, Gemini and open source models against your evaluation set and choose on evidence. The model is a setting, so it can change later.
Can the agent do damage?
How long to a first working agent?
Do we own it?
Also under ai engineering
Talk it through
Tell us what you need from ai agent development.
Thirty minutes with one of our architects. We will tell you whether it is a pilot, a build, or not worth doing yet.