AI agent development

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.

See it in our work

At a glance

First agent
Under a month, for a narrow pilot
Model
Chosen on your evaluation set
Runs in
Your cloud account
You own
Code, prompts, evaluations, documentation

What it covers

Agents that act, within a policy.

  • 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 narrow job, judged before it is trusted.

  1. 01

    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. 02

    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. 03

    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. 04

    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

Running in your cloud, owned by you.

  • 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

Real engagements, named.

Questions

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?
Only what its policy allows. Anything that costs money or touches a customer goes through a person until the evaluation record says it can be trusted, and every action is logged either way.
How long to a first working agent?
Under a month for a narrow pilot on your own data. Multi agent systems take longer, and we say how long after discovery.
Do we own it?
Yes. Code, prompts, evaluations, documentation. It runs in your cloud account.

What clients say

In their words.

01 / 04

Life Insurance Direct
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.

Russell Cain

Founder and CEO, Life Insurance Direct

Also under AI engineering

Where it leads next.

All of AI engineering

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