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Centricone Technologies
EngagementAI Agents and Automation

AI agents that do the work, not just describe it

Centricone Technologies builds agents that act: reading from and writing to the systems you already run, following a process you defined, stopping at the approvals you set, and logging every step so you can see what happened and why.

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An agent is judged on the actions it takes when nobody is watching. We define the tools it can reach, the approvals it must stop for, and the blast radius of its worst possible step — before it runs against production.

24/7

support coverage across US and Canada time zones

100%

of code reviewed, tested, and documented before release

The demo automated one task. The business runs on twenty

01

It can answer, but it cannot act

The assistant summarises the ticket and drafts the reply, and a person still opens four systems to do the actual work. The reading was never the expensive part.

02

Nobody will let it touch production

Without scoped credentials, approval gates, and a reversible action log, no owner signs off on letting a model write to the system of record — and they are right not to.

03

It works until the process changes

The automation was wired to a screen layout or a prompt that assumed last quarter's process, and it fails silently the week the process moves.

Centricone covers the whole agent: the process it follows, the tools and integrations it can reach, the approvals and guardrails around it, and the evaluation and monitoring that keep it trustworthy once it is live.

Build

From a process on a whiteboard to an agent that runs it

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Operate

What keeps an agent trustworthy in month six

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Deliverables

What you get

  • A live agent running a process you defined
  • Tool definitions, each with its own scoped credentials
  • Approval gates on every consequential action
  • An action log you can read and replay
  • A scored evaluation set, in your repository
  • Traces, cost reporting, and alerting per run

How an agent engagement runs

1

Map the process and pick the split

What the task actually involves, where the exceptions are, and which steps an agent should own. Ends with a written scope and an estimate.

2

Wire the tools before the reasoning

Integrations, credentials, and limits first, so the agent has a safe surface to act on before it is asked to decide anything.

3

Run shadowed, then gated

The agent proposes and a person approves, on real work, until the evaluation set says the proposals are good enough to widen.

4

Widen the mandate on evidence

Approval gates lifted step by step where the record supports it, and left exactly where it does not.

When an agent is the wrong build

Agentic AI is the most over-sold category in this market. We would rather scope you into something smaller that works.

  • The task is deterministic and well specified — a script or a workflow tool will be cheaper, faster, and more reliable than a model.
  • What you need is answers from your own documents rather than actions in your systems. That is an enterprise AI assistant.
  • The work is pulling structured fields out of documents at volume. That is document intelligence, and it is a different build.
  • The underlying systems have no usable API and no appetite for one. Fix the integration problem first — an agent cannot route around it.

Why teams build agents with Centricone

The process mapped before the prompt is written

Scoped credentials and approval gates by default

A scored evaluation set that you own

Every action logged, readable, and replayable

The capability behind this engagement

Model selection and evaluation, retrieval, guardrails, logging, and the cost controls that decide whether a pilot becomes a product — covered in more depth on the AI development services page.

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The capabilities behind this engagement

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Artificial Intelligence

Centricone Technologies builds AI that survives contact with production.

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Software Management

Centricone Technologies designs, builds, and looks after custom applications.

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Cybersecurity Services

Centricone Technologies tests your applications, cloud, and pipelines the way an attacker would, then works alongside your engineers to close what we find — with the evidence trail your customers and auditors keep asking for..

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Industries we run this for

Frequently asked questions

Not sure this is the right shape? Tell us the situation and we’ll say which engagement fits.

A chatbot answers. An agent acts — it calls tools, changes records, and completes steps in a process. That difference is entirely about what you let it reach, which is why the tool definitions and the approval gates matter more than the model choice does.
Three ways, all decided before it runs: the tools it can reach are scoped and credentialled individually, consequential actions wait for a named human approval, and every run is traced so a bad step can be found and reversed. Spend is capped per task as well.
Often, yes. The Model Context Protocol has become the common way to give a model access to tools and data without writing a bespoke integration for every model, and the ecosystem around it has grown quickly. But it is a transport, not a strategy — we use it where it saves work and a direct API where it does not.
Whichever survives your evaluation set, and expect that to change. We build so the model is a configuration rather than an assumption, because the frontier moves faster than a rebuild cycle and smaller models now handle many steps at a fraction of the cost.
If they have an API, almost always. Where they do not, we look at a database view, a queue, or a small service layer in front of the system rather than driving its screens, which breaks the first time the interface changes.