AI agents working inside your systems in under twelve weeks.

An agent does not merely answer: it acts. We build the whole agent on Claude, Google Gemini or OCI — integrations, permissions, logging and brakes included.

An assistant answers. An agent decides the steps, calls systems and changes state. The difference is not intelligence — it is permission. Which is why any serious conversation about agents starts with limits, not capabilities.

What changes when the system acts

An assistant that answers badly produces a wrong answer and someone corrects it. An agent that acts badly issues the wrong invoice, replies to the wrong customer, or closes the ticket that should have stayed open. The error stops being textual and becomes operational.

That is not an argument against agents. It is an argument for designing them backwards from the usual order: first define what the agent cannot do, then what it does. An agent without a written boundary is an intern with administrator access.

How we build an agent

• Closed scope. One agent per process, with an explicit list of permitted actions. No general-purpose agents with access to everything — they are impossible to test and worse to audit.
• Its own credentials. The agent enters systems under its own identity with the minimum privileges the process needs, not a shared service account nobody can account for.
• A reversible step. Wherever the action has an external consequence — send, pay, delete, publish — there is human confirmation or a reversal window. The rest runs unattended.
• Complete logging. Every decision is recorded with what the agent saw, what it decided and why. That is what lets you answer when someone asks what happened last week.
• A cost brake. A ceiling per run and per day. A runaway call loop is an operational failure, not a surprise on the month-end invoice.

Where they pay off

They pay off in multi-step processes that cross several systems, where today a person is the bridge between them: gather data from three places, check it against a rule, update a record and notify whoever needs to know. That is coordination work, not judgement work — and coordination is what machines are good at.

Where the rule is simple and stable, we build the deterministic automation instead of the agent — cheaper to run, and from the same team. Choosing between the two is an engineering decision, and we build both.

We build the whole agent

We do not hand over a prototype for another team to finish. We build the integrations into your systems, the permission layer, the log of every action, the cost brakes and the panel your team uses to see what the agent did — in our own code, delivered working and documented for whoever maintains it.

We develop on Claude, Google Gemini and OCI Generative AI, and the architecture keeps the model replaceable: changing provider is a configuration change, not a rewrite.

Frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot returns text; an agent performs actions in systems — creates records, sends requests, updates state. So a chatbot is judged on answer quality, and an agent is judged on what it can and cannot do.

How long does it take to get an agent into production?

Six to twelve weeks for the first process, including the period where it runs alongside the current method without replacing anyone. Later ones are faster, because the logging, permission and brake infrastructure already exists.

Will the agent replace the team doing this work today?

In practice it shifts the work: the team stops executing and starts reviewing exceptions and tuning limits. If the stated goal is headcount reduction, we say so up front — because it changes the design, the acceptable review rate and the risk being taken on.

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