AI agents that finish
the job, not the sentence.

What an AI agent is

An AI agent is a system that takes a goal, works out the steps to reach it, calls tools and data sources to carry them out, checks its own result against those sources, and stops for a human when the decision sits above an agreed threshold. A chatbot returns text; an agent returns finished work and a record of how it got there.

Ask a chatbot about your exposure to a counterparty and it returns a paragraph assembled from whatever the model absorbed in training. Ask an agent and it breaks the goal into steps, decides which sources it needs, queries the ledger, pulls the filings, calls the risk system’s API scoped to that task, drafts the reconciliation, checks its own totals against the sources, and flags what does not agree.

One produces text. The other produces work, and a record of how it got there.

Custom AI development for an enterprise is a different exercise from generative AI development in general. The goal is not a model that writes well; it is a system that completes a defined piece of work inside the tools you already run, and leaves a record of what it did.

What custom AI agent development involves

Scope

What the agent is responsible for, what it must never do, and where the approval threshold sits. This is the design decision everything else follows from.

Orchestration

Planning and state built on LangGraph, so an agent’s path through a task is explicit, inspectable and resumable rather than emergent.

Tools and integrations

Scoped, authenticated access to the systems you already run — databases, internal APIs, document stores, ticketing, ERP and CRM.

Text-to-SQL

Text-to-SQL is the translation of a plain-language question into a database query that the agent then runs. Scoped to a read-only role, restricted to the tables it is permitted to see, with the generated query kept in the audit trail so an answer can be checked against the SQL that produced it.

Verification

The agent checks its own output against the sources it used, and surfaces the disagreements instead of smoothing them over.

Human-in-the-loop

Anything above the approval threshold waits for a person. The agent prepares the decision; it does not take it.

Deployment

Containerised and orchestrated on your infrastructure — on-premises, private cloud or air-gapped.

Security is part of the build

Each agent runs with only the permissions its task needs, scoped to the session and limited in time. Every action is recorded. Tool access is authenticated and narrow, not a shared service account with the run of the estate.

Six principles govern this, and they are enforced in the runtime rather than documented in a policy and hoped for. An agent that can reach more than its task requires is a vulnerability regardless of how well it behaves in testing.

How an engagement runs

Discovery first: how the work is done today, where the time goes, and which step is actually worth automating. Then architecture, build, integration and deployment, with the system running in your environment at the end of it. Kickoff to production is typically 10–12 weeks.

Delivery is founder-led — senior people who designed the system are the ones building it, without an account-management layer between you and the work.