Custom AI agent development for work that spans your systems

A chatbot takes a question and returns text. An agent takes a goal, works out the steps and uses tools to carry them out. Ask an agent to 'chase the suppliers whose deliveries are late this week' and it can query the ERP for open purchase orders, compare due dates, draft a message to each supplier and queue the messages for your approval.

That reach is what makes agents useful, and it is also what makes them risky. Every agent we build has a short list of allowed tools, credentials scoped to what those tools need, and a cap on how many steps it may take before it stops and asks a person.

We start by writing down the task the way your best employee does it: what they look at, which systems they open, what they decide, and when they ask someone else. That write-up becomes the agent's specification and its first set of test cases.

Next we build retrieval over the data the agent needs, define each tool as a typed function with permission checks, and add approval to anything that writes. You see a working agent on sample data in the first sprint, and it moves closer to your live systems with each sprint after that.

Before launch we run the agent against a test set drawn from real cases, including the awkward ones. We measure how often it picks the right action and how often it correctly hands off to a person. Those tests stay in the codebase and run again whenever the prompts, the model or the tools change.

• Task specification and test cases taken from your real work
• Retrieval over documents, email and databases, with source citations
• Typed tools with permission checks and input validation
• An approval queue for every write to a system of record
• An evaluation suite that reruns on every change

Mavick, our AI operating system for textile factories, runs this design in production. A factory picks from 32 department templates, and each department gets one agent that answers with citations from that department's data. When the next step would write to a sheet, send an email or push data into the ERP, the run stops on an approval card. An approved action runs exactly once, and a declined one never runs.

Mavick reads seven ERPs live, including SAP Business One, Oracle NetSuite and Microsoft Dynamics 365, and it is in live beta. We bring the same approval pattern to custom agents in other industries.

DyorAI shows the pattern outside manufacturing. Safock designed, built and still manages this subscription platform, where AI agents combine technical, fundamental and on-chain analysis into one buy, hold or sell call for any crypto token, and subscribers receive a weekly market report by email.

Build cost depends on how many tools the agent can use, how many systems it connects to, how clean and reachable your data is, and how much approval and audit logic you need. An agent that answers questions from one document library is a much smaller project than one that reads three systems and writes to two.

Running cost depends on volume, the model you choose and how much context each step sends. We keep it down by using smaller models for simple steps, caching repeated lookups and capping steps per task. Running cost is part of the discovery conversation, and the build itself gets a fixed price in the proposal.