AI & Intelligent Systems
AI agents that act in your systems, within limits you set
Brihat Infotech builds agents that take a process from trigger to finish, such as matching a supplier invoice to its purchase order, and hand anything unclear to a named person.
What the agent can do
- Work across your systems
use the ERP, the CRM, the mailbox and the databases you already have, through their normal interfaces.
- Follow your process
each step comes from a process map made with the person who owns the work.
- Ask a person when unsure
a confidence score decides which cases it finishes and which it sends to an exception lane.
The limits we build in
An agent needs boundaries. Each agent has these:
- 01A budget cap on what model calls may cost in a day.
- 02An action allowlist
the agent can do only what is listed, in the systems listed.
- 03An approval lane for low-confidence cases, with a named reviewer.
- 04A kill switch that stops it at once and rolls back the last actions.
Each action it takes is written to an audit trail: what it saw, what it chose, what it changed. An agent is released only after it passes a scored test of real cases from your process. Our design reviews use the NIST AI Risk Management Framework as a reference.
What the runtime package includes
- The agent runtime and its policy layer, in your environment
- Exception lanes, with the screens your reviewers use
- The action audit trail
- A rollback and kill-switch procedure
- The scored test set the agent must keep passing
Where agents fit
Good candidates are processes with many repeated steps and a clear rule for the ordinary case: supplier invoices checked against orders, support emails sorted and answered from a knowledge base, onboarding documents chased and filed. The agent handles the ordinary case. The exception lane catches the rest.
Set the confidence threshold high at first, so most cases go to a person. Lower it as the scored tests and the audit trail show the agent holding up.
What an exception lane looks like
When the agent is unsure, it stops and creates a task for a named reviewer. The task shows what the agent read, what it proposes to do and why it hesitated. The reviewer approves, edits or rejects, and that decision is logged next to the agent's own actions.
Rejected cases go back into the test set. The next release is judged against them.
Who builds it
Two AI engineers build it with a domain analyst and an architect. The analyst spends time with your process owner, so the agent follows the way work happens today rather than the way the manual describes it. Releases reach a live environment every fortnight, and how we engage sets out the rest.
Agents work best on top of systems that already hold clean records. If yours do not yet exist, see custom ERP or systems integration and APIs first.
More in AI & Intelligent Systems
Name one process where people copy data between systems all day.
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.