Why off-the-shelf agents fall short
An off-the-shelf agent does not know your SOPs, cannot read your internal systems, and has no clear boundary on what it may decide alone. For general questions it is adequate; for operational decisions it becomes a risk.
The agents we build are explicitly bounded: their data sources are fixed, their authority is fixed, and every answer can be traced back to the document or record it came from.
The shapes most often needed
- Customer service agent — answering from an internal knowledge base and handing over to a human when the question leaves its scope.
- Document processing — reading invoices, contracts, or forms; extracting structured fields; validating them against the source system.
- Decision support — assembling context from several systems so the person deciding does not have to open five applications first.
- Internal agent — helping the team find policies, procedures, and precedent across the company's own documentation.
How we keep it under control
- Answers are restricted to defined sources and carry a reference to the originating document
- An explicit confidence threshold — below it, the agent hands over to a human instead of guessing
- Action authority is scoped per flow; consequential actions always pass through approval
- Every interaction is logged so behaviour can be audited and corrected
- Deployment follows your data policy: on-premise, private cloud, or VPC
The model is not the first decision
Model selection comes later, once it is clear what the agent must do, which data it may touch, and how costly a mistake is. Many requirements turn out not to need the largest model — and some turn out not to need an LLM at all.
Typical duration: 8–12 weeks. Final scope is set after the audit.