Assistants that act on your systems — inside the lines you draw.
Built on Azure AI Foundry and Azure OpenAI, agents can answer from your documents, act in your workflows, and hand off to people when they should. The engineering that matters is the boundary, not the model.
Answer from your policies, contracts, and manuals — with citations — instead of from the open internet's idea of your business.
Triage tickets, draft responses, reconcile records — actions defined as tools with explicit permissions, logged end to end.
Plain-language questions over governed warehouse data. On Fabric estates, see AI Data Agents.
Five layers, and the model is only one of them
Most agent projects spend their budget on the model and improvise the rest. The rest is what determines whether the agent is safe, accurate, and affordable.
The documents and data the agent may draw from — indexed with Azure AI Search, kept current, and scoped so "I don't know" beats a confident guess.
What the agent can do — each action an explicit, permissioned tool. An agent that can update a record can't also delete one by accident.
The agent acts with Entra ID identity and the asker's permissions. Access policy is enforced by the platform, the same as for a person.
Token budgets, model routing (small models for small jobs), and per-agent cost dashboards — usage growth shows up as a line item, not a surprise.
A test set of real questions and tasks with known-correct outcomes, run before rollout and on every change. This layer is the difference between a demo and a system.
Pick one process. We'll scope the agent and its guardrails.
If the data or documents underneath aren't ready, we'll tell you what to fix first — an agent over bad grounding just answers wrong, faster.