Agentic AI on Azure, built to survive production
Custom AI agents designed around a real business process, with the access controls, evaluation, and cost tracking your security and finance teams will ask about.
Talk to an Expert →An agent that works once is a demo
Anyone can build the demo now. What separates a workflow you can run the business on is what happens on attempt four hundred: a tool call fails, an agent loops, conversation state drifts, the token bill triples overnight, and nobody can say which agent touched which record.
We design agentic workflows for attempt four hundred.
Where AI agents for business earn their place
An agent is worth building when a process has clear inputs, a decision in the middle, and systems that don't talk to each other. Work we see land:
Operations
- Onboarding and provisioning: one agent validates identity, another provisions access, a third writes the audit record.
- Document and approval routing: classify, extract, route, and escalate, with the exception path defined instead of assumed.
Decisions and service
- Decision support: model reasoning paired with live queries against your own data for forecasting or risk review.
- Internal help desk: answers drawn from your systems, not a knowledge base someone last edited years ago.
Compliance
- Recurring checks: reviews that run on a schedule and log what they looked at, so the evidence exists before an auditor asks.
If a process can't be described precisely enough to write down, an agent won't fix it. We'll tell you that during scoping.
Copilot Studio or Foundry?
Both build agents. They fit different jobs. Copilot Studio is the fast path for agents that live inside Microsoft 365 and work from content and connectors you already have, such as an internal help desk or an HR assistant. Microsoft Foundry is the path when an agent needs custom logic, your own models, several systems, or tight network and data boundaries. We pick based on the process, and plenty of clients end up with both. The platform side is on our Microsoft Foundry page.
How we build
- Start from the process, not the platform. We map the current steps, the systems involved, and what "done correctly" means. The technology follows the problem.
- Define the agents. Each one gets instructions, a model, and a scoped set of tools. We author them in code so they're version-controlled and ship through your existing pipelines.
- Connect the tools and data. Azure AI Search for retrieval, Logic Apps for existing connectors, and custom tools exposed through MCP-compatible endpoints. Every connection gets a permission scope and an owner.
- Orchestrate. We start with a sequential pattern and add branching as the process needs it, with retries, state, and logging handled deliberately.
- Evaluate before users do. Automated evaluation measures intent resolution, tool call accuracy, and task adherence, so regressions show up in your pipeline instead of a support ticket.
- Put the meter where you can see it. Usage tracking and budget alerts go in before go-live.
The step-by-step build is in our guide to setting up an agentic workflow with Azure AI Foundry.
Our take: buy capacity after you've watched it run
Agent costs are consumption-based. They scale with tokens and tool calls, not seats, which is the opposite of how most licensing behaves. We start new agent workloads on pay-as-you-go, watch real usage for a few weeks, and only then forecast and pre-purchase capacity. Prepaid credits that don't roll over are an easy way to pay for AI nobody used.
The other thing we insist on: an agent operates under an identity, and that identity reaches whatever it's allowed to reach. Scoping it tightly is the design work that matters most. If you haven't set that boundary for Copilot yet, start with an AI Guardrails Assessment.
The controls your security team will ask for
- Role-based access control over who can create, run, and change agents and workflows
- Content filters that reduce unsafe output and help block prompt injection
- Network isolation and data residency that keep traffic and storage inside your requirements
- Your own resources for storage, search, and conversation state, so compliance obligations stay yours to meet
- Logging on every agent action, so your auditors can see what each agent did and when
Once agents multiply, you'll need to see and govern all of them in one place. That's what Microsoft Agent 365 is for.
Who builds it
- Azure Expert MSP, independently audited by Microsoft and held by fewer than 1% of partners
- Solutions Partner designations in Data & AI, Infrastructure, and Digital & App Innovation
- 1,600+ engagements delivered, with agent and data projects live in our pipeline now
Frequently asked questions about agentic AI on Azure
What is agentic AI?
Agentic AI is AI that takes actions instead of only generating text. Each agent has instructions, a model, and tools it can call, such as a search index, an API, or a business system. Several agents can chain together so the output of one becomes the input of the next, which lets a whole process run without a person moving work between steps.
How is an AI agent different from a chatbot?
A chatbot answers. An agent acts. A chatbot can tell an employee how to request access to a system. An agent validates the request, provisions the access, and records what it did. That's why agents need identity scoping, audit logging, and evaluation.
Should we build agents in Copilot Studio or Microsoft Foundry?
Copilot Studio suits agents that work inside Microsoft 365 using content and connectors you already have. Microsoft Foundry suits agents that need custom logic, your own models, several systems, or strict network and data boundaries. The process decides, and many organizations use both.
What does agentic AI cost to run on Azure?
Costs are consumption-based and scale with tokens and tool calls, not seats. We start new workloads on pay-as-you-go, measure real usage, and then forecast before anyone pre-purchases capacity. Budget alerts and usage tracking go in before go-live.
Can AI agents access our existing business systems?
Yes, through connectors, APIs, and custom tools exposed as MCP-compatible endpoints. Permission is the real constraint. An agent operates under an identity, and that identity should reach exactly the data the process requires and nothing more.
How do we know an agent keeps behaving correctly?
Through evaluation and monitoring that keep running after launch. Automated evaluation scores agent behavior on intent resolution and tool call accuracy inside your deployment pipeline, and action logging gives you a record of what each agent did.
Ready to design your first agent?
No slide decks. Senior Microsoft engineers, real numbers, and a workflow that holds up in production.
Talk to an Expert →