Howdy Of us!
When you sat by means of this session in the course of the Microsoft Azure Infra Summit 2026, you already know that Anand Guruswami and Arun Rabindar from the Cloud Native Experiences staff confirmed us one thing I’ve been ready to see for some time. An AI agent that doesn’t simply spit out a Terraform file from a imprecise immediate, however really thinks about your workload, talks to you about it, after which fingers you one thing you’ll be able to put in entrance of a pull request reviewer with out holding your nostril.
That is the Azure Deployment Agent, and on the time of broadcast it was nonetheless in preview inside Azure Copilot, with the identical brains delivery as an open supply ability you’ll be able to plug into GitHub Copilot, Claude Code, Cursor, or no matter your staff makes use of. On this put up I wish to break down what they confirmed, why it issues for IT execs, and how one can get fingers on with it.
📺 Watch the session:
Allow us to be sincere in regards to the everyday. More often than not we’re not constructing a model new workload from a clean canvas. We’re stitching sources collectively separately, copying patterns from a earlier venture, looking down the best SKU, checking quotas, then arguing with coverage on the way in which out the door. Completely different admins do it other ways, and that inconsistency is the place threat lives.
Here’s what the Deployment Agent modifications for us:
- It strikes the dialog up a degree, from “which useful resource do I click on” to “what am I really attempting to construct.”
- It grounds the structure within the Azure Effectively-Architected Framework, so the output just isn’t a generic LLM guess, it has reasoning behind it.
- It separates the plan from the code, so that you and your staff get to evaluation structure earlier than any Terraform or Bicep will get written.
- It plugs into the instruments we already use. Azure portal for the guided path, GitHub Copilot and Claude Code for the ability consumer path.
Briefly, it is about taking the boring repetitive components off our plate so we will give attention to the components that want human judgment.
The Deployment Agent is a functionality contained in the Brokers (preview) expertise in Azure Copilot. Consider it as a digital cloud resolution architect that lives in your Copilot chat. You describe the workload in pure language, and it walks you thru a multi step course of to land on a manufacturing prepared deployment.
Just a few issues that stood out from Anand’s portion of the session:
- It helps multi flip dialog. You may make clear scale, safety posture, resilience, SKU preferences, area constraints, and the agent will fold these into the plan.
- It produces a human readable infrastructure plan first, full with commerce offs and the reasoning for every useful resource selection, earlier than it ever writes infrastructure as code.
- Right this moment it generates Terraform contained in the portal, with Bicep assist touchdown within the portal expertise shortly. Within the GitHub Copilot move you’ll be able to already choose Bicep or Terraform.
- As soon as the plan is accredited, you get an actual artifact. You may open it in VS Code for the Net, or have Copilot open a pull request straight into your GitHub repo.
The deployment itself nonetheless goes by means of Azure Useful resource Supervisor. That’s essential. Your tenant insurance policies, RBAC, naming conventions, and current guardrails all nonetheless apply. The agent just isn’t bypassing your governance, it’s producing code that flows by means of it.
Arun did an excellent job pulling again the curtain on the internals. The agent follows a two step sample that provides you management at each checkpoint.
- Intent seize. The agent takes your immediate and clarifies the scope, the constraints, and what success appears like. No guessing, no leaping straight to YAML.
- Plan era. It produces a structured infrastructure plan with inputs, sub objectives, a full useful resource record, configurations, SKUs, and a per useful resource reasoning part.
- Validation in a loop. The plan runs by means of evaluators backed by the Effectively-Architected Framework pillars (reliability, safety, price, operational excellence, efficiency effectivity). If one thing fails, the agent regenerates and tries once more till the plan is stable.
- Human evaluation. The plan is offered to you in plain language. You may iterate. You may say “prioritize West US 2,” or “swap that SKU,” and the agent will replace the plan in place.
- Code era. Solely after you approve the plan does the agent emit Terraform or Bicep. The generated code goes by means of syntactic validation as nicely, once more in a loop, so it really parses and is able to apply.
Below the hood within the GitHub Copilot and Claude Code path, the staff has decomposed all of this into an open supply ability (the Azure Enterprise Infrastructure Planner) plus the Azure Effectively-Architected Framework as an MCP device. The bottom agent in your editor picks up the ability, runs the phases, calls the MCP device to floor the output, after which writes the IaC. Similar workflow, completely different host.
This isn’t only a toy for greenfield demos. Just a few locations the place I see this paying actual dividends:
- New workload bootstrapping. A staff wants an online app, SQL backend, secrets and techniques in Key Vault, monitoring, and a sane area technique. As a substitute of three days of clicking and replica pasting, you describe it and evaluation the plan.
- CSV ingestion to SQL automation. The Claude Code demo Arun ran was precisely this. CSV lands, will get processed, rows replace in SQL. The agent picked smart sources, justified each, and produced Bicep able to commit.
- Standardizing throughout groups. Completely different admins ending up with completely different shapes for a similar workload is the silent killer of operational consistency. A shared agent with a shared planner ability drags everybody towards the identical Effectively-Architected baseline.
- Talent leverage for smaller groups. Not each staff has a deep Azure architect on employees. The agent encodes lots of that have and surfaces it as dialog.
- Open supply customization. As a result of the ability and MCP tooling are open, platform groups in regulated environments can fork it, add their coverage context, their tagging guidelines, their naming conventions, and ship a tuned model internally.
One sincere tradeoff. Proper now the agent is greenfield first. The staff is actively engaged on brownfield eventualities, pulling insights from current workloads and referencing current sources. When you dwell totally in a posh current property, count on the expertise to maintain getting higher over the subsequent couple of releases.
If you wish to strive it this week, right here is the quick record:
- Ask your Azure tenant administrator to allow Brokers (preview) in Azure Copilot. The toggle lives within the Azure Copilot admin middle, and with out it you’ll not see agent mode in chat.
- Within the Azure portal, open Copilot, increase to full display, and swap on Agent mode on the backside of the chat panel.
- Describe a workload in plain language. Be particular about area, scale expectations, and any compliance constraints you care about.
- Evaluate the generated plan earlier than approving. Take a look at the commerce offs part, that’s the place the agent exhibits its work.
- For the editor path, set up the open supply Azure Abilities plugin from the microsoft/azure-skills repo, level your IDE on the Azure MCP Server, and run the identical workflow inside GitHub Copilot or Claude Code.
- Ship suggestions. The staff is delivery quick and the roadmap (brownfield assist, reference workloads, scoped agent permissions, richer structure diagrams) is formed by what you inform them.
When you loved this session, the total Microsoft Azure Infra Summit 2026 playlist is up on YouTube. Classes on Deployment Stacks, the SRE Agent, Azure Native, AKS networking, and much more are all in there. Bookmark this one and share it along with your staff: https://aka.ms/MAIS/2026-Playlist
Drop your questions, your struggle tales, and your want record for the Deployment Agent within the feedback. I learn them, the product staff reads them, and your eventualities are precisely what shapes the subsequent preview drop. What would you construct with it first?
Cheers!
Pierre Roman
