Introducing Agentic AI Chat in ClearSpecs AI for Azure DevOps

Working in Azure DevOps often starts with good intentions: a clear user story, a few useful comments, maybe some acceptance criteria. But as work items evolve, things get messy fast. Details get spread across fields, comments, and linked items. A simple request like "create test cases for this" or "split this story into tasks" can quickly turn into a lot of manual work.

That is exactly why I added Agentic AI Chat to ClearSpecs AI.

This is not just another chat box that generates generic text. The new experience is designed to work more like an assistant inside Azure DevOps. It helps you move from rough input to concrete, actionable work items while keeping you in control of what actually gets created or changed.

What makes it "agentic"?

The key difference is that the chat does more than just respond with text.

With Agentic AI Chat, you can ask for help in natural language, and the assistant can:

• Look up live work items in your Azure DevOps project
• Ask focused follow-up questions when important information is missing
• Propose new work items for review
• Propose updates to existing work items before anything is changed
• Help draft test cases, tasks, and backlog items
• Break larger stories down into smaller, more actionable pieces

Instead of forcing you to prepare the perfect prompt upfront, the chat helps refine the request step by step.

A better workflow inside Azure DevOps

Many AI tools assume you already know exactly what you want. In reality, that is often not how backlog refinement or work item management works.

Sometimes you start with a rough idea:

• "Create test cases for work item 340"
• "Split this story into tasks"
• "What is still missing here?"
• "Turn these meeting notes into backlog items"

In those moments, the right next step is not always immediate generation. Sometimes the best thing the assistant can do is ask one or two smart follow-up questions, gather the missing context, and then come back with a stronger proposal.

That is the experience I wanted to build.

Live work item awareness

One of the most useful parts of Agentic AI Chat is that it can work with actual Azure DevOps data.

Rather than replying with something generic, it can first look up the relevant work item and use that context to guide the conversation. That means the assistant can base its suggestions on the real title, description, acceptance criteria, and other available information instead of guessing.

This makes the output much more practical for day-to-day work.

Agentic AI Chat asks a focused follow-up question to gather missing details before drafting the final result.

From question to proposal

Another important part of the feature is that create and update actions are not applied automatically.

When the assistant wants to create a new work item or update an existing one, it first proposes the change. You can review it, adjust it if needed, and only then confirm it.

That gives you a much safer workflow:

• The assistant helps with the heavy lifting
• You still decide what actually happens
• Proposed changes stay reviewable before they are applied

For teams working in Azure DevOps, that balance matters. You want speed, but you also want control.

Practical examples

Here are a few examples of what Agentic AI Chat is especially useful for:

1. Generating test cases

You can ask the assistant to create test cases for an existing work item. If the work item is missing important context, the chat can ask a clarifying question first. Once it has enough information, it can propose one or more test cases for review.

The assistant looks up an existing work item, asks for clarification if needed, and then prepares test case proposals.

2. Splitting larger stories into tasks

Large user stories are often too broad to execute immediately. The chat can help break them into smaller tasks that are easier to estimate, assign, and track.

3. Turning rough notes into backlog items

If you have meeting notes, rough requirements, or an early idea, the assistant can help turn that input into structured backlog items.

4. Refining incomplete work items

When a story is missing detail, you can use the chat to figure out what is still unclear, what acceptance criteria may be missing, or what should be clarified before development starts.

Why I built this

ClearSpecs AI has always been about helping teams create better work items with less friction.

The first steps were focused on generating descriptions, acceptance criteria, and other structured content directly in Azure DevOps. That already saved time. But over time, it became clear that many real-world scenarios are not one-shot generation problems.

People do not always need "more text." Often, they need help thinking through the work.

That is where an agentic chat interface becomes much more useful. It can guide, clarify, propose, and help structure the work without forcing users into a rigid workflow.

Built for real backlog work

The goal of Agentic AI Chat is simple: make Azure DevOps work item management faster and easier without making it feel risky or opaque.

You can start with a rough question, explore what is missing, and move toward something concrete:

• a clearer story
• a set of test cases
• a list of tasks
• or a proposed update to an existing item

All of that happens inside Boards > ClearSpecs AI, without needing separate tools or complicated setup.

Availability

Agentic AI Chat is available in ClearSpecs AI for Azure DevOps.

If you are already using ClearSpecs AI, you can open Boards > ClearSpecs AI and start using AI Chat as part of your workflow.

Final thoughts

I believe the next useful step for AI in tools like Azure DevOps is not just better generation, but better collaboration.

That means AI that can:

• work with live project context
• ask smart questions
• help structure messy input
• and propose actions without taking control away from the user

That is the idea behind Agentic AI Chat in ClearSpecs AI.

If you work in Azure DevOps and spend time refining stories, drafting test cases, or cleaning up unclear work items, this should be a meaningful upgrade to your workflow.