AI Agent Studio: Swiftly Go From Data Contract to Certified Marketplace

Key Summary

Data products are one of the most powerful assets for your company. But a data product simply sitting in a marketplace is much like a beautiful, expensive car in a showroom: it has the looks, but until the wheels are in motion and the engine is running, it is just a piece of metal sitting in a store. 

Think of AI Agent Studio as the car’s steering wheel. This product transforms certified data products from static assets into dynamic, conversational agents that business users can interact with. The interactions don’t require SQL or waiting for engineering to take on, and are in plain English. 

Below, we'll unpack how AI Agent Studio makes AI accessible to every business user, what differentiates it from generic chatbot builders, and why grounded accuracy matters more than ease of use. 

AI Agent Studio: Empowering Business Users 

AI Agent Studio is the self-service layer of Xebia Axis that empowers business users to create, manage, and interact with AI agents. It removes the technical barriers that have traditionally kept AI in the hands of specialists. 

With the help of Agent Studio, business users can: 

The beautiful thing? No technical expertise is required. Anyone who knows what data they need can easily build an agent. 

How AI Agent Studio Works 

Every business user interacting with AI Agent Studio will have a personal "My Agents" panel where they can see all the agents they've built. Previously created agents are always available. There is no need to rebuild for recurring questions or needs. 

A user can simply ask: "Can you give me the latest inventory position?" and the AI agent fetches the answer from the underlying data product. On the right-hand side, the agent shows exactly how it generated the result: the lineage, the query it fired, and how it interpreted the question. This white-box transparency builds trust and roots results in the user’s needs. 

Three Options to Create an Agent 

When creating a new agent, users have three distinct options: 

1. Data Product Agent 
This option creates an agent on top of a certified data product. All available data products in the Axis marketplace are clearly listed. The user can simply select one, click create, and the agent is ready to go. No configuration and no code required here. 

2. Knowledge Assistant 
This option creates an agent on top of unstructured documents, such as PDFs, Word files, presentations, and emails. Users upload the documents they want the agent to interpret and process, and Axis handles the ingestion and indexing in the backend. The agent can then answer questions based on those documents. 

3. Super Agent 
This is the most powerful option. A Super Agent combines multiple capabilities, such as data product agents, knowledge assistants, and even other agents, into a single conversational interface. A business user can ask the Super Agent a question, and it will route to the appropriate underlying agent or combine information from multiple sources. 

Example: Super Agent for Inventory & Sales 

A regional sales manager opens the Agent Studio to asks their Super Agent to demand a comparison between the sales of two different quarters, as they are worried about the stock of a certain product. 

The Super Agent will not answer this question on its own, since it spans different domains and will route it to: 

Once the data has been obtained, the Super Agent will combine the data into a single response containing a ranked list of at-risk products, the sales trend behind each one, and a note on which items are most urgent given supplier delays. It will also keep the sources on track, as the user can always check which agent contributed to what part of the reply. 

This entire interaction doesn’t require SQL knowledge, waiting, or opening a ticket. It is a simple business question, answered in plain English, grounded in governed data. 

The Access Problem 

In traditional organizations, getting answers from data follows a broken pattern: 

This approach is slow, frustrating, and unsustainable. Xebia's Data & AI Monitor 2026 survey among more than 600 companies confirms how organizations know where to apply AI, but value realization remains stubbornly low. The bottleneck isn't strategy, it's execution. Download Xebia’s Data & AI Monitor 2026. 

Organizations are hungry for better ways to extract value from their data and develop use cases for applying AI. But only a minority believe they are fully harvesting the value AI brings. The gap is not data, but the ability to talk to it and access it properly. 

The agent technology that makes data debt urgent also makes it faster to resolve, but only if users can talk to their data. 

Sharing Agents: MCP and A2A 

Once an agent is built, it can be shared with others. Axis Agent Studio supports two standard protocols for agent sharing: 

A user simply clicks "Expose," receives the MCP server code, and sends it to their technical team. The team integrates it into downstream applications, and there is no redevelopment required. 

What Makes Axis Agent Studio Different 

Generic chatbot builders offer ease of use. But they don't guarantee accuracy or speed. Axis AI Agent Studio combines self-service with grounded accuracy and speed through two key differentiators. 

1. Grounded Agents Through Ontology 

Many AI chatbots guess what your data means. Axis agents don't. Behind the scenes, Axis creates an Open Knowledge Format (OKF) ontology at the raw data level and a Resolved Entity Matrix. 

This means the agent never guesses. The context is already with the agent:  the relationships between data elements, the business meaning, the governance rules. When a user asks a question, the agent generates a governed query based on the ontology, not on pattern matching or heuristics. 

2. Deterministic Code for Common Questions 

Not every question needs to touch an LLM. For the top 100 questions that users ask repeatedly, Axis can store deterministic code that fetches the answer directly. 

Here's how it works: 

This eliminates LLM hallucination entirely for common questions. Guessing and variance are eliminated from the equation. The same question always runs the same deterministic logic: Axis stores the code, not the data, so every time it's asked, it pulls fresh information straight from the source. For questions outside the cache, the grounded ontology will ensure accurate results. 

As the organization learns more, the cache can grow. Any question can be added to the deterministic set, progressively reducing reliance on LLMs and increasing speed and accuracy. 

Trust is built through architecture. AI Agent Studio is not a black box. Every chat line leaves a trace ID that flows into Axis Observability. Every agent response is stored and evaluated. Users see exactly how the agent generated each answer: the lineage, the query, the reasoning. This makes the agent white-box, not black-box. 

From Data Product to Business Impact 

Your data products are valuable, but their value only extends as far as the projected value of the decisions they enable. Without a way for business users to interact with them, they remain assets in a marketplace, not tools in the hands of decision-makers. 

Xebia Axis Agent Studio bridges that gap. It empowers business users to create, manage, and share AI agents with no technical expertise required. It grounds those agents in governed data and ontology to ensure accuracy. And it provides the transparency users need to trust the answers. 

The same agent technology that makes data debt urgent also makes it faster to resolve. Xebia Axis puts purpose-built agents alongside business users, with governance built into the platform itself. 

Take the next step towards an Agentic Enterprise where every business user is able to talk to their data. Discover more about Xebia Axis.