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AI Agents

Agents are software empowered by large language models. They take on a task, and work on its execution using the tools and reasoning they have. On the 2026 AI roadmap, agents are the number one approach to improve and enhance your processes with AI. 

A simple agent could, for example, read incoming orders from email and record them in your production system. A full-blown procurement agent, on the other hand, could run a full purchasing process, from sending out RFPs to answering vendor questions and finally making a reasoned recommendation for vendor selection. Agents are appropriate for many things – the key is to ensure sufficient tools, quality of data, and a good framework for monitoring their work. 

AI agents - Where to start and how?

  • Spot the use cases

    Agents can take on any task whose business rules you can describe, and for which the necessary tools are reachable through APIs. Choose your use cases with care to ensure quality – not everything is worth automating at this time.

  • Design the user experience

    Observe and care for the human responsible for the process. Is the user running the agent, or are humans merely randomly observing an autonomous AI worker? Find a user experience that both ensures sufficient quality from the agents’ work as well as maximizes the value of the involved human. 

  • Consider the low code alternatives

    The agentic hype has largely focused on custom software agents. However, especially your first proof of concepts can be implemented using self-service development tools such as Microsoft’s Copilot Studio. By handing off some of the agentic responsibilities from IT to business experts, you can leverage the strengths of the agentic model much faster. 

  • Be mindful of quality

    As agents drive an increasingly large part of your business, the velocity will increase. It becomes increasingly crucial to monitor what is happening: Did the agents make the right decisions, and based on what information? Plan the observation tooling and processes well in advance of the agentic explosion. 

  • Set up AI Manage the agentic workforce

    It makes sense to have agents collaborate just like humans. This way, not every agent has to know everything. The emergent agentic workforce will gradually take over the manual efforts in your organization. You should, however, know what and how your agents are doing– just as your HR keeps track of your employees, their tasks and performance.

Remember: AI is not just agents.

The flexible reasoning of LLM agents can be augmented using classic machine learning, computer vision and other AI fundamentals. These tools enable agents to reach fields that require extreme domain-level knowledge and accuracy.

Read more about our AI vision

AI adoption is a broad, organization-wide change project. Its efficient advancement requires driving staff capability, IT maturity and strategic alignment, all at the same time.

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What should I know about AI agents?

  • Agentic readiness is built on data

    Data quality and availability is crucial for AI projects. Most agents need to reach both the enterprise data warehouse as well as the document repositories. Ensuring the readiness and quality of their content will pay good dividends in the near future.

  • User experience improvement is part of process evolution

    Agents and AI are also impacting the relationship between humans and computers. Being able to dictate, pass over long-running tasks, and having an AI interface that crosses traditional app borders are just a few examples of how agentic AI changes how we work. All this is also relevant for your front-line workers, where things like voice control can mean even more than they do at the office.

  • Deep vertical agents maximize productivity

    Every app comes with their own agent now, but true competitive advantage is born out of having agents that actually know your data, rules and the way your business works. Judicious customization allows you to bring in features such as classic machine learning, allowing your agents to reason about complex, even mathematical, problems with high reliability.  

  • Agents deal poorly with tacit knowledge

    The organizational “What” is often well organized into a data platform, but the crucial understanding of “Why” typically resides in document archives – and even there, often as outdated versions. Tacit knowledge about organizational and cultural rules does not help an agent, so you must support them with explicit documentation about your ways of working.

Our toolbox for building AI agents

Simple is beautiful and wins the game most of the time. Agentic solutions require careful planning and continuous maintenance to leverage the non-stop AI evolution. Fundamentals remain the key: Your data and processes must be well-built in order for the actual AI stage to make sense.  

We recommend and leverage the following tools (an incomplete and everchanging list, though):

  • Microsoft Foundry is a secure baseline for cloud-driven AI solutions. Sprouting thousands of models, quality testing frameworks, statistical control and security features, Foundry helps make the technical parts of AI more manageable.

  • Microsoft Fabric is the foundational data store for your corporate data. Accompanying services such as Fabric IQ allow you – or us – to structure the data in ontologies, bridging the gap between your data structures and business realities. This bridge shortcuts the agents’ route to the correct solutions.

  • The organizational process guidance often lives in Microsoft 365 or a similar information worker solution. We integrate with all of them as necessary, and typically bring out the information to services like Azure AI Search or Foundry IQ. This gives our agents the best background information across the business, improving quality and reliability. 

  • Microsoft Copilot Studio and Microsoft 365 Copilot are AI tools aimed at end users. However, they are also often useful in building the first team-level experiments for AI-driven process improvements. We often start with these, and extend them with APIs and pro-code data sources to unleash the full AI capabilities of the cloud. 

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