Every month, we move one step closer to a world powered by multi-agent AI systems.

The promise of agentic AI to automate workflows and drive exponential productivity gains has been all the rage. In practice, building and managing AI agents requires a level of sophisticated orchestration, stringent security measures, and dedicated collaboration with expert partners.

Let’s dive in.

What is agentic AI?

In simple terms, agentic AI refers to dynamic AI systems that are designed to perform a set of autonomous steps or tasks, known as trajectories. Unlike conversational AI, agents work without continuous human intervention.

Technically, agents are an extension of a traditional retrieval-augmented generation (RAG) system that connects enterprise data to a generative AI model. Agentic AI aims to achieve a given goal by orchestrating a set of models equipped with multi-step tools that allows them to leverage multilingual and multimodal data sources in various formats and at scale. We’re talking about billions of documents from pdfs, emails, charts, images, and more.

That’s the scale for most global businesses. And to really provide enterprise-grade value, these agentic systems need the best combination of top-notch generative and retrieval models that together can:

Understand the intent of queries with multiple parts
Search a diverse set of sources and compare multiple documents
Analyze all types and formats of data, and reason autonomously and sequentially

Plus, to keep all that data secure, most businesses are choosing to go with private AI deployments, where they can ring-fence their data and tailor their agentic solutions.

Why is everyone so excited about it?

This is a two-parter.

First, the opportunity is tremendous, with expected productivity gains as high as 30% to 40% for some industries like financial services and manufacturing. Just imagine the upside of supporting your employees by reducing the organizational strain of manual and time-consuming workflows.

Second, with the growing availability of secure, private deployments and more robust agentic operations, regulated industries are better positioned to build tailored AI solutions. Many organizations in healthcare, energy, and public sector face rigorous data privacy measures that can now be addressed with the latest generation of agentic systems.

Where do you start?

Running AI agents requires enterprises to have certain resources in place, and it presents new risks that need to be mitigated by careful planning and guardrails.

For example, businesses may start by:

Choosing the right generative model, like Command R/R+, that is specifically trained for agentic operations.
Building an AI dream team that can address key challenges of AI deployments, such as interoperability, scalability, and cost-effectiveness.
Finding a trusted AI partner that can help with end-to-end technical support.

If you’re ready to get started, let our team answer your questions and start building tailored, secure AI agents on your own terms.

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