More than an Agent – The Value of the Multiagent System in Enterprise Agentic AI

The Difference Between AI Agents and Agentic AI 

AI Agents are software applications powered by generative AI that can understand goals, plan actions, use tools, and execute tasks—often with a degree of autonomy. They’re a leap beyond simple chatbots or assistants, capable of dynamically determining their own workflows and adapting in real time. 

However, Agentic AI goes further. It’s not about a single agent, but an ecosystem of multiple agents—each specialized, each sharing information and coordinating to achieve complex objectives. In this system, agents can discover one another, delegate tasks, and collaborate, much like a team of human experts 

The key differences between an AI Agent and Agentic AI are: 

How Enterprise Agentic AI Will Add New Value 

Enterprise Agentic AI is more than the sum of its parts. By orchestrating a network of specialized agents, organizations can: 

• Tackle complex, cross-functional workflows: For example, resolving a customer issue may require agents for verification, policy lookup, booking, and escalation—each agent handling its domain, but working together seamlessly. 

• Increase accuracy and reliability: Narrow, specialized agents are easier to design, control, and monitor, reducing the risk of errors and “hallucinations” common in large, general-purpose models. 

• Scale automation safely: With proper observability and governance, enterprises can deploy hundreds or thousands of agents, each with clear boundaries and traceability. 

• Drive innovation: Multiagent systems can discover new solutions by combining expertise, surfacing insights that no single agent could achieve alone. 

Why not just one “super agent”? 

There will be no “universal AI agent” in the foreseeable future. Specialization leads to higher accuracy, easier monitoring, and better business outcomes. 

Business Use Cases of Enterprise Agentic AI 

Multiagent systems are already delivering value across industries. Realistically we are only beginning to uncover all of the use cases that an agentic AI can deliver for businesses, but here are some practical examples we have seen that can drive immense value: 

• Customer Service: a team of agents work together – one to verify identity, one to check policies, and another to manage bookings or complete the ask or intention of the customer. 

• Manufacturing: agents coordinate to resolve supply chain issues, automate troubleshooting, and optimize inventory. 

• Legal: Agents draft contracts, summarize legislation, and flag compliance risks 

• R&D: Agents team up to surface IP trends, reuse assets, and ultimately accelerate product design. 

• Life Sciences: Agent teams work together to accelerate new drug delivery by performing deep research, analyze vast data sources, and ensure audit and compliance readiness. 

Case Study: 

Cummins, a leading manufacturer used Sinequa by Chapsvision to deploy a multiagent system to unify access to PLM, CAD, and support systems. This resulted in a 30% reduction in time-to-insight and faster resolution of engineering issues. The company has seen enormous cost savings as a result. 

The Key to Unlock Agentic in the Enterprise: RAG 

The true power of Agentic AI is only realized when agents are grounded in the right knowledge. This is where Retrieval-Augmented Generation (RAG) comes in. 

 What is RAG

RAG enables agents to dynamically retrieve up-to-date, relevant information from across enterprise systems—SharePoint, CRMs, databases, and more—before generating a response. This ensures outputs are accurate, current, and contextually relevant. 

 Why is RAG Essential?

RAG provides agents with access to all the knowledge and data that only exists within the organization. The benefits are numerous, but some important ones are: 

• Prevents hallucinations: Agents cite real sources, not just “best guesses.” 

• Ensures security and compliance: Only approved, traceable content is used. 

• Scales across data types: Text, images, structured data, and more. 

• Supports explainability: Every answer can be traced back to its source. 

“Feeding the generative AI with the right information represents 80% of the effort, the balance being devoted to the configuration of the LLM and prompt engineering.”  – Pierre Jalais, Solution Architect, Total Energies 

Sinequa Unlocks Enterprise Agentic AI 

Agentic AI is not just about smarter agents—it’s about building a collaborative, trustworthy, and scalable system that can transform how enterprises operate. The journey from single agents to multiagent ecosystems is complex, but the rewards are immense: faster innovation, better decisions, and a true competitive edge. 

How Sinequa by ChapsVision Helps: 

• Unified, secure search platform: Sinequa connects to all your enterprise knowledge, enabling robust, explainable, and governed retrieval for agents. 

• Enterprise-grade RAG: Our platform blends all five retrieval types, ensuring agents always have the right, trusted information. 

• Observability and governance: Full visibility into agent actions, with controls to protect sensitive data and manage costs. 

• Scalable deployment: Move from pilot to enterprise-wide adoption, supporting every function from R&D to compliance. 

Ready to unlock the value of multiagent systems in your enterprise? Book a demo to learn more today.