FeatureMulti-Agent AI: The Next Phase of Enterprise Automation
As enterprises transition from traditional AI applications to more sophisticated ecosystems, the need for multi-agent collaboration reflects a growing demand for scalable, resilient and autonomous systems.
These systems — composed of networks of specialized AI agents — go far beyond the capabilities of single-purpose tools, offering a framework for distributed intelligence, real-time coordination and scalable automation across business functions.
The 4 Types of Enterprise AI Agents
Unlike single-task AI tools that operate in isolation, multi-agent systems coordinate networks of AI agents — each with a defined role and objective — to collectively solve complex problems and automate end-to-end business processes. They are akin to a digital workforce of specialized bots that can plan, act, adapt and cooperate across departments, workflows or even geographies.
“Multi-agent AI systems represent the next evolution of enterprise AI — moving beyond single-purpose tools to coordinated networks of specialized agents that function like a digital workforce,” said Swami Chandrasekaran, global head of AI and data labs at KPMG US.
Each agent within these systems performs a specific role. Chandrasekaran classified them into four core categories:
- Taskers: Execute routine jobs like vendor compliance screening.
- Automators: Manage complex workflows such as month-end financial close.
- Collaborators: Work alongside human teams in areas like financial planning and analysis.
- Orchestrators: Coordinate everything across organizational boundaries, for example in multi-jurisdictional compliance oversight.
To keep this distributed architecture aligned, the system relies on a central planner to map goals to agents and an orchestration layer that coordinates tasks, resolves conflicts and ensures transparency. These systems, according to Chandrasekaran, stay contextually aware through enterprise knowledge corpus and maintain continuity via layered memory systems, taking action by leveraging tools including APIs and MCP servers.
Communication is facilitated by standardized protocols like A2A (agent-to-agent), which enable interoperability across platforms and vendors.
How AI Agents Are Built to Collaborate
Beatriz Sanz Sáiz, EY global AI sector leader, emphasized that the foundation of multi-agent collaboration includes three critical components: individual agents, communication protocols and a shared environment.
“Each agent is designed with specific capabilities and objectives, contributing to the overall system’s goals,” she explained. To function effectively, organizations must define roles clearly at the outset through detailed agent profiles and mapped responsibilities.
One of the emerging architectural strategies includes deploying what Sanz Sáiz calls an “agentic control tower” — a supervisory structure that monitors ethical guardrails, efficiency and performance across the system.
“Agents won’t perform with accuracy if they don’t have the right context,” she said. That context will be supplied by sector-specific, functional and synthetic data, which becomes central to any agentic platform.
However, with complexity comes risk, and to ensure safe and effective collaboration, companies must adopt rigorous validation processes.
Chandrasekaran recommended “purple team” testing — a methodology adapted from cybersecurity — as well as specialized evaluation procedures for multi-agent systems, including:
- Tool precision assessments
- Planner accuracy validation
- Chaos testing
- Edge case analysis
“Trust isn’t just built into the technology,” he said. “It’s systematically proven through deliberate oversight.” Explainability and auditability remain key concerns, especially as agents interact with each other and external systems.
Why Protocols and Oversight Are Key to Agent Success
Sanz Sáiz noted the importance of implementing standardized communication protocols and simulation environments like digital twins to enhance transparency.
“Organizations should prioritize the development of interpretable AI models,” she said, adding that audit trails and logging mechanisms are essential for tracking agent decisions and activities.
Conflict is another real possibility when agents pursue competing goals. Sanz Sáiz advocates for built-in conflict resolution mechanisms and adaptive learning. “We are actually observing that the development of adversarial agents within each application, which provide feedback and challenge outcomes, is becoming quite effective."
Decision-making hierarchies, negotiation frameworks and continuous monitoring all contribute to a more resilient multi-agent system.
From a business perspective, multi-agent collaboration is already delivering ROI across domains such as finance, compliance, IT support and supply chain management. From Chandrasekaran’s perspective, good examples include:
- Automated finance reconciliation
- Real-time compliance monitoring
- Coordinated global procurement
Sanz Sáiz added that agents can also optimize logistics, resource allocation, fraud detection and more.
The Future of Enterprise AI: Humans Managing Agents
Looking ahead, multi-agent ecosystems may redefine the structure of enterprise operations.
Chandrasekaran envisions a future in which humans will become “agent bosses,” explaining, "They’ll managing AI agents with distinct identities in HR systems, nurturing their capabilities and directing them toward strategic goals just as they would human team members."