Agentic AI's challenge is getting agents to act like a team, not a crowd
Adding more artificial intelligence agents to the workflow doesn't make an enterprise smarter. In fact, it can make operations harder to manage. The problem is not the capabilities of individual agents but how well they work together.
Key Takeaways
- SiliconANGLE UPDATED 12:06 EDT / JUNE 20 2026 AI Agentic AI's challenge is getting agents to act like a team, not a crowd GUEST COLUMN by Deepa Chauhan Adding more artificial intelligence agents to the workflow doesn't make an enterprise smarter.
Many enterprises are moving from experimenting with single AI agents to a multi-level approach that spans functions such as customer care, supply chain and finance.
- But modern enterprise operations are dynamic and interconnected.
Multi-agent systems had tremendous potential to adapt to changing conditions, but only when supported by a dedicated orchestration infrastructure.
- " It assigns tasks to the most suitable agent, manages communication between agents, balances workloads, and triggers human escalation when agents reach the limits of their authority or confidence.
Shared memory and context engine: Instead of each agent operating with its own narrow view, this layer maintains a unified, real-time source of truth by pulling data from enterprise operational systems that agents can query to create shared context before making decisions.
- How coordination infrastructure changes operations Most operational problems don't come from a lack of data but from teams having different versions of the truth.
Coordinated agents help close that gap by ensuring information moves quickly across the organization.
- Poor data quality: Data problems are bigger than most organizations admit.
Stats & Key Facts
- #Some large enterprises have reported reducing critical downtime by 30% to 40% after implementing this kind of coordinated system.
SiliconANGLE UPDATED 12:06 EDT / JUNE 20 2026 AI Agentic AI's challenge is getting agents to act like a team, not a crowd GUEST COLUMN by Deepa Chauhan Adding more artificial intelligence agents to the workflow doesn't make an enterprise smarter. In fact, it can make operations harder to manage. The problem is not the capabilities of individual agents but how well they work together.
Many enterprises are moving from experimenting with single AI agents to a multi-level approach that spans functions such as customer care, supply chain and finance. Each works in isolation, and coordinating their actions and ensuring they move toward a single goal is a challenge. The problem is no longer how to create AI agents, but how to ensure they work together rather than creating hurdles for each other.
Multi-agent systems had tremendous potential to adapt to changing conditions, but only when supported by a dedicated orchestration infrastructure. The coordination layer Coordination infrastructure serves as a central system that helps intelligent agents work as a team by distributing tasks, sharing information among agents and keeping everyone aligned toward the same goal. It relies on shared data stores and vector databases to improve orchestration.
For more details please read the original article at SiliconANGLE AI.
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