The conversation around AI agents has focused heavily on reasoning. Less attention has gone to coordination. Enterprises that have been experimenting with agents are struggling to manage agents that share context and operate across live business systems without stepping on each other. Running a single agent on streaming data is manageable. Running several agents across live business systems is more challenging.
Confluent began addressing this problem last October, when it introduced capabilities that allowed its customers to develop AI agents powered by streaming data. The data streaming platform has now taken it one step forward by rolling out new support for the open Agent2Agent protocol. This move shifts Confluent’s role from simply feeding agents data to helping orchestrate how they interact.
“If you want to be competitive, your AI can’t be looking in the rearview mirror,” said Sean Falconer, Head of AI at Confluent. “You need a system of AI agents that work together and constantly learn and share insights in real time. Confluent Intelligence connects teams’ AI investments and systems no matter where they’re built—so AI can automatically react to live data, take action, coordinate systems, and escalate to team members as needed.”
The Agent2Agent protocol is rolled out within what the company refers to as Confluent Intelligence. The aim is to enable AI agents to communicate and coordinate over real-time event streams. The update allows multiple agents to exchange signals, trigger one another, and operate across distributed enterprise systems using Confluent’s streaming infrastructure.
(Piotr Swat/Shutterstock)
The company also rolled out upgraded anomaly detection that works on live streaming data instead of looking back at historical batches, aiming to help agents focus on signals that actually matter.
“Businesses generate more data than ever, yet they struggle to understand what’s important and what can be ignored,” states Confluent in the press release. “Anomaly detection surfaces threats and opportunities that no human could spot on their own. Traditional anomaly detection often analyzes metrics in isolation and is frequently restricted to batch-based analysis on historical data. “
“Relying on simple statistical baselines, these systems are highly sensitive to noise, spikes, and bad data. Without context, they can generate false positives, and they typically surface issues after they’ve already impacted the system.”
Confluent shared findings from an IDC report that highlights that businesses are increasingly turning to AI agents to manage complex work and automate decisions. Citing the IDC FutureScape: Worldwide Future of Work 2026 Predictions, it shared, “By 2026, 40% of all G2000 job roles will involve working with AI agents, redefining long-held traditional entry-, mid-, and senior-level positions.”
According to Confluent, if the agents cannot share content or communicate with each other, then decisions are fragmented and insights can get trapped in silos. To bridge that gap, Confluent is positioning its streaming platform as the coordination layer for enterprise agents. Through A2A support and Confluent Intelligence, it enables agents to share real-time context, events, and decisions across systems.
(Yurchanka-Siarhei/Shutterstock)
When viewed through a competitive lens, this move by Confluent is less about adding A2A support and more about defending strategic ground. Some of its competitors are embedding AI directly into their data cloud and tightening the link between operational workloads and their lakehouses. Some of the hyperscalers are even offering managed agent frameworks tied to their own cloud ecosystem. In that setting, Confluent wants to play a bigger role that goes beyond the layer that just pipes data between systems.
Now by extending Confluent Intelligence to support agent coordination and anomaly filtering, the company is pushing its streaming platform further up the stack. The aim here is to ensure that as enterprises deploy multi-agent systems, the event backbone remains central to execution.
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Author: Ali Azhar
