Support for the Agent2Agent protocol helps connect AI agents anywhere in real time so they can collaborate at enterprise scale
Multivariate Anomaly Detection takes anomaly detection to the next level, stopping problems before they start
Dubai, United Arab Emirates—April 8, 2026—Confluent, Inc. (Nasdaq: CFLT), the data streaming pioneer, today announced new Confluent Intelligence capabilities that connect artificial intelligence (AI) agents and uncover more accurate, intelligent data analysis. Confluent’s Streaming Agents use the Agent2Agent (A2A) protocol to trigger and coordinate external AI agents using real-time data streams, making it easier to connect AI systems across an enterprise. Multivariate Anomaly Detection looks at multiple metrics to automatically spot unusual patterns in data streams, helping teams prevent issues with greater accuracy—before they cause outages or downstream impacts. Together, these capabilities create intelligent context-aware AI systems that adapt as data, agents, and business conditions change.
“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.”
Build Collaborative Agent Ecosystems
Businesses are increasingly turning to AI agents to automate decisions and handle more complex work. According to the IDC FutureScape: Worldwide Future of Work 2026 Predictions, “By 2026, 40% of all G2000 job roles will involve working with AI agents, redefining long-held traditional entry-, mid-, and senior-level positions.” And even that’s likely a conservative estimate. But as agents spread across tools and systems, most operate in isolation. If agents can’t communicate with each other or share context across a business, insights get trapped in silos and decisions are fragmented.
Confluent’s Streaming Agents addresses this by connecting AI agents to real-time data with Anthropic’s Model Context Protocol (MCP) and to other agents with the A2A protocol. Together, they can continuously analyze information from agent frameworks such as LangChain, data platforms like BigQuery, Snowflake, and Databricks to generate insights, then trigger enterprise AI platforms like ServiceNow and Salesforce workflows to take immediate action—closing the gap between insight and execution. By connecting these systems, Confluent turns stream-level analysis into “insight to action” generating the real-time intelligence needed to quickly adapt as business needs change.
With A2A support in Streaming Agents, teams can:
- Build smarter, reusable AI agents: Feed existing agents and systems with fresh context from Confluent to asynchronously respond to events and take further actions.
- Unlock inter-agent communication and auditability: Capture every agent action in an immutable log for auditability and replayability. Leverage Apache Kafka® to orchestrate communication between agents and to reuse agent outputs across other agents and systems.
- Centralize orchestration and governance in one place: Streaming Agents acts as the orchestrator, and Confluent ensures governance, security, and end-to-end observability for all agent interactions.
Teams in all industries can use A2A support in Streaming Agents to drive higher revenue, to lower risk, and to save on costs. Streaming Agents can personalize offers in retail, reduce credit risk underwriting in financial services, automate care recommendations in healthcare, predict maintenance in manufacturing, and proactively remediate outages in telecommunications.
Act on Live Signals and Eliminate Blind Spots
Businesses generate more data than ever, yet they struggle to understand what’s important and what can be ignored. 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’s Multivariate Anomaly Detection, a new feature of the built-in Machine Learning (ML) Functions, analyzes related metrics together to reduce false positives and catch real issues faster. It allows teams to detect anomalies across multiple metrics while ignoring data outliers, ensuring higher accuracy for complex data monitoring. Teams can start using Multivariate Anomaly Detection immediately since they don’t need to build or update the model, which learns as data changes.
In addition, teams can:
- Understand a system’s healthy state: Traditional anomaly detection tools rely on averages, which can get thrown off by a single random spike in data. Confluent’s Multivariate Anomaly Detection uses ML that reacts and learns with teams’ real-time data to ignore one-off glitches and understand systems better.
- Recognize complex problems and patterns: Confluent’s Multivariate Anomaly Detection analyzes multiple metrics together as a unified group, such as looking at CPU, memory, and latency combined, instead of just one at a time, to find patterns. Now, teams can uncover complex issues that would otherwise be missed if they looked only at individual metrics.
- Act automatically: By constantly measuring how far new data points are from the “true normal,” data that drifts too far is instantly flagged as an anomaly.