Confluent, Inc., the data streaming pioneer, is offering new Confluent Intelligence capabilities that connect artificial intelligence (AI) agents and uncover more accurate, intelligent data analysis.
According to the company, 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, the company said.
“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.”
Confluent’s Streaming Agents connect 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 including BigQuery, Databricks, and Snowflake to generate insights, then trigger enterprise AI platforms like Salesforce and ServiceNow 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, the company said.
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 act 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, the company said.
A2A support in Streaming Agents is now available in open preview.
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.
For more information about this news, visit www.confluent.io.