Latest from Confluent streamlines use of streaming for AI

Streamlining streaming for AI

Enterprises continue to invest in AI development, but also continue to struggle to build AI tools that can be trusted enough to move into production.

In response, many data management and analytics vendors -- including Databricks, MongoDB and Tableau, among others -- have recently introduced tools aimed at improving AI pipelines and the data they feed AI applications so that outputs are more accurate and the tools can be trusted in production.

These capabilities are a nice step forward for developers as they build and govern agentic applications. While they don't differentiate Confluent in the market, they do help it stay competitive.

Now Confluent is similarly adding new capabilities aimed at refining AI development with its strategy shaped by customer feedback, according to Sean Falconer, the vendor's head of AI.

"A big part of it came directly from customers," he said, noting that the biggest challenges customers face when building AI applications no longer relate to AI models, but instead relate to the accessibility and relevancy of the data informing AI applications. "We saw growing demand from teams trying to operationalize AI, especially around making real-time data easier to work with and easier to secure."

In particular, developing the fully managed MCP server and adding support for Agent Skills were motivated by interactions with users, Falconer continued.

"We saw strong adoption of our open source MCP server," he said. "Customers were already using it to manage and troubleshoot streaming infrastructure through AI tools, so the next logical step was giving them a fully managed experience that's easier to use in production."

Specific new capabilities in Confluent Intelligence and Confluent Cloud include the following:

• The fully managed MCP server and support for Agent Skills to manage streaming data for real-time AI tools.
• Automated PII detection and redaction in Flink SQL.
• Secure connectivity to Microsoft Azure-hosted services with support for Azure Private Link.
• An open source adapter that integrates Flink SQL on Confluent Cloud with DBT Labs so data engineers can easily build and manage streaming data pipelines using a familiar framework.
• Support for new AI models from Anthropic and Fireworks AI to build real-time AI applications.
• Support for vector search on Amazon DynamoDB to expand Confluent's ecosystem.

Collectively, Confluent's new capabilities, while they don't substantially differentiate Confluent from competing vendors providing AI development capabilities, keep Confluent competitive, according to Kevin Petrie, an analyst at BARC U.S.

"I do believe these capabilities are a nice step forward for developers as they build and govern agentic applications," he said. "While they don't differentiate Confluent in the market, they do help it stay competitive."

Perhaps the most critical of the new capabilities is automated PII detection and redaction, Petrie continued, noting that his firm's research shows that AI adopters prioritize data privacy above all other aspects of a responsible AI framework.

"Confluent's automated redaction of PII in Flink helps enforce privacy policies and satisfy regulatory requirements such as GDPR or CCPA while maintaining the real-time service levels that AI often needs," he said.

In addition, Petrie noted that support for Agent Skills -- which was originally developed by Anthropic and made open source in December 2025 -- could give Confluent a temporary advantage.

"Confluent has some early-mover advantage with its support of Agent Skills, which are fast becoming a must-have open format for providing AI applications with the context they need to deliver value," he said.

Like Petrie, Catanzano called out the value of automated PII detection and redaction.

"It solves the fundamental blocker that security teams face when deciding whether to allow data into AI pipelines," he said. "This single capability can unlock entire use cases in regulated industries like healthcare and financial services that were previously off-limits."

Collectively, the new features are logically constructed to help customers more effectively build and secure real-time AI tools, Catanzano continued. However, model monitoring and MLOps capabilities are not included and could help customers as they continue to invest in AI development.

"They've focused heavily on the data layer and security controls, but they haven't addressed model monitoring, drift detection, or other MLOps concerns that also plague production AI systems," Catanzano said. "[However], that may be intentional given their focus on being the streaming foundation rather than a complete AI platform."

Looking ahead

As Confluent plans future product development, continuing to add and enhance features that help enterprises move AI initiatives into production at scale remains a focus, according to Falconer.

"You'll continue to see us invest in areas like MCP, Agent Skills, agents and real-time context delivery so developers can more easily build AI applications and agents that stay connected to what's happening in the business right now," he said. "A lot of the industry is realizing that AI is only as useful as the quality and freshness of the context behind it."

Security and governance are also priorities, Falconer continued.

"Enterprises want to move faster with AI, but they also need confidence that sensitive data is protected and that these systems operate within the right controls and policies, so a big part of our focus is making secure, governed real-time data access a built-in part of the platform," he said.

Catanzano noted that from a competitive standpoint, Confluent provides a broader combination of streaming, governance and AI-native features than Kafka and some other competing platforms. To continue distinguishing itself from its peers, Catanzano suggested that Confluent add prebuilt capabilities such as industry-specific templates to further streamline real-time AI application development.

"They could differentiate further by creating industry-specific templates and prebuilt streaming pipelines for common AI use cases -- fraud detection, personalization, predictive maintenance -- that combine their governance, connectivity and agent capabilities into turnkey solutions that reduce time-to-value for new customers in regulated industries," he said.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.