Actian’s Winter 2026 Product Launch Solves the Agentic Trust Problem and More

The fundamental problem with enterprise AI isn’t the models or the compute. It’s that most organizations are trying to build AI on data infrastructure that was designed for humans, not autonomous systems.

AI agents need contextual data. AI models need consistent, trusted datasets. AI workflows need seamless integration across cloud and on-premises systems. And all of this needs to work with existing enterprise investments, security policies, and compliance requirements.

Most AI infrastructure vendors assume you’re starting fresh. Actian assumes you’re not.

Organizations have data platform investments to preserve. They have mission-critical applications to protect. They have security policies to maintain. They have real-time requirements to meet. And they have AI initiatives to enable.

Most vendors are solving pieces of this puzzle. With the Winter ‘26 Product Launch, Actian is solving the whole thing, without forcing organizations to choose between innovation and operational stability. 

Actian Data Observability

Actian Data Observability delivers a comprehensive AI-native approach to data quality with Data Observability Agents, an MCP server, improved platform interoperability, and unstructured data format support.

What’s new: Seven specialized AI agents (Validation, Incident Diagnosis, Lineage, Data Insight, Orchestration, Routing, and Help) that validate data at ingestion and coordinate resolution steps. The MCP-compliant server uniquely provides both read and write capabilities, allowing AI agents to not only query data quality status but also set up monitors and configure validation rules directly. Platform interoperability now includes native integrations with Microsoft OneLake and Hive Catalog. Expanded format support adds XML and PDF monitoring capabilities for unstructured data quality validation.

Problem solved: The complete AI agent data integration challenge. Instead of validating data after the fact, Data Observability Agents validate data continuously as it lands in your lakehouse. Before any query. Before any downstream use. Before any AI agent touches it. 

Most MCP implementations are read-only, forcing manual configuration for AI workflows. Actian’s write-capable MCP server lets AI agents autonomously manage data quality infrastructure. The platform integrations solve the data silo problem across modern lakehouse architectures, while XML/PDF support addresses the 80% of enterprise data that’s unstructured.

Why it matters: This is the first production-ready data observability platform designed specifically for autonomous AI operations. AI agents can not only consume quality signals but actively participate in data governance. Financial services can validate transaction data and PDF contract documents. Healthcare can monitor patient records and XML clinical documents. The platform interoperability means this works across Microsoft Fabric, Apache Iceberg, and Git-based data environments without vendor lock-in. 

Actian Data Intelligence Platform

The Data Intelligence Platform now includes native Microsoft Fabric integration and an AI-powered Chrome Extension.

What’s new: Direct integration with Microsoft Fabric environments, including OneLake and Fabric-managed data assets, plus a Chrome extension that provides data context directly in PowerBI, Tableau, and other BI tools, fundamentally changes how organizations discover and trust their data.

Problem solved: Organizations using Microsoft Fabric no longer need to duplicate metadata or change existing workflows to get visibility and governance across their data landscape. Business users can trust reports without constantly asking, “Can I trust this data? or “What does this metric mean?”

Why it matters: This eliminates the data discovery tax that slows down AI initiatives. When data scientists and business analysts can quickly find, understand, and trust data without switching tools, AI projects move from months to weeks. For enterprises heavily invested in Microsoft ecosystems, this preserves those investments while extending their value.

Actian Ingres 12.1

Ingres 12.1 delivers transparent encryption, real-time change data capture (CDC), integrated observability, and in-database analytics.

What’s new: Full transparent encryption for disk blocks and transaction logs with master-key architecture, TLS 1.3 for client-server communication, new Log Reader API enabling real-time CDC with an official Debezium connector for Kafka/Spark streaming, a new Actian Monitor with OpenTelemetry/Prometheus/Grafana integration to view more than 100 DBMS metrics, and in-database ML Inference (native TensorFlow support) to deploy predictive models directly within the X100 analytics engine and eliminate data movement.

Problem solved: The “system of record isolation” problem that prevents operational databases from participating in modern AI and analytics workflows. Organizations can now meet audit requirements and security baselines without application changes, stream operational data to AI pipelines without batch jobs or production load, and run analytics and AI models directly where data lives, eliminating architectural friction and data movement complexity.

Why it matters: This enables enterprises to preserve their rock-solid Ingres investments while participating fully in AI initiatives. Government payroll systems, transportation networks, and manufacturing control systems can now feed AI pipelines in real-time, meet modern security standards transparently, and provide operational visibility without destabilizing production. Instead of choosing between stability and innovation, enterprises get both, turning their most trusted systems into AI-ready infrastructure.

Actian Zen 16.10

Zen 16.10 introduces new Kafka connectors for real-time integration, Prometheus-based telemetry, SQLAlchemy dialect support for Python developers, and built-in SQL data masking.

What’s new: Kafka Connect-based integration brings Zen directly into existing Kafka environments for real-time streaming, a native Prometheus-compatible /metrics endpoint for cross-platform engine observability, SQLAlchemy dialect support that lets Python developers use Zen with familiar ORM workflows, and built-in SQL data masking that enforces column-level protection directly in the database.

Problem solved: Fragmented integration, monitoring, developer workflows, and data protection. Organizations can now stream validated data in real-time through Kafka, monitor engine health across platforms with Prometheus, build Python applications using familiar SQLAlchemy workflows, and enforce consistent data masking directly at the database layer.

Why it matters: This strengthens Zen’s ability to support modern integration, monitoring, development, and security requirements without adding architectural complexity. Teams can adopt real-time streaming, unified observability, Python-native workflows, and database-level data protection while continuing to run Zen as their trusted database engine.

HCL Informix® 15.0.1

HCL Informix 15.0.1 delivers Parallel Checkpoint capabilities, Async I/O for advanced format devices, and enhanced administration features designed for high-performance, mission-critical applications.

What’s new: Parallel checkpoint for improved uptime and recoverability, async I/O for high-capacity storage devices (>512k block size), enhanced database creation parameters, and improved archecker support for complex fragmentation schemes.

Problem solved: The performance and reliability bottlenecks that prevent mission-critical applications from supporting modern AI workloads. Large datasets can now be processed with optimal I/O performance while maintaining enterprise-grade reliability.

Why it matters: For organizations running business-critical applications on HCL Informix, these updates reduce downtime risk and keep performance predictable as workloads grow. Faster checkpoints, optimized I/O on 4K storage, and accelerated table restores help maintain uptime and speed recovery when it matters most.

The Future of Enterprise AI

Enterprise AI isn’t about having the newest models or the biggest compute clusters. It’s about having a data infrastructure that can reliably feed trusted data to AI systems at enterprise scale, across hybrid environments, while meeting security and compliance requirements.

Actian’s Winter 2026 portfolio delivers exactly that, not through rip-and-replace modernization, but through intelligent evolution of existing data infrastructure.

Actian Data Intelligence and Actian Data Observability will be showcased at the Gartner Data & Analytics Summit in Orlando, March 9-11. Actian Data Observability’s Data Observability Agents are launching in public preview. 

Informix® is a trademark of IBM Corporation in at least one jurisdiction and is used under license.

About Author

About Dee Radh

As Senior Director of Product Marketing, Dee Radh heads product marketing for Actian. Prior to that, she held senior PMM roles at Talend and Formstack. Dee has spent 100% of her career bringing technology products to market. Her expertise lies in developing strategic narratives and differentiated positioning for GTM effectiveness. In addition to a post-graduate diploma from the University of Toronto, Dee has obtained certifications from Pragmatic Institute, Product Marketing Alliance, and Reforge. Dee is based out of Toronto, Canada.