Actian, the data and AI division of HCLSoftware, has introduced Data Observability Agents and a new Model Context Protocol (MCP) server for Actian Data Observability. These innovations ensure only trusted, high-quality data reaches AI models, reports, and automated workflows by providing continuous validation, anomaly detection, root-cause explanation, and real-time quality signals.
Quick Intel
As agentic AI gains rapid adoption, its advanced reasoning capabilities depend entirely on the accuracy and trustworthiness of underlying data. Without proper safeguards, AI autonomy can lead to significant business risks. McKinsey & Company research reveals that 51% of enterprises using AI have experienced negative consequences, with nearly one-third attributing the issues to AI inaccuracy stemming from unreliable data.
Actian's new Data Observability Agents provide active defense for data integrity, managing the full lifecycle of data quality issues from detection through resolution.
"Enterprises are handing over more control to AI agents, but without a safety net, this autonomy quickly becomes a business liability," said Guillaume Bodet, chief product officer at Actian. "Our new Data Observability Agents close this trust gap by handling the full lifecycle from detection to resolution, helping to identify anomalies earlier, resolve them faster, and make them explainable to humans and AI models."
Actian Data Observability Agents: Active Defense for Data Integrity The suite includes specialized agents—Validation, Incident Diagnosis, Lineage, Data Insight, Orchestration, Routing, and Help—that collaborate to address data quality challenges. This approach reduces operational costs while building greater confidence in large-scale reporting and automation.
Capabilities include real-time autonomous detection of anomalies, plain-language root-cause explanations, suggested validation rules, and coordinated remediation steps. The agents integrate seamlessly with zero-copy lakehouse environments such as Apache Iceberg, Delta Lake, and Hudi, validating data directly without relocation. Business and technical users can query data quality status, configure monitors, and gain insights using natural language, decentralizing trust across teams.
"Data readiness, access, and quality are the biggest challenges that organizations face in scaling AI and agentic AI initiatives," wrote Jayesh Chaurasia, senior analyst at Forrester. "The ability to deliver trusted, high-quality, and context-rich data across diverse systems and pipelines is no longer optional for enterprises. Data quality is mission-critical in the race to operationalize generative and agentic AI."
The MCP Server: A Built-in Checkpoint for AI Workflows The Model Context Protocol (MCP) Server serves as an embedded checkpoint within AI-driven processes. It exposes observability context in real time, enabling AI assistants and agents to confirm data trustworthiness before proceeding.
Features include surfacing incidents, alerts, monitors, and validation results directly to avoid context-switching, enterprise-ready authentication for secure natural language or programmatic queries, and support for write operations that allow agents to initiate resolution actions seamlessly.
The Actian Data Observability Agents are now available in public preview, and the MCP server for Actian Data Observability is generally available. To learn more about the new capabilities, read the "Actian's Winter 2026 Product Launch Solves the Agentic Trust Problem and More" blog and visit https://www.actian.com/data-observability/.
About Actian
Actian empowers enterprises to confidently manage and govern data at scale. Actian data management and data intelligence solutions help streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments.
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