Actian Introduces Data Observability Agents for the Agentic AI Era
New Actian Data Observability capabilities validate data at ingestion for safer autonomous AI decisions
Actian, the data and AI division of HCLSoftware, launched Data Observability Agents and a new Model Context Protocol (MCP) server for Actian Data Observability, allowing only trusted data to reach AI models, reports, and automated workflows.
The Actian Data Observability Agents validate data continuously upon ingestion into the lakehouse, detecting anomalies, explaining root causes in plain language, and coordinating resolution without manual investigation. The MCP Server exposes these quality signals in real time, so AI agents can verify data is trustworthy before acting.
Rapid adoption of agentic AI is proving that advanced reasoning capabilities are useless without accurate data. In fact, McKinsey & Company found that 51% of enterprises using AI have encountered negative consequences and for nearly one-third, the cause was AI inaccuracy.
“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
Actian’s new suite of purpose-built Data Observability Agents—including Validation, Incident Diagnosis, Lineage, Data Insight, Orchestration, Routing, and Help—works together to manage data quality issues from detection to resolution across the full data quality lifecycle. This helps lower operational costs while enhancing confidence in reporting and automation at scale. Key capabilities include:
- Autonomous Detection and Remediation: Detect anomalies in real time, explain root causes in plain language, suggest validation rules, and coordinate resolution steps.
- Zero-Copy Lakehouse Integration: Validate data in place across Apache Iceberg, Delta Lake, and Hudi environments without requiring data movement.
- Decentralize Data Trust: Allow business and technical users to query data quality status, set up monitors, and understand issues using natural language.
“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.”
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The MCP Server: A Built-in Checkpoint for AI Workflows
The MCP Server for Actian Data Observability creates an intelligent gateway for data quality within AI-driven workflows. By embedding observability context directly into AI assistants, teams and agents can automatically validate data before taking action. Key capabilities include:
- Real-Time Quality Signals: Surfaces incidents, alerts, monitors, and validation results directly to AI assistants and agentic workflows without requiring context-switching.
- Enterprise-Ready Authentication: Securely query context via natural language or programmatic calls without custom API or webhook integrations.
- Write Operations: Allow agents to trigger resolution actions without switching tools so production-ready agentic systems can act on trusted data rather than assumptions.
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