Actian Builds a Safety Net for the Agentic AI Era

ROUND ROCK, TX – February 24, 2026 – As enterprises race to deploy a new generation of autonomous AI systems, Actian, the data and AI division of HCLSoftware, today announced a new suite of tools designed to act as a critical safety net, ensuring these powerful systems operate on a foundation of trusted data. The launch introduces Data Observability Agents and a Model Context Protocol (MCP) server, aiming to solve the growing trust deficit that threatens to derail the promise of so-called 'agentic AI'.

The move comes at a pivotal moment. While advanced AI that can reason and act independently holds immense potential, its reliance on vast datasets creates significant risk. A November 2025 report by McKinsey & Company starkly illustrated the problem, finding that 51% of enterprises using AI have already faced negative consequences, with nearly a third of those incidents stemming directly from 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, in the announcement. "Our new Data Observability Agents close this trust gap by handling the full lifecycle from detection to resolution."

The Growing Pains of Autonomous AI

The industry is rapidly moving beyond generative AI—which creates content—to agentic AI, which takes action. These autonomous agents are designed to set goals, plan complex tasks, and execute them with minimal human oversight, from optimizing supply chains to managing financial portfolios. Projections show a swift adoption curve, with a recent Deloitte report suggesting that 74% of companies plan to deploy agentic AI within the next two years.

However, this rush toward automation is shadowed by a significant "trust deficit." The same report found that 71% of organizations admit they cannot fully trust autonomous AI agents for critical enterprise use. The core of this distrust lies in the data. An AI agent is only as reliable as the information it uses to make decisions. Inaccurate, incomplete, or biased data can lead to flawed logic, costly errors, and potentially catastrophic business outcomes.

This challenge is what industry analysts see as the primary bottleneck to scaling AI initiatives. "Data readiness, access, and quality are the biggest challenges that organizations face in scaling AI and agentic AI initiatives," wrote Jayesh Chaurasia, a senior analyst at Forrester, in a January 2026 report on data quality solutions. "Data quality is mission-critical in the race to operationalize generative and agentic AI."

Actian's Two-Pronged Approach to Data Integrity

Actian's announcement details a two-pronged strategy to build that mission-critical trust directly into the data pipeline. The solution combines active data validation with a novel communication layer for AI models.

The first component is a new suite of Data Observability Agents. These specialized agents—including Validation, Incident Diagnosis, Lineage, and others—work in concert to continuously monitor data as it flows into an organization's data lakehouse. They are designed to autonomously detect anomalies, use plain language to explain the root cause of a data quality issue, and coordinate remediation steps, all without requiring manual investigation. A key feature is their ability to perform this validation in place using a "zero-copy" approach, meaning they can work directly with modern data storage formats like Apache Iceberg, Delta Lake, and Hudi without the costly and time-consuming process of moving data.

The second, and perhaps most innovative, component is the new Model Context Protocol (MCP) Server. This server acts as an intelligent gateway, creating a checkpoint for AI agents before they act. The MCP, an open standard introduced in late 2024, standardizes how AI models communicate with external tools and data sources. Actian's MCP Server exposes real-time data quality signals—such as alerts, validation results, and ongoing incidents—directly to AI workflows. This allows an autonomous agent to programmatically query the MCP server and ask, in essence, "Is this data safe to use for this decision?" before proceeding.

This built-in verification step is designed to prevent AI systems from acting on faulty assumptions, providing a crucial layer of governance for autonomous operations.

A Crowded Field and a Critical Differentiator

Actian is entering a dynamic and increasingly crowded data observability market. Companies like Monte Carlo, Bigeye, and Acceldata have established strong positions by providing platforms that help data teams monitor for "data downtime" and ensure the health of their data pipelines. These platforms have become essential tools for organizations struggling with the complexity of modern data stacks.

Where Actian aims to differentiate itself is in its specific focus on the decision-making loop of agentic AI. While many observability tools alert human teams to data quality problems, Actian's MCP Server is designed to communicate directly with the AI agents themselves. This creates a real-time, automated governance layer that is purpose-built for a world where machines, not just people, are consuming data to take action.

By enabling agents to not only query data quality status but also trigger resolution actions via the MCP server, Actian is positioning its platform as an active participant in the AI workflow, rather than just a passive monitor. This direct integration addresses the need for AI systems to be not only powerful but also self-aware of the integrity of the information they are built upon.

From Finance to Healthcare: Where Trusted AI is Non-Negotiable

The implications of this technology are most profound in high-stakes industries where the cost of an AI error is measured in more than just dollars. In financial services, agentic AI is being developed for automated fraud detection and algorithmic trading, where a decision based on flawed data could trigger massive losses or regulatory penalties. In healthcare, AI agents that monitor patient data or assist in diagnostics require an unimpeachable level of data accuracy to prevent life-threatening mistakes.

For these sectors, the ability for an AI system to automatically verify the trustworthiness of its data before acting is not a luxury but a fundamental requirement for safe deployment. By providing a mechanism to enforce data quality at the point of decision, Actian's new capabilities could help accelerate the adoption of autonomous systems in these and other regulated fields, such as manufacturing and supply chain logistics, where reliability is paramount.

The Actian Data Observability Agents are now available in public preview, while the MCP server is generally available. The launch represents a significant step in the evolution of data management, shifting the focus from simply cleaning data to building inherent, verifiable trust into the very fabric of next-generation AI systems.