Reimagining the Data Observability Market With Context and Agents

Industry analysts have become increasingly aligned on a core insight: AI initiatives struggle to scale not because of model limitations, but because enterprises lack trusted, contextual data foundations. Research from firms such as Gartner and Forrester consistently points to metadata quality, lineage, and governance as prerequisites for trustworthy and explainable AI, especially as organizations move beyond pilots toward more autonomous systems.

That challenge of ensuring reliable data for AI is particularly visible in data observability. While the market has made real progress in detecting issues in data pipelines and datasets, most platforms still depend heavily on human interpretation to explain why problems occur and what they mean to the business. As vendors begin introducing MCP-style connectivity and early agent concepts, the industry is clearly moving in the right direction – but unevenly, and with significant variation in depth and maturity.

This is the context in which Actian’s winter release, which included MCP Server and Data Observability Agents for Data Observability, should be understood: not as isolated features, but as complementary capabilities designed to introduce context and reasoning into data observability workflows.

The Market Today: Useful, but Reactive

Today’s data observability solutions are largely built around monitoring data freshness, dataset volumes, schema drift, and statistical anomalies in data pipelines – a necessary foundation, but no longer sufficient on their own. Many platforms apply machine learning to reduce alert noise, and some are adding conversational interfaces or copilots to help users interrogate incidents.

Yet three structural limitations persist across the category:

• Context remains fragmented. Observability tools detect data reliability signals, but business definitions, lineage, and governance metadata typically live elsewhere.
• Root cause analysis is still manual. Alerts initiate investigation, not resolution.
• AI remains assistive rather than autonomous. Copilots summarize issues, but rarely reason across pipelines or take action.

The result is a reactive operating model that becomes increasingly difficult to sustain as data ecosystems grow, and AI adoption accelerates.

MCP: A Shared Language for Observability Context

It’s important to acknowledge that MCP is beginning to surface across the data observability market, with a growing number of vendors experimenting with MCP-style integrations. That said, while a few vendors offer MCP capabilities for data observability, most offerings still rely on traditional APIs or webhook-based approaches that require custom development to connect with AI assistants or agentic frameworks. Even where MCP is present, implementations are typically read-only, exposing incidents, anomalies, and monitor status, so AI can help humans investigate issues more efficiently.

Where approaches differ is in how MCP is applied.

As MCP adoption emerges in data observability, most vendors use it as a read-only interface, exposing incidents, anomalies, and monitor status so AI assistants can help humans investigate problems more efficiently. Actian’s Data Observability MCP is designed differently: by enabling controlled write capabilities, it allows AI agents to move beyond analysis and actively participate in reliability workflows, automating actions rather than merely summarizing issues.

Metadata gives AI agents and LLMs the business context – definitions, lineage, and governance – that transforms them from eloquent guessers into trusted advisors.

Agents: Extending Data Observability

Actian’s Observability Agents build naturally on this foundation. Rather than replacing existing data observability capabilities, they extend them.

Where today’s tools primarily detect and notify, agents are designed to reason across data bservability signals, correlate issues across pipelines, and explain the impact in business terms. Over time, they can also support corrective actions, reducing reliance on manual intervention.

This is not an all-or-nothing shift. The agents introduce autonomy progressively, aligned with how enterprises adopt automation in practice.

Why This Matters

For CDOs, data platform leaders, and AI teams, the implications are clear:

• Data observability without shared context struggles to support agentic AI safely.
• Agents without a governed data context introduce as much risk as value.
• Incremental autonomy grounded in a trusted data context scales better than bold rewrites.

Actian’s approach reflects these realities. MCP establishes a common language for trust. Agents introduce reasoning and autonomy. Broader platform alignment compounds value over time.

Closing Perspective

Many vendors are eager to label their offerings “AI-powered observability.” Analysts, meanwhile, continue to emphasize that trust, context, and explainability are the real constraints on AI success.

By grounding data observability in shared context and extending it with agents that can reason – not just alert – Actian is charting a pragmatic path toward more autonomous, trustworthy data operations.

For organizations serious about scaling AI responsibly, that distinction is not theoretical. It’s strategic.

Check out this video that shows our Data Observability Agents in action!

About Author

About Phil Ostroff

Phil Ostroff is Director of Competitive Intelligence at Actian, leveraging 30+ years of experience across automotive, healthcare, IT security, and more. Phil identifies market gaps to ensure Actian's data solutions meet real-world business demands, even in niche scenarios. He has led cross-industry initiatives that streamlined data strategies for diverse enterprises. Phil's Actian blog contributions offer insights into competitive trends, customer pain points, and product roadmaps. Check out his articles to stay informed on market dynamics.