What is autonomous-ready data?

Just like you, lately I’ve been thinking a lot about agentic workflows—and what they truly demand from the foundational data infrastructure layer.

Yes, we all know that bad data leads to bad outcomes. However, that old mantra takes on a much more urgent meaning when you’re building systems where AI agents are reasoning, deciding, and acting independently.

What pushed me deeper into this was hearing firsthand from some of our most forward-looking customers—teams right on the cusp of AI acceleration. They’re not just piloting copilots; they’re designing closed-loop systems where agents parse documents, validate data, send updates, or trigger actions—all autonomously.

It’s not just about adding AI. It’s about re-architecting trust into the entire data stack.

From Dashboards to Dynamic Agents

In a recent conversation, a data leader put it bluntly:

We’re prioritizing AI projects where the agents don’t touch our proprietary data—not because that data isn’t valuable, but because we’re not ready yet. The governance, access, and quality just aren’t there.”

That hit home. Because this isn’t a lack of ambition—it’s an honest reflection of where most teams are today. AI isn’t held back by model performance—it’s held back by data readiness.

While we all know that enterprise data is among the most high-value, high-impact assets, there is a natural resistance when it comes to letting autonomous agents act on it. This further prompted me to dig deeper into the reasons behind it and to focus specifically on enterprise data use cases (also where our expertise lies). These are workflows and solutions built on a company’s existing, critical, and high-value data.

What Makes a Workflow “Agentic”?

Agentic workflows go beyond simple automation. Instead of executing predefined scripts, agents operate with context-awareness and decision-making autonomy. They perceive inputs (such as invoices or emails, ingested data), reason through them, and take actions across systems without requiring human oversight.

In practice, these workflows often look like:

• Reconciliation of transactions using invoices and ERP data
• Automated document parsing and record validation
• Personalized reporting and downstream updates &
• Natural language interactions with these workflows with non-technical SME’s

But with this autonomy comes the critical need for trust. How can we be sure that an agent acts safely when the data it relies on might be incomplete, outdated, or anomalous?

The Data Infrastructure Changes

Agentic systems require a revised data backbone, one designed not only for human-driven analytics but also for autonomous agents that reason, decide, and act in real-time.

Interoperability: Tools and Services as MCP Servers

For Agentic systems to truly scale, interoperability is critical. Tools and services that agents rely on, whether for data validation, access, enrichment, or downstream actions, need to be exposed as MCP (Model Context Protocol) servers. MCP enables agents to securely discover, invoke, and trust external services in real-time, transforming isolated tools into callable, verified building blocks within the Agentic ecosystem.

Streaming & Transformation: Apache Kafka, Apache Flink

For most enterprise use cases, upstream data is constantly transported, transformed, and fed into storage systems to power AI and BI workloads. Validating data at this layer for freshness, schema integrity, accuracy, and anomalies is critical to prevent inaccurate data from ever reaching data lakes or vector databases. This is why forward-thinking AI architects are increasingly embedding validation directly into streaming pipelines.

Data Lakes & Open Data Storage: Apache Iceberg, Delta Lake, Hudi

Often, data is pushed into object store data lakes, such as S3, GCS, or ADLS, and managed in open table formats. These open table formats enable scalable, versioned data lakes with support for time travel and auditability, also allowing organizations to decouple compute from storage in a composable way. Implementing validation and observability at this layer ensures agents operate on consistent, trusted snapshots of data, which is essential when actions depend on precise data correctness at specific points in time.

Vector Databases: pgvector, Pinecone, Weaviate

As agents work more with unstructured data (documents, text, images), vector databases enable semantic search and context-based understanding rather than simple exact matches. Crucially, data should be validated before vectorization. For example, when converting OCR-driven PDFs, critical data elements should first be checked for completeness and correctness to ensure agents reason on trusted, accurate inputs. This is a common pattern in enterprise use cases.

APIs: ERP, CRM, operational systems

Beyond analysis, agents need to act — updating records, creating tasks, or sending alerts. Secure, well-governed APIs are the channels through which agents move from insights to direct enterprise actions.

Catalogs & Access Control: Unity Catalog , Atlan , Actian , Alation , Dataplex

For agents to safely access and manipulate data, they must know where it resides, who owns it, and what policies apply. Furthermore, each querying workflow must enforce access permissions at every step. Modern catalogs and governance frameworks ensure this controlled, compliant, and explainable data access.

Observability: Telmai for real-time ingestion validation

Agents cannot rely on stale or corrupted data. Real-time observability, which validates data quality as it enters the system, prevents failures and builds trust before any action is taken, transforming data quality from reactive patching to proactive assurance. Observability and data quality must be ensured before the Agentic workflows can access or read the data, and not after the data lands in the access layer, at the data lake, or before it enters the lake at event streams.

P.S.: I am not aware of any observability tools/DQ tools that support event streams for data accuracy other than Telmai; therefore, I have not included other tools here. I am happy to edit this.

Becoming Autonomous-Ready

From our work with customers at Telmai, we’re seeing a few common themes emerge:

1. Multi-modal triggers require low-latency, trustworthy inputs.
2. Validation must occur at the ingestion layer, such as Kafka, data lakes, or vector databases, before access by Agentic workflows.
3. Agents require unified, governed access across batch, stream, and vector layers.

In short, if AI is going to operate with less human oversight, your data quality posture cannot remain reactive.

Final Thoughts

I feel privileged to learn from customers who are truly on the frontier, shifting from static dashboards to dynamic agents. Their honesty about what works (and what doesn’t) continues to shape my thinking about scaling AI safely and meaningfully. There is still a lot to learn, but this is my humble attempt to share what I’ve observed so far.

At Telmai, we’re committed to enabling this shift: empowering teams to validate data at the exact moment it enters the system, regardless of where it resides or who or what will consume it.

We’ve entered the age of autonomy. And it all starts with data you can trust, before the agent even answers the question.

Want to learn how Telmai can accelerate your AI initiatives with reliable and trusted data? Click here to connect with our team for a personalized demo.

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