Operational data: Giving AI agents the senses to succeed -

Presented by Splunk

Organizations across every industry are rushing to take advantage of agentic AI. The promise is compelling for digital resilience — the potential to move organizations from reactive to preemptive operations.

But there is a fundamental flaw in how most organizations are approaching this transformation.

We are building brains without senses

Walk into any boardroom discussing AI strategy, and you will hear endless debates about LLMs, reasoning engines, and GPU clusters. The conversation is dominated by the “brain” (which models to use) and the “body” (what infrastructure to run them on).

What is conspicuously absent? Any serious discussion about the senses — the operational data that AI agents need to perceive and navigate their environment.

This is not a minor oversight. It is a category error that will determine which organizations successfully deploy agentic AI and which ones create expensive, dangerous chaos.

Consider the self-driving car analogy. You could possess the world’s most sophisticated autonomous driving AI, but without LiDAR, cameras, radar, and real-time sensor feeds, that AI is worthless. Worse than worthless, it’s dangerous.

The same principle applies to enterprise agentic AI. An AI agent tasked with security incident response, infrastructure optimization, or customer service orchestration needs continuous, contextual, high-quality machine data to function. Without it, you are asking agents to make critical decisions while essentially blindfolded.

The three critical senses agents need

For agentic AI to operate successfully in enterprise environments, it requires three fundamental sensory capabilities:

1. Real-time operational awareness: Agents need continuous streams of telemetry, logs, events, and metrics across the entire technology stack. This isn’t batch processing; it is live data flowing from applications, infrastructure, security tools, and cloud platforms. When a security agent detects anomalous behavior, it needs to see what is happening right now, not what happened an hour ago

2. Contextual understanding: Raw data streams aren’t enough. Agents need the ability to correlate information across domains instantly. A spike in failed login attempts means nothing in isolation. But correlate it with a recent infrastructure change and unusual network traffic, and suddenly you have a confirmed security incident. This context separates signal from noise.

3. Historical memory: Effective agents understand patterns, baselines, and anomalies over time. They need access to historical data that provides context: What does normal look like? Has this happened before? This memory enables agents to distinguish between routine fluctuations and genuine issues requiring intervention

The hidden cost of data debt

Here is where things get uncomfortable for most organizations: The data infrastructure required for successful agentic AI has been on the “we should do that someday” list for years.

In traditional analytics, poor data quality results in slower insights. Frustrating, but not catastrophic. In agentic environments, however, these problems become immediately operational:

Inconsistent decisions: Agents oscillate between doing nothing and triggering unnecessary failovers because fragmented data sources contradict each other.
Stalled automation: Workflows break mid-stream because the agent lacks visibility into system dependencies or ownership.
Manual recovery: When things go wrong, teams spend days reconstructing events because there is no clear data lineage to explain the agent’s actions.

The velocity of agentic AI doesn’t hide these data problems; it exposes and amplifies them at machine speed. What used to be a quarterly data hygiene initiative is now an existential operational risk.

What winning organizations are building

The organizations that will dominate in the agentic era aren’t those deploying the most agents or using the fanciest models. They are the ones who recognized that agentic sensing infrastructure is the actual competitive differentiator.

These winners are investing in four critical capabilities, all of which are central to the Cisco Data Fabric:

1. Unified data at infinite scale and finite cost: Transforming disconnected monitoring tools into a unified operational data platform is imperative. To support real-time autonomous operations, organizations need data infrastructures that can efficiently scale to handle petabyte-level datasets. Crucially, this must be done cost-effectively through strategies like tiering, federation, and AI automation. True autonomous operations are only possible when unified data platforms deliver both high performance and economic sustainability.

2. Built-in context and correlation: Sophisticated organizations are moving beyond raw data collection to delivering data that arrives enriched with context. Relationships between systems, dependencies across services, and the business impact of technical components must be embedded in the data workflow. This ensures agents spend less time discovering context and more time acting on it.

3. Traceable lineage and governance: In a world where AI agents make consequential decisions, the ability to answer “why did the agent do that?” is mandatory. Organizations need complete data lineage showing exactly what information informed each decision. This isn’t just for debugging; it is essential for compliance, auditability, and building trust in autonomous systems.

4. Open, interoperable standards: Agents do not operate in single-vendor vacuums. They need to sense across platforms, cloud providers, and on-premises systems. This requires a commitment to open standards and API integrations. Organizations that lock themselves into proprietary data formats will find their agents operating with partial blindness.

The real competitive question

As we move deeper into 2026, the strategic question isn’t “How many AI agents can we deploy?”

It is: “Can our agents sense what is actually happening in our environment accurately, continuously, and with full context?”

If the answer is no, get ready for agentic chaos.

The good news is that this infrastructure isn’t just valuable for AI agents. It enhances human operations, traditional automation, and business intelligence immediately. The organizations that treat operational data as critical infrastructure will find that their AI agents work better autonomously, reliably, and at scale.

In 2026 and beyond, the competitive moat isn’t the sophistication of your AI models — it’s the operational data providing agents the insights to deliver the right outcome.

Cisco Data Fabric, powered by Splunk Platform, provides a unified data fabric architecture for the agentic AI era. Learn more about Cisco Data Fabric.

Mangesh Pimpalkhare is SVP and GM, Splunk Platform.

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