Operational data: Giving AI agents the senses to succeed
Operations teams are sprinting to deploy agentic AI. The pitch is strong: move from reactive firefighting to preemptive, self-correcting systems. But most strategies share the same blind spot - we're building brains and bodies, without giving them senses.
Walk into any exec review and you'll hear plenty about model choices and GPU capacity. What's missing is the plan for the data that agents use to perceive reality. No senses, no judgment. In production, that turns from a nice-to-have into risk.
Think self-driving. The smartest driving system is useless - and dangerous - without live feeds from cameras, LiDAR, and radar. Enterprise agents are no different. If you expect an agent to handle incident response, tune infrastructure, or orchestrate customer workflows, it needs continuous, contextual, high-quality machine data. Otherwise you're asking it to make critical calls while blindfolded.
The three senses every enterprise agent needs
1) Real-time operational awareness
Agents need live telemetry from apps, infrastructure, networks, security tools, and cloud platforms - not hourly batches. If a security agent flags abnormal behavior, it must see what is happening this second, across layers, so actions match reality.
2) Context across domains
Raw events aren't enough. Agents need instant correlation: logs with metrics, configs with deploys, auth with network flows. A spike in failed logins alone is noise. Correlate it with a recent change and east-west traffic anomalies, and you've got a confirmed incident.
3) Historical memory
Agents need to know what "normal" looks like. Baselines, trends, and recurrence patterns turn random fluctuation into clear judgment. With memory, agents can separate harmless blips from issues that require intervention.
The hidden cost of data debt
Most orgs have deferred the data work required for agentic operations. In classic analytics, weak data slows insights. Annoying, but survivable. With autonomous workflows, poor data becomes immediate operational pain.
- Inconsistent decisions: Conflicting sources cause agents to oscillate between inaction and pointless failovers.
- Stalled automation: Playbooks break because dependencies and ownership aren't visible.
- Manual recovery: Teams burn days reconstructing timelines due to missing lineage and unclear inputs.
Agent speed doesn't hide data problems. It exposes them - at machine speed.
What winning teams are building
The differentiator isn't how many agents you deploy or which LLM you pick. It's the sensing infrastructure underneath - the data fabric that feeds agents accurate, timely, and contextual signal.
1) Unified data at scale and sustainable cost
Replace fragmented monitoring with a unified operational data platform. Petabyte-scale ingestion, smart tiering, federation, and AI-driven optimization keep costs in check while sustaining real-time performance. You can't run autonomous operations if your data platform can't keep up.
2) Built-in context and correlation
Enrich data as it flows: service maps, dependencies, ownership, business impact, and change context. The goal is simple - reduce time agents spend "figuring it out," and increase time spent doing the right thing.
3) Traceable lineage and governance
Every decision should be explainable: which signals, from where, at what time, transformed how. Lineage isn't just for debugging - it's mandatory for audit, compliance, and trust. If you can't answer "why did the agent do that?", you're not ready for production autonomy. For governance and compliance practices tailored to operational AI, see the AI Learning Path for Regulatory Affairs Specialists.
4) Open, interoperable standards
Agents must sense across clouds, vendors, and on-prem systems. Commit to open formats and APIs so you're not boxed in by proprietary pipelines. Standards like OpenTelemetry help you keep visibility consistent wherever your stack runs.
The operational question that matters in 2026
It's no longer "How many agents can we launch?" The question is: "Can our agents sense what's happening - accurately, continuously, with full context?" If the answer is no, expect chaotic behavior and fragile automation.
The upside: this data fabric pays off immediately for humans too - clearer incident timelines, faster triage, and stronger automation. Treat operational data like critical infrastructure, and your agents will act more reliably and at scale.
Cisco Data Fabric, powered by Splunk Platform, provides a unified data fabric architecture built for agentic AI.
Practical next steps for Ops leaders
- Inventory signal gaps across apps, infra, network, identity, and security. Close them before scaling agents.
- Adopt open telemetry standards and centralize correlation. Reduce custom glue code that breaks under change.
- Define retention tiers: hot for real-time decisions, warm for investigation, cold for trend analysis.
- Implement decision lineage now - inputs, transforms, outputs - so audits don't slow you down later.
For governance guidance, see the NIST AI Risk Management Framework. If you're upskilling your team for agentic operations, explore role-based programs like the AI Learning Path for CIOs or the AI Learning Path for Project Managers.
Your membership also unlocks:
