AI agents are breaking enterprise observability stacks built for human-scale query patterns

AI agents do not keep business hours. That straightforward fact is exposing a structural gap in how most enterprises monitor their infrastructure, according to Eric Tschetter, Chief Architect at Imply, who outlined the problem in a recent conversation with analyst Kevin Petrie on EM360Tech.

Traditional observability tools were built around one core assumption: workloads have peaks and troughs driven by human behavior. Traffic drops overnight. Queries cluster around business hours. Alerts and anomaly baselines are tuned accordingly. Agentic AI breaks that model completely.

A query pattern observability was never designed for

Where a human-operated application might generate thousands of queries during a business day and relatively few overnight, an AI agent runs continuously, probing data systems at consistent volume regardless of the hour. The monitoring stack never sees a quiet period it can use to establish a normal baseline. Thresholds that worked for human-scale traffic can either flood on-call teams with false positives or, worse, miss genuine performance degradation because the system never learns what "low load" looks like.

Tschetter's framing points to a gap that is less about raw compute capacity and more about observability logic. The underlying databases and infrastructure may handle the volume. The monitoring layer on top may not know what to make of it.

This is a problem that arrives before most teams expect it. An enterprise can run a successful AI agent pilot at limited scope, see clean observability data, then scale the deployment and find that the monitoring setup that looked adequate at small volume is now producing noise or silence at production scale.

Data architecture is under pressure from the same shift

The observability challenge is one expression of a broader architectural stress that agentic AI is placing on enterprise data infrastructure. The same EM360Tech programming has highlighted related questions: whether enterprise data architecture can support AI at scale, and whether policy-based data security frameworks hold up when autonomous agents, rather than human users, are initiating data access.

For infrastructure and IT operations leaders, these are not future concerns. Enterprises already running agentic AI in production, or preparing to, are finding that design decisions made for human-in-the-loop workflows need to be revisited across monitoring, access control, and data routing.

Imply's focus is real-time analytics databases capable of handling high-throughput, low-latency query loads. That context matters here: the observability problem Tschetter describes is partly a data platform problem. Streaming, always-on query traffic requires a monitoring backend that can itself process and analyze at the same pace.

ROI and production readiness remain unresolved for many teams

The observability gap sits within a wider pattern. Across enterprise technology circles in 2026, a recurring theme is the distance between AI investment and production outcomes. Many organizations have run pilots that never made it to sustained deployment. The infrastructure and tooling layer, including observability, is one of the concrete reasons that gap persists.

For ops and infrastructure teams being asked to support AI agent rollouts, the question is practical: does the current monitoring stack know what good looks like for a workload that never stops? If the answer is unclear, that is the evaluation that needs to happen before the next deployment phase.

What this means for your team

• Audit your observability baselines. Determine whether your current alerting thresholds and anomaly-detection models were calibrated on human-paced traffic patterns and, if so, what recalibration is needed for always-on agent workloads.
• Test at scale before you need it. Run agentic workload simulations at production volume in a staging environment to identify where your monitoring stack produces blind spots or alert fatigue.
• Review data platform suitability. Confirm that the database and query infrastructure supporting AI agents can expose the metrics your observability tools need, at the frequency those tools require, to be useful.
• Engage platform vendors now. Ask your observability and database vendors specifically what they support for agentic AI query patterns, and get that answer before a production incident forces the question.