Topic starter
11/11/2026 6:05 pm
TL;DR: AI agents can overwhelm observability pipelines with telemetry that is captured but not governed, routed or retained usefully, Apica reports. Production teams then lose the ability to reconstruct agent decisions after incidents or compliance reviews.
Editorial analysis by NHI Mgmt Group, based on content published by Apica: “The AI Disruption Your Business Actually Needs to Worry About (And It’s Not Unemployment)”.
By the numbers:
- Production AI agents generate 10-100x more telemetry than POC workloads.
Key questions
Q: Why do observability pipelines fail for agentic workloads?
A: Observability pipelines fail for agentic workloads when traces, logs and metrics do not preserve the agent's decision context, tool calls and state transitions.
Q: What is the difference between logging and replayable telemetry?
A: Logging records events for later inspection, while replayable telemetry captures enough structured state to reconstruct and rerun a system's execution path.
A: Security teams should log the agent identity, the human or role that authorized action, the configuration and model version in effect, and the full access control path behind each decision.
Practitioner guidance
- Establish replayable agent audit trails Define the minimum trace set required to reconstruct agent decisions, then verify that those events remain searchable after normal retention, routing, and cost controls apply.
- Set retention rules by business value Classify telemetry by investigation value before it reaches storage, so critical agent traces are preserved while low-value noise can be reduced safely.
- Test incident reconstruction before production rollout Run a tabletop that asks teams to explain a real agent action from telemetry alone, including prompts, tool calls, and downstream state changes.
Bottom line: AI agents create a governance problem when their telemetry cannot be replayed, audited, or retained at the point where decisions matter.
Glossary terms: Agentic Workload, Telemetry pipeline, Audit Trail, Replayability
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11/11/2026 6:07 pm
Ghost telemetry is an AI governance failure, not just an observability defect. Once an agent can change state, trigger workflows, or influence customer outcomes, the organisation needs a replayable evidence chain, not simply more logs. The problem is that many monitoring programmes still treat telemetry as an engineering by-product rather than a governed record of machine action. Practitioners should frame this as evidence governance for agentic systems, not storage optimisation.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should teams do first when AI agents are already in production?
A: Teams should first inventory all agent identities, map the credentials they use, and verify that each one has a named sponsor and monitored workflow. That creates the minimum basis for containment, investigation, and accountability before broader policy changes are attempted.
👉 Read our full editorial: AI agents create telemetry that observability cannot govern