Topic starter
19/08/2026 3:57 pm
TL;DR: AI agent observability only becomes security-grade when instrumentation is chosen for tamper resistance, not just richness, because kernel eBPF and cloud audit logs survive compromise while framework callbacks and self-reports do not, according to ARMO. That makes evidence quality, not telemetry volume, the decisive design choice for security teams.
Editorial analysis by NHI Mgmt Group, based on content published by ARMO: “Runtime Observability for AI Agents: What to Instrument and Why”.
Key questions
Q: What breaks when AI agent observability relies only on in-process telemetry?
A: You lose evidence quality as soon as the workload can influence the collector.
Q: Why do tamper-resistant signals matter for AI agents and incident response?
A: Because incident response depends on evidence, not just visibility.
Q: How should security teams decide between kernel eBPF and framework-level traces?
A: Use kernel or provider-side signals as the primary evidence layer, then add framework-level traces for semantic context.
Practitioner guidance
- Prioritise tamper-resistant telemetry first Deploy kernel-level eBPF and provider audit logs before expanding framework callbacks or SDK traces, so the security team has evidence that survives compromise.
- Classify every observability source by trust tier Inventory each signal source and label it as tamper-resistant, partially resistant, cooperative, or repudiable, then remove any control assumptions that depend on cooperative data alone.
- Correlate runtime AI-BOM with exercised identity Compare what the agent was granted with what it actually loaded, called, and authenticated to at runtime, especially where managed services or hidden dependencies are involved.
Bottom line: AI agent observability becomes security-grade only when the evidence source can survive the agent’s own attempts to suppress or falsify it.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
19/08/2026 4:35 pm
Tamper resistance is now a core observability requirement, not a niche feature: AI agent telemetry only becomes security-grade when the source can survive compromise. Cooperative signals still matter for debugging, but they cannot be the sole basis for incident response when the workload itself may be the threat. For practitioners, the control question is whether evidence can still exist after the agent decides to hide it.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: When does AI agent telemetry become a governance problem rather than an ops problem?
A: It becomes a governance problem when the agent can act on real identities, tools, or data and the telemetry cannot prove what it exercised. At that point, visibility determines accountability, blast radius analysis, and whether response teams can trust the record. Observability choices start shaping control effectiveness, not just debugging quality.
👉 Read our full editorial: Runtime observability for AI agents needs tamper-resistant signals