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20/08/2026 5:36 pm
TL;DR: AI agent tracing tools capture prompts and tool calls for debugging, but ARMO argues they still miss the authorization context, baseline reference, and downstream linkage security teams need to triage incidents correctly. The practical shift is from telemetry that explains model behaviour to records that support security-grade attribution, drift detection, and cross-layer investigation.
NHIMG editorial — based on content published by ARMO: Prompt and Tool Call Visibility: What Your AI Agents Are Actually Doing
By the numbers:
- 64% of valid secrets leaked in 2022 are still valid and exploitable today, proving that detection alone is not enough without automated revocation.
Questions worth separating out
Q: What breaks when authorization is only evaluated after an AI agent acts?
A: What breaks is prevention.
Q: Why do AI agents complicate existing IAM and PAM controls?
A: AI agents complicate IAM and PAM because they often inherit delegated credentials, operate across multiple systems, and keep acting after the initial approval moment has passed.
Q: How do security teams know if agent observability is actually working?
A: Observability is working only when teams can tie together token activity, tool calls, and latency for a specific agent session.
Practitioner guidance
- Define a security-grade agent record schema Require entity identity, intent context, authorization context, baseline context, and downstream linkage in every prompt or tool call record before it enters security workflows.
- Correlate application and runtime evidence Stitch SDK, framework, proxy, and eBPF records with a shared correlation ID so analysts can reconstruct a single execution path across layers.
- Separate debugging telemetry from security telemetry Keep ML observability tools for development, but add a security pipeline that preserves longer retention, scope metadata, and response-ready linkage.
What's in the full article
ARMO's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step capture-point examples for SDK, framework, proxy, and kernel instrumentation choices
- The five-field record schema in implementation terms, including how to populate entity identity and downstream linkage
- Practical guidance on correlation IDs, retention, and privacy handling for prompt content versus tool parameters
- A decision checklist for selecting a minimum viable instrumentation stack in production Kubernetes environments
👉 Read ARMO's analysis of prompt and tool call visibility for AI agents →
Prompt and tool call visibility: what security teams are missing?
Explore further
20/08/2026 6:22 pm
Security-grade visibility is now an identity problem, not just an observability problem. Once an agent can invoke tools, it is acting through delegated authority and must be treated like another identity-bearing entity in the stack. That means records need to express who or what acted, under which scope, and against which policy boundary. For IAM and PAM teams, the lesson is that telemetry without authorisation context cannot support governance.
A question worth separating out:
Q: What should teams do when AI agent monitoring spans SDKs, proxies, and eBPF?
A: They should treat the layers as complementary, not interchangeable, and use a shared correlation model to join them. SDKs provide application intent, proxies provide network context, and eBPF provides runtime evidence. The control objective is a reconstructable chain, not maximum raw logging volume.
👉 Read our full editorial: Prompt and tool call visibility is now a security control
