Topic starter
20/08/2026 5:29 pm
TL;DR: AI agents operate through a five-stage loop of thought, action, execution, reflection, and alignment, and Fiddler argues that observability must capture that full lifecycle rather than only latency or error metrics. The governance implication is that agentic AI control depends on tracing intent, tool use, adaptation, and guardrails, not just monitoring outputs.
Editorial analysis by NHI Mgmt Group, based on content published by Fiddler: “Anatomy of an Agent: Observing the Full Lifecycle of AI Agents”.
By the numbers:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
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 agentic AI systems complicate identity governance more than traditional service accounts?
A: Traditional service accounts usually follow fixed workflows, while agentic systems can choose actions and sequence them at runtime.
Q: How do security teams know whether agent guardrails are working?
A: They know guardrails are working when denied tool calls are visible in logs, high-risk paths are blocked consistently, and the agent cannot override policy from inside its own session.
Practitioner guidance
- Implement lifecycle traces for every agent session Capture prompt inputs, memory retrieval, plan generation, tool selection, execution output, reflection, and alignment decisions as one traceable chain.
- Bind tool calls to authorisation context Record which policy, workflow rule, or human approval authorised each external action so agent activity can be audited after the fact.
- Instrument guardrail enforcement points Log escalations, reroutes, denials, and fallback invocations as control events so teams can prove when alignment logic actually intervened.
Bottom line: Agentic observability has to capture how AI agents decide and adapt, not just how fast they run or whether they error.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
20/08/2026 6:17 pm
Lifecycle visibility is the right control boundary for agentic AI. The article makes the case that telemetry stops at symptoms, while agent governance starts at intent and decision-making. That distinction matters because the security problem is not only whether an agent succeeded, but whether its reasoning, tool choice, and adaptation stayed inside policy. Practitioners should treat lifecycle visibility as the operational boundary for agent oversight.
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: What is the difference between runtime semantic tracing and ordinary observability for AI agents?
A: Ordinary observability tells you whether a system is healthy, fast, or error-prone. Runtime semantic tracing tells you how an agent reasoned, what it believed, which tools it used, and how those decisions evolved. For agentic AI, that distinction matters because governance depends on understanding behaviour, not just infrastructure status.
👉 Read our full editorial: Agentic observability needs lifecycle visibility, not just telemetry