Topic starter
11/06/2026 12:04 am
TL;DR: Agentic AI systems that rely on planning, tool use, and memory often degrade outside controlled environments, and the source article highlights why unreliable tools, weak long-term planning, and poor generalization can undermine real-world performance according to ZioSec citing Arxiv. The governance gap is structural: current controls assume deterministic behaviour, but agentic systems adapt mid-session and can drift beyond the conditions IAM teams planned for.
Editorial analysis by NHI Mgmt Group, based on content published by ZioSec: “Enhancing Adaptability in Agentic AI: Challenges and Solutions”.
Key questions
Q: What breaks when agentic AI systems adapt outside controlled environments?
A: Static controls break first.
Q: Why do adaptive agents create more governance risk than ordinary automation?
A: Because they can change their next step based on feedback, context, and prior memory.
Q: How do security teams know if agentic AI controls are failing?
A: The main signs are session drift, repeated retry loops, unauthorized tool calls, and behaviour that diverges from the documented task sequence.
Practitioner guidance
- Map the full agent execution boundary Document every planning, tool, memory, retriever, and simulator component that can change what the agent does at runtime.
- Separate tool governance from output review Assess tool selection, tool results, and final answers as different control points instead of relying on one success metric.
- Treat memory as part of identity scope Include short-term context stores and long-term retrieval layers in access reviews, change control, and offboarding logic.
Bottom line: Agentic AI introduces a governance problem because runtime adaptation can change the actor's behaviour after access has been granted.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 6:39 am
Adaptive agent behaviour invalidates deterministic access assumptions: The article shows that agentic AI systems do not behave like static service accounts once planning, tool use, and memory all influence execution. That matters because enterprise controls often assume the actor will remain stable long enough for policy to be set, reviewed, and enforced. When the agent can re-plan mid-session, the assumption behind fixed access design no longer holds, and practitioners must rethink control timing as much as control scope.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: How should organisations govern memory and retrieval layers in agentic AI?
A: Treat them as part of the identity boundary and change-controlled runtime surface. If memory or retrieval can affect later decisions, they need inventory, ownership, review, and lifecycle controls just like other non-human identity dependencies that shape access and behaviour.
👉 Read our full editorial: Agentic AI adaptability exposes new governance gaps in enterprise controls
This post was modified 1 day ago by NHI Mgmt Group