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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.
NHIMG editorial — based on content published by ZioSec: Enhancing Adaptability in Agentic AI: Challenges and Solutions
Questions worth separating out
Q: How should security teams govern agentic AI systems that can change tool use at runtime?
A: Security teams should govern agentic AI as a runtime identity problem, not just a model deployment problem.
Q: Why do agentic AI systems create more risk than ordinary automation?
A: Agentic systems create more risk because they can choose actions, tools, and timing during execution rather than following a fixed script.
Q: What breaks when AI memory is reused across multiple tasks?
A: When memory is reused across tasks, stale context, sensitive data, and prior assumptions can carry into new decisions.
Practitioner guidance
- Map every agent tool boundary Document which tools the agent can call, what data each tool can see, and which calls produce side effects.
- Bound memory by task and retention class Separate transient task context from reusable long-term memory, and define what can persist after task completion.
- Test for plan drift under changing inputs Use adversarial and scenario-based testing to see whether the agent changes tool choice or sequence when the environment shifts.
What's in the full article
ZioSec's full article covers the operational detail this post intentionally leaves for the source:
- The study’s four adaptation paradigms and how each one changes agent training.
- The distinction between tool-adaptation and agent-adaptation in practical deployments.
- The role of memory, retrieval, and reinforcement signals in improving agent performance.
- The article’s framing of cybersecurity risks when agents depend on external tools.
👉 Read ZioSec's analysis of adaptability challenges in agentic AI →
Agentic AI adaptability: what it means for IAM and security teams?
Explore further
12/06/2026 6:39 am
Agentic adaptability exposes a runtime governance problem, not just a model-quality problem. The article shows that planning, tool use, and memory can all change the way an agent behaves once it is deployed in the wild. That means the security issue is not only whether the model is accurate, but whether the identity path remains governable when execution is non-deterministic. Practitioner conclusion: identity controls must account for behavioural drift, not only initial entitlement.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to the same report.
A question worth separating out:
Q: How do I decide whether an agent needs stricter controls on tools or memory?
A: Start with the failure mode that would cause the biggest governance breach. If the main risk is side effects or privilege expansion, tighten tool controls. If the main risk is data retention or cross-session contamination, tighten memory controls. In many deployments, both need separate boundaries and separate review points.
👉 Read our full editorial: Agentic AI adaptability exposes new governance gaps in enterprise controls
