Agentic AI adaptability exposes new governance gaps in enterprise controls

At a glance

What this is: This is an analysis of why agentic AI systems struggle to adapt reliably in real-world environments, with the key finding that planning, tool use, and memory all create failure modes when conditions shift.

Why it matters: It matters because IAM, PAM, and lifecycle controls are increasingly being asked to govern autonomous runtime behaviour that can change faster than conventional review, approval, and monitoring cycles.

👉 Read ZioSec's analysis of adaptability challenges in agentic AI

Context

Agentic AI combines planning, tool use, and memory so a system can choose actions and work with external services during runtime. The governance problem is that these systems do not behave like static applications, and their identity and access patterns can shift while a task is still in progress.

For IAM and NHI programmes, that creates a control mismatch. Existing access models are usually built for known services, known scopes, and predictable execution paths, while agentic systems can re-plan, call different tools, and carry context forward in ways that make runtime oversight harder than provisioning-time policy.

The article frames this as an adaptability problem, but the identity issue is deeper: if the actor can change how it executes, then access, authorisation, and monitoring all need to account for behavioural variation, not just assigned entitlements.

Key questions

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. That means defining tool boundaries, separating read and write authority, and monitoring whether the agent changes its action path after new feedback. If execution can drift, the control model has to follow the drift, not just the original prompt.

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. That runtime flexibility makes authorisation harder to predict and easier to overshoot. The real issue is not automation alone, but the possibility of changing authority within the same task.

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. That creates persistence risk because a later action may be influenced by information that should have expired. Teams need to know what survives between sessions and whether that persistence is appropriate for the actor’s role.

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.

Technical breakdown

Planning modules and runtime decision drift

Planning modules break a goal into action steps using methods such as Chain-of-Thought, Tree-of-Thought, ReAct, and Reflexion. The important point is not the naming of the method but the fact that planning can change as new feedback arrives. That makes the system less like a scripted workflow and more like a runtime decision-maker that can revise tool choice, order of operations, and intermediate objectives while still pursuing the same high-level task. In identity terms, that creates a moving target for policy enforcement because the actor’s effective behaviour can diverge from the originally scoped request.

Practical implication: Treat plan drift as a governance signal and monitor for task re-framing that expands tool use beyond the original approval scope.

Tool use modules and external dependency risk

Tool use modules connect the agent to search engines, APIs, code execution environments, and browser automation. These integrations are where adaptability becomes an access issue, because each tool call is a trust decision that may expose data, trigger side effects, or cross a privilege boundary. The article’s core point is that tool reliability is not only a performance concern. It is also an identity concern because the agent’s access path is only as safe as the external action it can invoke at runtime. When tools behave inconsistently, the agent can also mis-handle context or chain incorrect actions.

Practical implication: Inventory every tool reachable by the agent and separate read-only, write, and execution-capable paths under distinct controls.

Memory persistence and long-horizon behaviour

Memory modules store short-term context and long-term knowledge so the agent can recall prior interactions and improve over time. That helps continuity, but it also introduces persistence risks because remembered context can bias future actions, carry forward stale assumptions, or preserve sensitive data longer than intended. In governance terms, memory turns a single task into a potentially multi-session identity event. The article’s adaptability frame therefore intersects with data retention, context scoping, and traceability, especially when memory is shared across tasks or reused in new environments.

Practical implication: Apply explicit retention boundaries to agent memory and audit what context survives from one task to the next.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• AI LLM hijack breach — attackers used stolen AWS access keys to hijack Anthropic LLM models on Bedrock.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

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.

Adaptive tool chains create an identity blast radius that is harder to predict than static application access. When an agent can choose among APIs, search, code execution, and browser tooling, the effective risk depends on which tool is selected in context and what action follows. That is the same reason NHI programmes struggle when access is broad but execution is runtime-driven. Practitioner conclusion: tool reachability and action reachability must be separated in the control model.

Long-term memory turns agentic access into a persistence issue as well as an authorisation issue. Stored context can carry sensitive data, stale assumptions, or prior task state into later decisions. In practice, this blurs the line between temporary task execution and durable identity behaviour, which makes conventional session assumptions weaker. Practitioner conclusion: governance has to treat memory as part of the security boundary, not as a passive feature.

Agentic AI adaptability should be evaluated through the same lens as NHI lifecycle governance. The field already knows that identity risk increases when scope, context, and duration are allowed to expand without review. Agentic systems intensify that pattern because they can revise actions mid-session and reuse context across tasks. Practitioner conclusion: the control question is not whether the system is clever, but whether the lifecycle of its access is still intelligible.

OWASP NHI Top 10 and agentic AI guidance are converging on the same structural problem: runtime behaviour is now the attack surface. The article’s framework aligns with the broader shift toward identity-centric governance for non-human actors. That matters because access decisions based only on provisioning-time intent will miss the behaviours that emerge once the agent starts adapting. Practitioner conclusion: teams should manage the actor’s runtime behaviour, not just its credentials.

From our research:

• 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.
• For broader agentic AI governance context, see OWASP NHI Top 10 for the runtime risks that emerge when identity, tools, and autonomy intersect.

What this signals

Runtime adaptability should now be treated as an identity governance signal. When an agent can revise its tool path after feedback, the question is no longer whether access was provisioned correctly, but whether the programme can detect and constrain behaviour once the session starts. That is a materially different control problem from traditional service-account management, and it should change how teams review logs, approvals, and exceptions.

Agentic memory is becoming an overlooked security boundary. If context persists across sessions, then retention, reuse, and retrieval all become part of the attack surface. Teams should prepare for governance questions about what state is allowed to survive, who can inspect it, and how much of the agent’s future behaviour is being shaped by prior task data.

The more agentic systems are deployed into operational workflows, the more IAM teams will need to measure behaviour rather than entitlement alone. A system can be provisioned correctly and still fail at runtime if the combination of planning, tools, and memory produces actions the business did not intend.

For practitioners

• Map every agent tool boundary Document which tools the agent can call, what data each tool can see, and which calls produce side effects. Split read, write, and execution paths so the agent’s effective authority is visible before a runtime decision expands it.
• Bound memory by task and retention class Separate transient task context from reusable long-term memory, and define what can persist after task completion. Review whether sensitive data or stale instructions are being carried into later sessions.
• 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. Focus on cases where a valid first step turns into an unsafe second step.
• Separate approval scope from execution scope Require explicit boundaries for what the agent may decide versus what it may execute. A system may be allowed to plan broadly, but execution rights should remain tightly segmented to the minimum callable surface.

Key takeaways

• Agentic AI adaptability is an identity governance issue because runtime behaviour can change even when provisioning is correct.
• The scale of the problem is already visible, with 80% of organisations reporting agent behaviour beyond intended scope.
• Teams need controls that bound tool reach, memory persistence, and execution scope, not just the initial access grant.

Key terms

• Agentic AI: Agentic AI is software that can decide and act at runtime using tools, memory, and feedback rather than only returning a static output. In identity terms, it behaves like a non-human actor whose access path can change during execution, which makes governance depend on observed behaviour as much as assigned entitlements.
• Tool Use Module: A tool use module is the part of an agentic system that connects the agent to external systems such as APIs, search engines, code execution, or browser automation. It is where identity risk becomes operational because the agent can cross trust boundaries and trigger side effects beyond the prompt itself.
• Agent Memory: Agent memory is the stored context an agent uses to remember prior interactions, facts, and task state. It can improve continuity, but it also creates persistence risk if sensitive data, stale assumptions, or obsolete instructions survive longer than intended and influence future decisions.
• Runtime Governance: Runtime governance is the practice of controlling and monitoring what a non-human actor does while it is executing, not only what it was allowed to do at provisioning time. For agentic AI, that means tracking decision paths, tool use, and memory reuse as part of the security boundary.

Deepen your knowledge

Agentic AI adaptability and runtime governance are covered in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building controls for agents that can change behaviour mid-session, it is worth exploring.

This post draws on content published by ZioSec: Enhancing Adaptability in Agentic AI: Challenges and Solutions. Read the original.