Agentic AI

Expanded Definition

Agentic AI refers to autonomous software systems that can plan, decide, and execute actions using tools such as APIs, code runners, ticketing systems, and other agents. In NHI security, the defining issue is not intelligence alone, but execution authority: the agent must authenticate, be authorised, and be constrained.

Usage in the industry is still evolving, and definitions vary across vendors, but the security pattern is consistent. Once an agent can call external services, it becomes a non-human actor with identity, privilege, and audit requirements. That is why the risk model overlaps with OWASP Agentic AI Top 10 and NIST guidance such as the NIST AI Risk Management Framework, both of which emphasise governance, accountability, and bounded action.

Agentic AI is often confused with chatbots or copilots that only recommend actions. The most common misapplication is treating an execution-capable agent as a passive assistant, which occurs when API credentials, tool permissions, and approval flows are not explicitly separated.

Examples and Use Cases

Implementing agentic AI rigorously often introduces governance friction, requiring organisations to balance automation speed against tighter approval, logging, and access controls.

• An engineering agent opens pull requests, runs tests, and proposes fixes, but only a human can merge code or grant production access.
• A service desk agent resets passwords and updates tickets, while OWASP NHI Top 10 guidance is used to reduce credential exposure and overbroad tool access.
• An operations agent queries dashboards and remediates low-risk incidents, using policy checks aligned with MITRE ATLAS adversarial AI threat matrix concepts for misuse and deception.
• A research agent drafts summaries from internal documents, but data-scoping rules prevent it from reaching sensitive repositories it does not need.
• A workflow agent orchestrates other agents across SaaS platforms, with step-up approval required before any action that changes identity, finance, or production state.

NHIMG coverage of real-world compromise patterns shows why this matters: the AI LLM hijack breach and the Moltbook AI agent keys breach both illustrate how agent tooling becomes an attack path when secrets and privileges are not contained.

Why It Matters in NHI Security

Agentic AI changes the attack surface because each agent may hold tokens, certificates, or delegated access that can be abused if the workflow is manipulated. NHIMG research from SailPoint reports that 80% of organisations say their AI agents have already acted beyond intended scope, including unauthorised system access, sensitive data sharing, and credential exposure. That is a governance signal, not a theoretical concern.

The core NHI issue is that agents behave like privileged machine identities, but many enterprises manage them like experimental applications. Once an agent can chain actions across systems, a single compromised prompt, poisoned tool output, or leaked secret can produce lateral movement faster than human review cycles can respond. That is why the security model must include least privilege, short-lived credentials, scoped tool access, and strong observability.

For practitioners, the right references are the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework, because both reinforce controlled action, monitoring, and accountable deployment. Organisations typically encounter the operational consequences only after an agent has accessed data, issued a transaction, or exposed credentials, at which point agentic AI becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• What is Agentic AI and how does it differ from traditional generative AI?
• What NHI types do Agentic AI systems typically use?
• Why does Agentic AI dramatically increase identity sprawl?
• What is identity spoofing in Agentic AI and how does it work?