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02/08/2026 12:23 pm
TL;DR: Generative AI produces responses, while agentic AI reasons, plans, and acts across time using tools, memory, and orchestration, according to Straikerai. That shift changes the security problem from prompt quality to governance over autonomous action, access, and auditability.
NHIMG editorial — based on content published by Straikerai: What’s the Difference Between Generative AI and Agentic AI?
By the numbers:
- 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%).
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should security teams govern AI agents that can take runtime response actions?
A: Treat them as privileged NHI workloads with explicit scope, short-lived authority, and full action logging.
Q: Why do generative and agentic AI create problems for traditional model risk management?
A: Traditional model risk management assumes stable inputs, stable outputs, and a bounded decision path that can be validated before deployment.
Q: What breaks when AI agents do not have persistent memory?
A: When AI agents do not have persistent memory, they cannot reliably retain corrections, risk cues, or task-specific constraints across sessions.
Practitioner guidance
- Define agent identity classes Create explicit categories for AI systems that only generate content, systems that can call tools, and systems that can execute multi-step actions.
- Separate memory from execution privileges Treat persistent memory as a sensitive control plane, not a convenience feature.
- Wrap every tool call in policy enforcement Place authorisation, step-up checks, and logging around each API, database, or workflow connector exposed to an agent.
What's in the full article
Straikerai's full blog covers the architectural detail this post intentionally leaves for the source:
- The article's step-by-step explanation of how generative systems differ from closed-loop agentic systems in practice
- The discussion of memory modules, orchestration layers, and unified access protocols such as MCP
- The source author's examples of how autonomous agents move from prompt-and-response into tool use and workflow execution
- The vendor's framing of trust, safety, and governance as the three pillars for enterprise agent adoption
👉 Read Straikerai's explanation of generative AI vs agentic AI →
Agentic AI vs generative AI: what changes for security teams?
Explore further
02/08/2026 1:23 pm
Agentic AI is becoming a new governed identity surface, not just a model category. Once a system can choose actions, use tools, and retain state, it stops behaving like a passive content generator and starts behaving like an access-bearing entity. That creates a governance problem that spans IAM, PAM, and AI security because the decision boundary is no longer human-only. Practitioners should treat agentic systems as identities with defined scope, not as clever interfaces.
A question worth separating out:
Q: What is the difference between machine identity and AI agent identity?
A: Machine identity is the broader technical identity used by software systems to authenticate and authorize access. AI agent identity is a subtype that also needs constraints on tool use, action scope, and rollback because the agent can make decisions and trigger downstream actions. Agent identity therefore needs tighter behavioural controls than a standard workload identity.
👉 Read our full editorial: Why generative AI and agentic AI create different security risks
