Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Straikerai: “What’s the Difference Between Generative AI and Agentic AI?”.
Key questions
Q: How should security teams govern AI agents that read untrusted text and can act on it?
A: Treat the agent as a privileged runtime with untrusted input, not as a chat interface.
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 an AI system has memory and tool access but no clear governance?
A: The organisation loses the ability to prove what the system was allowed to do, why it did it, and whether the action stayed inside policy.
Practitioner guidance
- Define the system’s action boundary List every action an agent can take, then separate read-only suggestions from write, delete, and transact capabilities.
- Scope delegated access to the minimum viable task Issue credentials and API permissions only for the specific workflow the agent must complete, and expire them when the task ends.
- Log decisions, tool calls, and state changes Capture which prompt, memory state, tool invocation, and downstream system action led to each outcome.
Bottom line: Generative AI and agentic AI create different security problems because one produces content while the other can execute actions through tools, memory, and orchestration.
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02/08/2026 1:23 pm
Agentic AI is the first AI pattern that makes governance, not prompting, the primary control plane. Once a system can decide, call tools, and continue across time, the security question changes from whether the output is safe to whether the action path is authorised. That shifts attention toward policy enforcement, delegated access, and traceability. The practical conclusion is that security teams should govern the action envelope, not just the model output.
A few things that frame the scale:
- Gartner predicts that AI systems will initiate 50% of all service requests by 2030, driven largely by agentic AI.
A question worth separating out:
Q: What is the difference between generative AI and agentic AI for access control?
A: Generative AI mainly needs controls around input, output, and data handling, while agentic AI needs controls around delegated authority, credential scope, and runtime authorisation. The second model behaves like an operational actor, so access control must be designed around actions and outcomes, not just prompts.
👉 Read our full editorial: Why generative AI and agentic AI create different security risks