Notifications
Clear all
Topic starter
12/06/2026 9:50 pm
TL;DR: Agentic AI is the system-level architecture for autonomous decision-making, while AI agents are the actors inside it, and mixing them up distorts planning, procurement, and risk management, according to Keyfactor. The identity assumption that agents can be governed like static workloads starts to break once autonomous systems coordinate at speed and scale.
NHIMG editorial — based on content published by Keyfactor: Agentic AI vs AI Agents: What’s the Difference and Why It Matters
Questions worth separating out
Q: How should security teams govern AI agents that operate inside an agentic system?
A: Start by separating the system from the actor.
Q: Why do AI agents create different identity risks than traditional automation?
A: Traditional automation usually follows a fixed script, but an AI agent can decide which action to take, when to take it, and which tool to use.
Q: What do IAM teams get wrong when they treat agentic AI as just another application?
A: They often focus on application access and miss the fact that the agents themselves are the actors making decisions.
Practitioner guidance
- Define the governed unit before buying controls Separate the architecture question from the actor question in your programme design.
- Assign each agent a verifiable runtime identity Treat every AI agent as a non-human workload that needs cryptographic identity, explicit authorization boundaries, and traceable action logs.
- Review lifecycle controls against agent speed Test whether your recertification, offboarding, and access review processes can keep pace with agent creation, delegation, and retirement.
What's in the full article
Keyfactor's full blog covers the operational detail this post intentionally leaves for the source:
- How Keyfactor distinguishes agentic AI from AI agents in its own product and messaging context.
- Examples of cryptographic identity and prompt-signing capabilities discussed for AI agent trust.
- The vendor's explanation of how its zero-trust framing applies to autonomous AI deployments.
- Supporting product references for lifecycle management and attestation that are not detailed here.
👉 Read Keyfactor's analysis of agentic AI vs AI agents and identity risk →
Agentic AI vs AI agents: what changes for IAM and security teams?
Explore further
13/06/2026 12:11 am
Agentic AI and AI agents are not synonyms, and collapsing them weakens governance design. The article is right to separate the system from the actor, because control ownership changes depending on which layer you are discussing. At the system layer, teams need oversight of orchestration and decision paths; at the actor layer, they need identity, authorization, and auditability. Practitioners should stop using one term to cover both problems.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: How can organisations tell whether they need orchestration controls or identity controls first?
A: If the main problem is how decisions are coordinated across systems, start with orchestration controls. If the main problem is who or what is acting, start with identity controls. Most real deployments need both, but the first control should match the failure point you are actually trying to reduce.
👉 Read our full editorial: Agentic AI vs AI agents: why the distinction changes governance
