Topic starter
18/08/2026 2:46 pm
TL;DR: AI agents handle single tasks while agentic AI orchestrates multi-step workflows across multiple agents, and the difference changes how identity, permissions, and auditability must be designed, according to Ory. The security model shifts from per-agent controls to coordinated machine identity governance, because legacy IAM was not built for ephemeral, tool-using agent stacks.
Editorial analysis by NHI Mgmt Group, based on content published by Ory: “AI Agents vs Agentic AI: 7 Key Differences You Need to Know”.
Key questions
Q: How should security teams govern RAG and agentic AI differently?
A: Treat RAG as a retrieval governance problem and agentic AI as an execution governance problem.
Q: Why do agentic AI workflows create new IAM risk compared with traditional automation?
A: Traditional automation usually follows fixed rules and predictable paths, so its access model is easier to review.
Q: What are the signs that an agent’s effective permissions are too broad?
A: Look for agents that can move from retrieval to mutation, inherit user or service credentials without separation, or reach multiple environments with the same identity.
Practitioner guidance
- Map every agent to a distinct machine identity Inventory each agent, its scope, and its downstream tools so access can be issued and revoked per actor instead of per platform.
- Separate orchestration privileges from task privileges Give the orchestration layer only the authority needed to coordinate agents, and keep task-level permissions narrow for each individual agent.
- Replace shared secrets with standards-based flows Use OAuth 2.0 and OpenID Connect for agent authentication and delegation so access is traceable and not tied to static credentials.
Bottom line: AI agents and agentic AI create different identity problems because one governs a task-level runtime actor while the other governs the orchestration of many actors across a workflow.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
18/08/2026 3:28 pm
AI agents and agentic AI should not be governed as the same identity class. A single agent is a task-level identity, while agentic AI is an orchestration layer that composes multiple identities into a workflow. That difference changes the control surface from individual access decisions to chained delegation, handoff integrity, and attribution. The practitioner conclusion is that architecture, not branding, should determine the control model.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should security teams respond when agentic AI still depends on static credentials?
A: They should treat static credentials as a deployment blocker, not a convenience layer. If an agent can reuse shared secrets or long-lived tokens, its access scope becomes hard to trace, hard to revoke, and easy to abuse. The first priority is replacing reusable secrets with short-lived, attestable issuance and tying every credential to an accountable owner.
👉 Read our full editorial: AI agents vs agentic AI: identity controls that keep both safe