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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.
NHIMG editorial — based on content published by Ory: AI Agents vs Agentic AI: 7 Key Differences You Need to Know
By the numbers:
- 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.
Questions worth separating out
Q: How should security teams govern AI agents that need access only for a single task?
A: Use just-in-time identity issuance tied to the task, not a standing account that remains valid after the work finishes.
Q: Why do multi agent systems create more identity risk than single AI assistants?
A: Multi agent systems create more identity risk because they combine delegation, shared context, and external communication across multiple execution steps.
Q: What do teams get wrong about least privilege for AI agents?
A: They often stop at permission scope and ignore behavioural scope.
Practitioner guidance
- Define a credential per agent Register every AI agent as a distinct non-human identity with its own lifecycle, ownership, and revocation path.
- Scope permissions to task boundaries Restrict each agent to the minimum data sources and actions required for its exact job, then test what happens when the task is completed or the context changes.
- Treat handoffs as control points Map every agent-to-agent and agent-to-tool transition as a trust boundary, then require logging, authorization checks, and exception handling at each stage.
What's in the full article
Ory's full blog post covers the operational detail this post intentionally leaves for the source:
- A step-by-step comparison of how single agents and orchestration layers differ in practice across planning, tool use, and execution.
- Examples of how OAuth 2.0, OIDC, SCIM, and MCP fit into agent identity architecture at runtime.
- The article's full identity model for deciding when an agent needs independent credentials versus workflow-level governance.
- Additional implementation examples showing how teams can apply least privilege and traceability to multi-agent systems.
👉 Read Ory's analysis of AI agents versus agentic AI and identity controls →
AI agents vs agentic AI: what identity teams need to govern?
Explore further
18/08/2026 3:28 pm
AI agent identity is a machine identity problem first and an AI problem second. The article is right to separate single-task agents from orchestration layers, but the governance burden starts with credentials, scopes, and revocation. Once an agent can act, it must be registered, constrained, and attributable like any other non-human identity. The practical conclusion is that agent projects fail when they are treated as model deployments instead of identity estates.
A few things that frame the scale:
- 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.
- The same research found that only 52% of companies can track and audit the data their AI agents access, leaving 48% with a compliance and investigation blind spot.
A question worth separating out:
Q: How should security teams govern AI workflows that use multiple tools and data sources?
A: Security teams should govern AI workflows by placing explicit authorization at each decision point, not by relying on the permissions attached to the surrounding application or service account. The practical goal is to scope read, retrieve, and execute access separately so the workflow cannot inherit broader reach than it needs for the task.
👉 Read our full editorial: AI agents vs agentic AI: identity controls that keep both safe
