Agentic AI tools and identity governance: what teams need to know

TL;DR: Agentic AI tools now plan, act, and adapt across enterprise systems without constant prompting, but that autonomy expands the identity problem beyond static automation, according to Lasso Security. Existing IAM, audit, and approval models were built for access that stays reviewable, not for actors that can change scope mid-session.

NHIMG editorial — based on content published by Lasso Security: Top 13 Agentic AI Tools in 2026 and Their Key Features

By the numbers:

• By 2029, 80% of customer support issues will be handled by an AI agent, according to Gartner.
• 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 agentic AI tools before they reach production?

A: Start by treating the agent as a governed identity, not a feature.

Q: Why do agentic AI tools complicate zero-trust assumptions?

A: Zero Trust assumes continuous verification around a known actor and a bounded request.

Q: What fails when organisations manage agents like ordinary automation?

A: They miss the fact that the actor is making decisions, not just executing a script.

Practitioner guidance

• Classify every agent as an identity object before deployment Assign an owner, business purpose, and permitted action envelope before the agent is connected to production tools.
• Constrain tool chaining by session and purpose Limit which connectors an agent can combine in one execution path, and separate read, write, and external-action permissions.
• Bind memory to explicit trust windows Decide which retained context can influence later actions, and expire it when the approval basis changes.

What's in the full article

Lasso Security's full article covers the operational detail this post intentionally leaves for the source:

• Vendor-by-vendor descriptions of the 13 tools, including platform scope and interface style.
• Comparative feature tables that show which tools emphasize browser control, APIs, memory, or low-code design.
• Implementation-facing examples of how each platform fits specific business workflows.
• Source-specific commentary on the vendor's view of agentic adoption and control trade-offs.

👉 Read Lasso Security's analysis of the top 13 agentic AI tools and their risks →

Agentic AI tools and identity governance: what teams need to know?

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