The Identity Gaps in Agentic AI: 9 Problems We Must Solve to Secure the Future - Source: securityboulevard.com

AI agents have evolved from passive tools into proactive actors—making decisions, executing transactions, and interacting with APIs autonomously. Unlike traditional non-human identities (NHI) that serve narrow, static purposes, agentic identities are dynamic, ephemeral, and often independent in their actions.

By 2026, Gartner predicts that 30% of enterprises will rely on AI agents that operate with minimal human input. This shift has outpaced legacy IAM systems—architectures built for human users and static NHIs cannot meet the demands of agentic AI. Before enterprises can secure agentic AI, they must recognize the identity gaps these agents expose at every step of their lifecycle.

Let’s break down the nine identity problems that must be solved to build a secure foundation for AI agent operations.

9 Problems in the Agentic User Flow

Despite the rapid rise of autonomous AI agents, most identity systems still follow patterns built for human users and static applications. That mismatch introduces a series of critical gaps—points of failure where trust, control, and security break down in the agentic user flow. Here’s where those problems show up most acutely.

1. We Lack Strong Initial Authentication for Agent Delegation

Today’s identity systems don’t provide secure, passwordless flows that bind humans or delegating agents to AI agents without risk of credential leakage. The lack of seamless, frictionless OIDC authentication with passkeys, FaceID, or similar methods leaves gaps that attackers can exploit.

Problem: Human-to-agent authentication chains aren’t trustworthy enough to withstand machine-speed operations.

2. Delegation and Trust Are Vague and Implicit

In legacy OAuth implementations, trust between subject (human/delegator) and actor (agent) is often coarse and implicit. Granular delegation rights via scopes are rarely enforced, leaving agents with excessive or unclear authority.

Problem: Delegation boundaries are fuzzy, violating Zero Trust principles.

3. Intent Is Not Explicitly Captured

When a human or agent directs an AI agent to act, most systems fail to capture or bind that intent clearly to the agent’s task. This creates ambiguity in audit trails and increases the risk of agents executing unintended or unauthorized actions.

Problem: The chain of intent is broken, and agents can operate outside their mandate.

4. Agents Lack Secure, Context-Aware Task Discovery

AI agents must dynamically locate APIs, services, and endpoints to fulfill tasks. But today’s discovery mechanisms don’t integrate with identity systems, exposing APIs to unverified queries and increasing lateral movement risk.

Problem: No identity-aware discovery leaves agents free to probe systems without guardrails.

5. Agent Authentication to APIs Is Weak or Nonexistent

Many agents connect to APIs using static keys or generic client credentials. Public agents rarely use PKCE properly; internal agents often lack workload identity protections like SPIFFE/SPIRE. This opens APIs to spoofing and unauthorized access.

Problem: Agents are not reliably proving who they are to systems they access.

6. Agents Are Provisioned Poorly or Not at All

Static pre-provisioning of agents leads to credential sprawl and orphaned identities. Conversely, many agents aren’t provisioned at all—operating invisibly in the environment. Without Just-in-Time (JIT) identity creation, there’s no governance, no context, no accountability.

Problem: Agents are either over-provisioned or invisible, with no dynamic control.

7. Access and Authorization Are Too Coarse

Legacy IAM applies broad API or app-level controls but lacks fine-grained, dynamic policy enforcement. Without ABAC/OPA models, agents gain access beyond what their task requires, violating least privilege and creating audit gaps.

Problem: Zero Trust is not applied at the precision level agentic AI demands.

8. No Human-In-The-Loop for Sensitive Agent Actions

Most IAM systems have no way to bring humans into the loop when agents attempt sensitive actions. There’s no liveness validation, no passwordless step-up MFA, no explicit approve/deny decision at runtime.

Problem: Agents operate unchecked in scenarios where human validation should be mandatory.

9. Insufficient Observability and Logging

Traditional identity systems log coarse activity but don’t capture the full agentic context—subject, actor, intent, policy decisions, outcomes, and delegation chains. Without this, forensic reconstruction, compliance audits, and incident response are crippled.

Problem: Agent operations are invisible or insufficiently logged, undermining trust and accountability.

Why We Must Solve These Problems Now

AI agents are transforming enterprise operations, but they introduce systemic risks if we don’t close these gaps:

Over-permissioned agents expose critical data and APIs.

Audit failures leave enterprises unable to prove compliance.

Uncontrolled agent actions slow innovation as security clamps down.

The future isn’t static identity. It’s agentic. And we must secure it before the risks outpace our ability to manage them. Maverics Agentic Identity exists to solve these nine problems—enabling Zero Trust at machine speed, dynamic policy control, and secure, observable agent ecosystems.

Read the next blog post in this series to learn more: A New Identity Playbook for AI Agents: Securing the Agentic User Flow