Enterprise AI has traditionally been task-oriented and good at handling defined workflows but ill-equipped for scenarios requiring independent judgment. Chatbots, RPA tools, and other automated systems were designed to support operations, not lead them.
AI agents mark a significant evolution. Rather than waiting for prompts, they proactively analyze, decide, and act. They work across systems, synthesize context, and respond dynamically in real time, transitioning from assistants to autonomous co-workers.
Also read: Indian HR Leaders Expect Agentic AI Adoption to Grow 383% by 2027
Consider a cyberattack: Traditional AI might flag anomalies and assist with triage. In contrast, agentic AI can trace the intrusion path, isolate affected systems, adjust firewall rules, and launch forensic data capture, all without waiting for human instruction.
This is one of the many ways agentic AI is redefining expectations across enterprise systems, especially within IAM.
A market on the precipice of transformation
According to Gartner®, by 2028, 33% of enterprise software will incorporate agentic AI, which is a sharp rise from less than 1% in 2024. These systems are expected to influence 15% of daily operational decisions, signaling a pivotal shift in enterprise governance and autonomy.
Vendors are now embedding agentic capabilities across critical domains. AI agents are being used to triage support tickets, summarize audit trails, interpret security logs, and recommend policy changes, all in real time.
These AI systems aren’t chatbots but they’re emerging as intelligent operational layers woven directly into enterprise workflows to support faster, context-driven decisions.
IAM frameworks were not designed for autonomous actors
Identity in the enterprise has evolved from human users to machine accounts like scripts, APIs, and bots. While each shift has added complexity, identities have remained predictable and role-bound.
Agentic AI changes that. These entities act independently, adapt in real time, and make decisions based on an evolving context, not static roles. Identity is no longer tied to a person or permission set. In fact, it’s dynamic, behavioral, and situational.
The fundamental assumption underpinning most IAM systems is anthropocentric, framing identities as human, assigned with defined roles and governed by predictable access patterns. AI agents defy this model. Their access needs are conditional, their activity patterns continuous, and their organizational placement fluid.
This misalignment creates several friction points:
- Shadow agentic AI: Autonomous agents can be deployed without an IT department’s knowledge, operating outside established IAM controls. This shadow AI introduces unmonitored access and decision-making, expanding the attack surface and complicating compliance efforts.
- Delegation complexity: AI agents often act on behalf of human users, making access delegation, audit trails, and revocation mechanisms critical. Without clear tracking of these “on-behalf” actions, organizations face challenges in accountability and risk management.
- Dynamic access needs: Traditional IAM systems struggle with the fluid access requirements of AI agents. Static role assignments and periodic reviews are insufficient for entities that adapt their behavior and access patterns in real time.
- Lack of explainability: AI-driven decisions can lack transparency, making it difficult to understand or justify access changes. This opacity hinders trust and complicates audits, especially in regulated industries.
- Governance blind spots: Existing policies and controls may not account for the autonomous nature of agentic AI, leading to gaps in oversight and potential policy violations.
Rewriting the rules of IAM
To govern this new class of actors, IAM must evolve into something more dynamic namely an intelligent, context-aware trust layer that validates every move, not just every identity.
That’s where Zero Trust becomes more than a philosophy. Every access request must be justified by context, behavior, and intent. Provisioning an identity once is no longer enough. Trust must be earned continuously.
In parallel, the concept of an identity fabric moves from vision to necessity. AI agents don’t stay confined to one platform or one system. They move fluidly across domains, clouds, and business units. Managing them demands a unified fabric of identity services stitched together by real-time telemetry, not brittle integrations.
So what does this evolution look like in practice?
- Life cycle control that evolves with the agent: Provisioning isn’t a set-and-forget task. Organizations need workflows that constantly reevaluate whether an agent should exist, what it’s doing, and what it still needs access to based on live behavior, not legacy roles. This isn’t just maintenance but it’s containment.
- Access decisions that are made in context: Attribute-based access control (ABAC), when powered by real-time telemetry, ensures permissions are aligned to purpose, context, and risk, not just roles. Think of it as intent-aware security: access that flexes when the situation changes.
- Monitoring that sees behavior, not just credentials: Each agent builds a behavioral fingerprint. By establishing what “normal” looks like, and watching for deviations, security teams can flag drift, lateral movement, or rogue decisions. This isn’t surveillance but how trust is earned and kept.
- Explainability built into every action: If an agent grants itself access or modifies a system, teams need to know why, not just by the log trail, but the logic: what inputs it had, what policy it applied, what action it followed. In the age of AI, transparency is control.
- Governance that moves at machine speed: Define life cycle rules, escalation thresholds, and corrective actions as policy-as-code and enforce them wherever agents operate: across platforms, pipelines, and cloud layers. Governance can’t be an afterthought. It has to live where the agents do.
A strategic recalibration of trust
Agentic AI is already weaving itself into the enterprise stack. Make no mistake it’s still early days. Most deployments today operate within narrow, well-scoped environments. Think predefined domains, high-volume tasks, and human-in-the-loop models. The leap to true autonomous coordination across business units, with minimal oversight, is still in progress.
Right now, IAM teams should be rewriting policies to handle autonomous identity life cycles. Security architects should be embedding risk signals into access decisions. CIOs should be experimenting with agent governance models that can scale across HR, finance, legal, and operations teams.
The organizations defining access, accountability, and autonomy today won’t just adopt agentic AI later but they’ll lead it. And the frameworks built today will determine whether AI agents become assets or liabilities.
The article has been written by Jay Reddy, Senior Technology Evangelist, ManageEngine
