Every decade identity hits a quiet inflection point. It started with passwords, then moved to MFA, then to passwordless, and now to passkeys. Each shift changed the user experience, but kept one thing constant. The “user” was always a human.
That assumption is starting to break.
Over the last 18 months, AI agents have moved from a novelty to necessity. They are scheduling meetings, writing code, planning trips, triggering workflows, interacting with financial systems, and increasingly making decisions without human touch.
Consumer AI adoption has exploded to over two billion global users. Enterprise adoption crossed 90%. With the rise of multimodal agents, the interface has grown past being on a screen and into APIs.
This changes everything.
Instead of users clicking buttons, agents are calling endpoints. Instead of intent being visible, hidden logic is driving behavior. And instead of minutes between actions, agents are performing thousands of actions every minute.
Identity systems were built around human cognition, like logins, consent screens, approvals, and persistent sessions, so they were never engineered for this velocity.
Our founder & CEO, Rakesh Soni put the security concern around the rise of agentic IAM simply and powerfully during his recent conversation on SC Media’s CISO Stories Podcast:
“When these agents are knocking on your door, the first question is the same as with any human: Who’s there? The difference is that agents don’t wait for you to answer.”
This is the defining challenge in the next phase of IAM. Though agents belong to humans, they behave like machines at the end of the day. It is only safe to give them access, not autonomy.
Some of the most surprising examples and real-world scenarios Soni shared around how agents behave in consumer ecosystems and what happens when they misinterpret intent are explored in depth in the full podcast with SC Media.
The Rise of Non-Human Identities (And Why They Break IAM Fundamentals)
For the first time in the history of digital identity, we may be approaching a point where the majority of application users are non-human agents. AI agents are beginning to purchase products, interpret content, and schedule activities, all through APIs, without ever touching a login screen.
This shift is already happening across both consumer and enterprise ecosystems.
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A shopping agent can now reorder household items based on behavior it predicts.
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A travel-planning agent can price-compare, book, modify, and cancel itineraries.
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A news agent can read, summarize, and deliver personalized content without the user ever visiting a website.
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A customer-support agent can submit and update tickets without a human in the loop.
These agents behave like autonomous software systems, yet they represent the intentions of a human behind them. And that creates the identity paradox our current IAM infrastructure was never designed to solve.
The problem becomes even more complex when velocity enters the equation. Agents don’t wait, get tired, or do second guesses. They operate at machine speed by issuing thousands of requests per minute in a way no human user ever could.
As Soni analyzes, “These agents are knocking on your door to get things done. And they won’t wait for you to answer.”
This simple observation reveals a hard truth for identity leaders that IAM systems built for human cognition will collapse under agent velocity.
Let’s break down why:
1. Agents Inherit Trust Without Inheriting Accountability
When a human delegates an action to an agent, IAM systems rarely distinguish between the two. The agent receives privileges as if it were the person, even though it lacks the human’s ability to interpret intent or understand boundaries. This creates an identity gap in which systems assume intent simply because credentials are valid. As Rakesh notes on the podcast, that assumption breaks down the moment agents act independently of the user’s expectations.
2. Mismatch Between Agent Logic and Real-World Environments
Humans naturally adjust when rules shift, or context requires nuance. Agents do not. They follow instructions literally, and when the environment evolves, their behavior can diverge from what the user intended. Identity systems that rely on predictable human patterns struggle to account for an actor that can repeat a misinterpretation thousands of times without hesitation.
3. One Agent Isn’t the Challenge; A Network of Agents Is
A single agent is rarely the problem. The challenge is the interconnected network of agents that enterprises and consumers begin to rely on. Agents call other agents, trigger downstream processes, and combine actions across multiple systems. This creates cascading chains of activity where a small misalignment in one agent can ripple through many applications before anyone notices.
4. Agents Introduce Identity Drift
Human users maintain relatively stable intentions over time, but agents evolve based on new prompts, data sources, fine-tuning updates, or model changes. An agent authenticated today may behave differently tomorrow. IAM was never designed for identities whose behavior changes autonomously, and this unpredictability becomes a structural weakness.
5. The Attack Surface Expands Through Legitimate Activity
Agents operate at machine speed, amplifying even harmless privileges into high-frequency actions that overwhelm oversight. A permission appropriate for a human, like editing a profile, retrieving data, or invoking an API, multiplies into thousands of operations per minute in the hands of an agent.
Some of the more surprising real-world manifestations of where agents overstep because they misunderstand boundaries were discussed in the podcast and are best heard directly from Soni. He’s framed these early cases as signals of a much larger systemic shift about to hit identity teams.
Also read : How AI Is Changing the Game in User Authentication
Why AI Agents Demand a New Identity Model (Human vs. Non-Human IAM)
The moment a non-human identity begins acting on a user’s behalf, the entire foundation of modern IAM stops behaving as designed.
The breakdown starts with intent. Human-driven IAM assumes that each action emerges from a conscious decision. But then, agents do not make decisions; they can only execute logic. They take a user’s instruction, reinterpret it through probabilistic reasoning, and act with absolute confidence.
On the other hand, IAM evaluates whether the credential is legitimate; it has no understanding of whether the interpretation is authorized. This is how valid credentials become vectors for unintended, large-scale outcomes because the system cannot distinguish human intention from machine execution.
The failure further deepens as agents evolve. They can change profoundly in a single model update. Their behavior, risk posture, and decision pathways mutate independently of the person who authenticated them. An IAM system sees the same token and the same user. But it is now authorizing an entity whose logic it has never evaluated.
And then comes velocity. Human IAM relies on authentication based on friction like MFA prompts, session timeouts, behavioral anomalies, or confirmation screens. Agents bypass all of it because they never interact with a UI. They operate purely through APIs, issuing commands at a frequency people never could.
Soni surfaced this with striking clarity in the podcast, saying, “You need to know not just who the agent represents, but what it should reasonably be allowed to do.”
The Hidden Risks of Agent Autonomy: When Intent Breaks at Machine Speed
When AI agents act at machine speed, intent fails silently and then scales. The real challenge is preventing misinterpretations from turning into high-velocity cascades before anyone has a chance to intervene. Four structural risks define this new reality:
1. Misinterpreted Intent Becomes Machine-Amplified
An agent misunderstanding expands into an entire workflow, which is executed end-to-end in milliseconds. Traditional IAM has no mechanism to detect that the action is “correct” for the token but “incorrect” for the human.
2. Velocity Collapses Human Guardrails by Default
Agents operate exclusively through APIs. Once an agent begins acting on flawed intent, the sequence is complete long before any intervention layer can respond.
3. Standing Permissions Turn Small Errors Into Systemic Issues
Human IAM assumes that a single approval can govern a full session. Agents turn that model into a liability. When agents inherit human-level privileges, even temporary misalignment becomes dangerous. A valid token, paired with an incorrect interpretation, is enough to trigger a high-impact chain reaction, even without malicious intent or external compromise.
The Shift to Agent-Aware Identity: What the Next IAM Era Requires
As soon as non-human actors begin participating in workflows, identity can no longer be anchored to static permissions or one-time authentication events. The trust fabric itself must evolve. IAM has to stop treating agents as extensions of the user and start treating them as independent decision systems that require their own boundaries, their own signals, and their own accountability models. Without this shift, organizations will continue to approve actions they never intended and justify failures they never saw coming.
The identity system must understand the relationship between the user and the agent, the scope being delegated, and the context in which actions occur. This requires continuous negotiation of trust rather than inherited trust. In this new model, the question is no longer “Has the user authenticated?” but “Does this specific agent action still reflect the user’s intent right now?” That reframing forces IAM to operate at the same tempo as the agents it governs, evaluating both credentials and alignment.
Soni, too, notes this in the podcast. “The organizations that thrive in the age of autonomous systems will be the ones that design identity not for humans alone, but for the ecosystems of agents that will soon power every customer journey, every workflow, and every application surface.”
The Three Pillars of Agentic IAM
Three core pillars define this shift and provide the foundation for securing agent behavior without slowing innovation.
Pillar 1 : Runtime, Policy-Driven Authorization
Human IAM relies on static permissions because our behavior is slow and predictable. Agents break that assumption immediately. Policy must now be evaluated at the moment of action, and not at login. Runtime authorization ensures every agent operation is checked against contextual rules that understand the user–agent relationship, the task being executed, and the evolving conditions around it. This removes the reliance on standing approvals and replaces them with continuous enforcement at machine speed.
Pillar 2 : Ephemeral, Task-Bound Credentials
Persistent credentials give agents more power than they need and expose organizations to unnecessary risk. Agentic IAM replaces long-lived tokens with short-lived, scoped credentials that expire automatically once a task completes. This drastically reduces the blast radius of misinterpretation or drift, ensuring an agent can only act within the narrow window and scope the human intended. Automation stays fast, but trust becomes finite and renewable.
Pillar 3 : Relationship-Based Access Control
Roles were designed for human hierarchies; agents operate in dynamic, multi-system workflows where role boundaries blur instantly. Relationship-based access evaluates whether a given agent, user, and resource form a valid trust relationship at that moment. It enforces boundaries based on context rather than organizational charts. This millisecond-level verification prevents agents from moving laterally, chaining unintended actions, or stepping outside delegated authority.
Together, these pillars form the architecture for the next era of identity. And as Soni emphasized in the podcast, it is the minimum requirement for organizations that expect AI agents to operate across customer journeys, enterprise workflows, and mission-critical systems.
What Loginradius Is Building for the Agentic Future
The future of identity will be shaped by how well organizations can govern the agents acting on behalf of their users. In the podcast, Soni makes it clear that the core challenge is ensuring the right agent belongs to the right user, and that its authority stays within the boundaries the user intends. That alignment between human intent and agent action is where LoginRadius is investing deeply, because without it, agents can easily “go rogue” and make decisions or transactions a user never approved.
As Soni explains, the LoginRadius Agentic IAM platform, now in beta, centers on solving two foundational problems:
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Verification of the correct agent-to-user binding, ensuring the system always knows which human an agent truly represents.
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Defining and enforcing the limits of what agents can do, using fine-grained authorization, attribute-based access control, and risk-informed policies.
These controls let users specify exactly what their agents are allowed to perform so they don’t “do some crazy stuff,” as he put it (whether that’s executing unintended actions, overspending, or mishandling sensitive data.)
The platform is also being designed for real-world scenarios already emerging across LoginRadius customers. Soni highlights use cases where users bring their own agents. For example, connecting ChatGPT to an e-commerce account to shop on their behalf, or using a news-reading agent to summarize articles based on personal preferences. As these patterns grow, LoginRadius is focused on creating the flows that allow users to decide what access an agent receives, how tokens and API keys are managed, and when a human approval must be reintroduced.
Soni also emphasizes the complexity of governing agents at scale, like:
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protecting tokens, certificates, and API keys;
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preventing agents from overshooting intended boundaries;
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ensuring privacy laws like GDPR and CCPA aren’t violated when agents handle personal data.
LoginRadius is designing its Agentic IAM capabilities so that “these agents are really helpful.” That guiding principle of binding agents to users, limiting what they can do, and enforcing approvals when autonomy becomes risky is at the heart of what the team is building for an AI-driven identity future.
If you want to understand the next frontier of identity and learn Rakesh’s full insights, examples, and predictions, watch the full podcast with SC Media.
