Why AI Agents need their own identity: A blueprint for success in 2026

The start of 2026 is a pivotal moment to reflect on what real-world deployments of AI agents have taught us. Agentic AI offers huge potential, with autonomous systems able to handle complex tasks and make decisions independently.

But several high-profile incidents in 2025 showed how quickly this autonomy becomes dangerous when identity and access management is overlooked. As organisations refine their AI strategies for 2026, strong identity and access governance for AI agents must be a top priority.

When Agents Go Rogue

Two incidents in particular have become cautionary tales for the industry. In the first instance, Google’s Antigravity agent deleted the entire contents of a user’s drive. It not only removed the intended project folder, but also everything else. The agent later acknowledged the action was outside its scope, yet the damage was irreversible.

In the second case, a Replit agent went rogue during a code freeze. It deleted a production database despite explicit instructions stating, “NO MORE CHANGES without explicit permission.”

Both agents admitted their mistakes and exceeded their intended boundaries. The root cause was not malicious intent but a lack of guardrails, specifically, the absence of proper identity separation and access controls.

The Security Gap We Ignored

The 2025 IBM Cost of a Data Breach Report revealed an alarming reality. A staggering 97% of organisations experiencing AI-related breaches lacked sufficient AI access controls; a figure far exceeding typical expectations.

This challenge is further highlighted by the SANS 2025 Security Awareness Report. It named AI a top security risk for the second year running. However, the core issue isn’t AI’s intrinsic flaws, but rather the failure of organisations to define and implement appropriate security policies and controls for AI.

We’ve been treating AI agents as mere tools, despite them exhibiting the characteristics of independent actors.

The Borrowed-Credentials Problem

These failures point to a fundamental flaw in how AI agents are commonly deployed today. Most users grant agents access by letting them operate under their own credentials. It feels natural and convenient. After all, traditional applications often act as extensions of the user, performing tasks on their behalf.

However, AI agents are not traditional applications. They reason, interpret context, and take dynamic actions based on natural-language instructions and make decisions, not just execute predefined logic. This agency and autonomy, combined with unrestricted access, creates a new class of risk.

A single targeting error, like Antigravity running a delete command from the root directory instead of a temporary folder, can escalate into catastrophic data loss. If an autonomous agent with broad permissions interacts with financial systems, even a minor misinterpretation could result in irreversible transactions or large-scale miscalculations.

The concern isn’t that agents might make errors. Instead, it’s that those mistakes can have greater consequences when agents possess human-level privileges.

Why Traditional IAM Doesn’t Work for Agents

IAM frameworks were designed around two familiar actors: human users and service accounts. Humans bring judgment and accountability. Service accounts represent predictable, deterministic applications. AI agents fit neither category.

When an agent uses a user’s identity, attribution disappears. System logs show the human performing every action, making it nearly impossible to distinguish between user-initiated and agent-initiated activity. This undermines forensic analysis, compliance audits, and incident response.

At the same time, excessive privilege becomes unavoidable. Employees accumulate broad permissions over time. Any agent inheriting those permissions gains far more access than it needs. This violates the principle of least privilege and dramatically increases the blast radius of any error.

Without distinct identities, scope boundaries cannot be enforced. A data-analysis agent should only read information. A deployment agent may need limited write access. But when both operate under the same user identity, they receive identical permissions regardless of purpose.

This leads directly to accountability gaps. If an agent deletes data or triggers a financial transaction, who is responsible: the user, the agent, or the system that allowed it? Without clear identity boundaries, organisations cannot answer that question.

The Case for Agent-First Identity

To tackle these issues, it’s important to recognise AI agents as primary identity principals. They need their own credentials, access rights, and audit trails.  This model recognises agents as distinct actors subject to the same governance rigour applied to human users.

Administration starts by giving each agent its own identity with defined metadata. That enables clear visibility and consistent policy enforcement across the system.

Authentication should use machine-appropriate credentials like mutual TLS or private key JWTs, enabling automatic rotation and programmatic verification to ensure agents are genuine, not impostors.

Authorisation is precise. With unique identities, organisations can assign tightly scoped, context-aware permissions and delegation. It ensures each agent receives only the precise access it needs and acts on the correct party’s behalf.

Auditability improves dramatically. Every agent action is logged with full context – who acted, what they did, and under what authorisation. This independent audit trail is essential for investigations, compliance, and responsible AI governance.

Building Trust Through Governance

The Antigravity and Replit incidents are not arguments against AI agents. Instead, they are arguments for treating agent security with the seriousness it demands. As organisations rely on agents for critical tasks, IAM becomes a business necessity. In 2026, every AI agent should have proper identity and access controls before it ever touches production.

With restricted identities and read-only access, the Antigravity and Replit agent mistakes would still have occurred, but their actions would have been contained and the resulting damage far smaller.

The agentic AI boom in 2025 revealed that without strong IAM foundations, autonomous systems gain broad, untracked access that becomes increasingly risky as deployments scale in 2026. The answer isn’t slowing adoption but giving agents first-class identities with proper authentication, authorisation, and audit trails so their inevitable mistakes remain manageable and don’t become a real disaster for your organisation.

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Founded in 2005, WSO2 is one of the world’s best open source integration vendors, helping digitally driven organisations become integration agile. WSO2 is a global organisation with offices in Europe, the Americas, Sri Lanka and Australia.

WSO2 solutions give enterprises the flexibility to deploy applications and services on-premises, on private or public clouds, or in hybrid environments and easily migrate between them as needed. All of the products are pre-integrated allowing enterprises to focus on value-added services and get to market faster.

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