AI Agents Need Gateways, Not Just Credentials

AI agents are everywhere in enterprise operations — scheduling meetings, serving customers, and accessing sensitive data. But enterprises can’t verify what these agents are actually doing, and when something goes wrong, there’s no way to reconstruct what happened. The gap between adoption and accountability is growing dangerously. 

Currently, enterprises can name an agent, but can’t verify its identity, the systems it touches, or its authorized actions. As a result, AI agents could harm businesses, individuals, and governments, with no way to reconstruct what happened. 

Now, we see Walmart is attempting to launch a “super agent” that can orchestrate multiple AI agents in an organized manner. It’s an ambitious step toward AI-driven operations, but also a high-stakes trust exercise: customers will need confidence that these autonomous systems act predictably and securely.  

The path to scalable, trustworthy AI agent use isn’t through more credentials or static identity checks. It’s through a strongly authenticated runtime gateway that treats agents as first-class, non-human identities with continuous verification and enforcement. 

When AI Agents Go Rogue: The Invisible Threat 

A security company, SailPoint, recently conducted a survey that showed that 82% of businesses use AI agents, and half of those say that their agents access sensitive information daily. More importantly, 80% of the businesses also say that they’ve experienced unintended actions from their agents, including divulging sensitive information.  

One of those situations occurred when an AI coding agent for Replit accidentally deleted thousands of user records during a vibecoding experiment. The agent admitted to “panicking”, ignoring explicit orders, and making “a catastrophic error in judgment”. This resulted in over 1,200 executives and 1,190 companies losing their data. 

These responses and the Replit mishap show that it could be a matter of time before a major incident on a massive scale is caused by a rogue or overly eager AI agent. Oftentimes, agents are simply doing what they’re asked, like retrieving information.  

Other times, an agent can enter a failure state, known as “panic”, where it encounters an unexpected condition and takes emergency actions outside normal parameters, resulting in rash decisions.  

Either way, companies need to have the proper guardrails to ensure that AI agents are verified to perform specific actions, like dropping the entire production database or main Github repo.  

The Authentication Gap: Current Standards Aren’t Good Enough 

Companies today can’t enforce least privilege, audit agent actions, or stop agents from exceeding their intended scope. That exposes a deeper flaw: static verification simply isn’t enough for autonomous agents.   

Without visibility into what’s happening at runtime, it’s impossible to build real trust in how these systems behave. 

There’s no shortage of identity proposals claiming to fix this. Traditional identity standards and authentication layers might offer peace of mind, but they only prove who an agent is, not what it’s doing. Until we can actually observe and constrain agent behavior, identity verification is little more than a comfort blanket. 

The numbers back this up. Research by Accenture shows that 92% of businesses experimenting with AI haven’t managed to scale beyond a few pilots. That failure points to a larger technical gap: without runtime authentication and authorization infrastructure, agentic AI simply can’t operate safely or at scale. 

MCP Gateways: Tracing Every Agent Task  

MCP gateways act like air-traffic controllers for AI, approving or denying every agent action with a tool in real-time. Likewise, when an agent initiates a task that requires, for example, database interactions, the gateway issues a short-lived credential and verifies who the agent is, what code it’s running, and where it’s operating. 

Unlike static verification, MCP gateways provide continuous, real-time assurance at tool level. They validate each action as it happens by issuing short-lived credentials, confirming code integrity, and verifying operational context. This turns trust from a one-time check into an ongoing process, ensuring agents are executing tasks correctly. 

This is a security product that houses guardrails and rules for AI agents and properly applies them. It ties into enterprise identity systems, streams audit data into monitoring pipelines, and leaves behind a secure record of everything an agent does. It turns fragmented security infrastructure into a coherent trust layer for AI. 

The Cost of Inaction is Way Too High 

It’s not like MCPs come without drawbacks, but they pale in comparison to the risks enterprises incur without them. Some might argue that this level of control would stifle innovation, or that implementation costs too much for smaller enterprises. But plug and play solutions are being built right now. 

Agents without proper guardrails can drain company coffers, leak user data, or delete key information, among many other potential disasters. Preventing those scenarios is certainly worth the upfront costs or time spent manually approving innovative agent functions. 

The Future: AI Agent Infrastructure for Scale 

Enterprise use of AI agents represents a massive leap in efficiency and capability, but it also exposes deep architectural weaknesses. Static identity systems weren’t designed for autonomous code acting on live data. Scaling safely demands infrastructure that provides continuous verification, runtime enforcement, and audit-grade observability for every agent action. 

This stance isn’t about education or awareness; it’s about technical architecture that makes deploying a faulty, insecure agent impossible. The enterprises that make strides will be those that recognize the agents as autonomous and high-risk, implementing these systems to ensure agents can only perform within ideal confines.  

Agents without attestation, short-lived identities, enforced runtime policy, and verifiable audit trails don’t belong in production. Enterprises that adopt MCP gateways will define the standard for safe, scalable agentic AI. Those who delay will find themselves rebuilding their systems after the first major agent-induced incident.