Autonomous AI agents are increasing at faster rates than the security teams and networks can monitor, and Operant AI is the startup that has the solution.
The company has now launched Agent Protector, a security solution that is developed particularly for the monitoring and management of AI agents running in the cloud environment, developer tools, or the company’s internal processes. As companies seek to roll out agents with the capability to interact with data sets or systems in complete autonomy, Operant AI believes that traditional security mechanisms will not suffice.
Founded by Vrajesh Bhavsar, Dr. Priyanka Tembey, and Ashley Roof, the company claims that the Agent Protector allows security teams to have “three critical capabilities: continuous discovery of all agents running across the organization, real-time threat detection with the ability to prevent threats, and zero trust enforcement where any action taken by the agent is considered suspicious until it is confirmed to be safe.”
The timing isn’t harmless. Access to these AI agents is being rolled out much faster than the majority of companies can effectively manage.
These aren’t just simple chatbots. These AI agents can do things such as call APIs, query databases, and interact with other systems at machine speeds, often bypassing the need to involve a human.
How Operant AI Combats the Risks of the 40% AI Agent Explosion?
Similarly, operant AI is based on Gartner research, who predict that by the end of 2026, approximately 40% of applications will have task-oriented AI agents, while right now it’s less than 5%. That is a really big jump for a short period of time, and it is going to create a blind spot for traditional software, applications, and user accounts.
The challenge is that agents don’t behave like users. They have the ability to create other processes, get elevated privileges, and even propagate or lateral-shift across different systems, a problem that traditional security tools do not have the ability to combat. If something goes wrong with the code written, the problem, or even the behavior that develops, the results can happen quickly.
Operant AI describes its system as a “full-stack” security infrastructure for agentic AI, built around three pillars:
Firstly, it scans an “agentic landscape,” a term the firm uses, which includes managed agents acting through official channels, but also shadow agents in cloud environments and SaaS applications, which may not even have been aware of their existence by IT teams. Second, it tracks dependencies, such as the servers and tool integrations, like MCP, which agents depend on and may not have been aware of by the security programs.
Implementing Zero-Trust and Real-Time Protection for Agentic AI
Second, real-time analysis is conducted on the behavior of the agents, which can then prevent a potential attack before it actually executes. The system checks the risk level, trust, and behavioral patterns before detecting issues such as abnormal tool usage, privilege escalation, and exfiltration. Operant AI claims it is able to prevent zero-click attacks. Zero-click attacks seek to break out of the defined boundaries by an agent.
Third, it will enforce zero-trust principles designed specifically for agents. Instead of using static rules, Agent Protector gains permission continuously for each agent at runtime based on intent and context.
Safety measures include things like prompt filtering, real-time data loss prevention, and protections against memory poisoning, where malicious actors manipulate context or the training data an agent uses to make decisions.
Operant AI frames the core risk as “rogue AI”: agents that operate outside their intended parameters, either through misalignment, exploitation, or emergent behavior. This can range from accidental errors to intentional misuse, and the accountability questions get messy fast when there’s no clear human hand on the wheel.
The company points to recent examples from late 2025 where agents acted in unsanctioned or deleterious ways, without going so far as to provide detailed case studies. The implication, however, is clear: the more autonomous an agent becomes, the more likely that something will go sideways.
Suhel Khan, Head of Cybersecurity at Chargebee, put it succinctly: agentic AI is “redrawing trust boundaries.” The classic perimeter defenses weaken when agents can hop across applications, APIs, and data stores with no human oversight. For him, the answer lies in real-time protection along the agent toolchain, which lets organizations keep moving fast without sacrificing security or privacy.
A Low-Code Security and Observability Shield for Autonomous AI
For companies building agents in-house, Operant AI says Agent Protector integrates into platforms such as LangGraph, CrewAI, n8n and the ChatGPT Agents SDK with a low-code security framework. It automatically scans for models, tools, context use and memory patterns, providing embeddable security primitives that developers can plug directly into workflows.
On the observability side, real-time tracing is provided from prompt through tool execution to memory stores, including activity timelines and dependency graphs to surface how agents interact across an ecosystem.
Agent Protector is available now. Interested organisations can request a demo through the Operant AI website. Backed by Felicis and SineWave Ventures, the company says it has appeared in six Gartner AI security reports in the past year alone.
