When AI Agents Go Off Script: Why Agentic Gateways Are the Next Layer of Defense

Nadav Cornberg - Founder | CEO

AI agents are rapidly moving from experimental tools to core operators inside organizations. They automate workflows, interact with internal systems, and even collaborate with other agents. But as their autonomy grows, so does a subtle and powerful set of risks, such that traditional API gateways and security tools simply weren’t built to detect.

The core issue is behavior over time. AI agents don’t “break rules” in the same way humans or attackers do. Instead, they exploit allowed capabilities in unexpected ways, evolve their behavior after updates, and communicate through ambiguous natural language that slips past conventional controls.

To manage this new landscape, organizations need a new control layer: Agentic Gateways — systems designed to continuously observe agent behavior, infer intent, and ensure alignment with company policy as agents evolve.

1. Abusing Existing APIs in Unintended Ways

Agents often have legitimate access to APIs, but they can chain those capabilities to achieve outcomes the original designers never anticipated.

Take a simple example: an agent tasked with retrieving information about individual users. Instead of using a bulk endpoint (which might not exist), it loops through a single-user API a million times to reconstruct an entire dataset.

Every single call looks fine in isolation. There’s no “breach.” But collectively, it’s a massive policy violation, and existing gateways usually can’t tell the difference. Agentic Gateways address this by looking at sequences of actions over time, identifying when agents are using allowed capabilities in ways that undermine the system’s intent.

2. Unexpected New Behaviors Post Updates

Agents don’t stand still. They’re fine-tuned, connected to new tools, or upgraded with better reasoning. These changes can introduce entirely new behaviors, sometimes overnight.

For example, a support agent that used to respond to basic questions might suddenly start initiating refunds or escalating sensitive workflows after an update. These shifts often happen outside the traditional release cycle, which means security and governance teams may not even realize what changed.

An Agentic Gateway provides behavioral continuity, comparing current agent actions against historical patterns and policy baselines. When a new behavior emerges, it can flag or contain it early, before it turns into an incident.

3. Ambiguous Prompts Passed Between Agents

As multi-agent systems become more common, agents increasingly hand off instructions to each other using natural language. These handoffs can be ambiguous or even subjective, and they often bypass structured policy enforcement.

For example, one agent might send “fetch all relevant data for this group” to another. The receiving agent could interpret that broadly and initiate a large-scale data pull. Because these interactions happen in free-form language, existing monitoring tools struggle to detect intent or enforce policy.

Agentic Gateways can analyze the semantics of these exchanges, not just the API calls that follow. They bridge the gap between language and action, spotting when ambiguous instructions lead to behaviors that violate policy.

Why are Agentic Gateways Essential

AI agents don’t fit neatly into the security models we’ve built for humans. They are:

• Persistent - Once given a task, an agent will find any possible pathway to get it done. If the direct route is blocked, it will try alternatives — chaining APIs, rephrasing prompts, or iterating until it succeeds. Traditional controls that assume static usage patterns can’t keep up with this relentless adaptability.

  
  

• Evolving - Agents don’t stay static. Fine-tuning, new integrations, model upgrades, or subtle changes in upstream prompts can completely alter how they behave. A task that was safe yesterday may suddenly trigger unintended side effects today. Because these shifts often happen outside of standard release processes, they bypass traditional change management and security reviews — introducing new behaviors quietly and at scale.

  
  

• Opaque - Agents communicate and reason in natural language, often making decisions through chains of ambiguous or emergent instructions. Their internal reasoning isn’t fully visible, and their interactions can involve subtle linguistic cues that existing tools can’t interpret. As a result, understanding why an agent took a particular action often requires analyzing behavior across multiple steps, contexts, and interactions — not just a single API log.

Agentic Gateways provide the oversight layer that connects policy, behavior, and intent over time. They’re not just about access control, they’re about understanding what agents are doing and why, even as those strategies change.

As agentic systems become core to operations, this kind of persistent behavioral oversight won’t be optional. It will be the difference between scalable autonomy and silent, compounding risk.