INFERA AI.Firewall and the Agent Runtime Security approach to protecting enterprise AI agents

In 2025–2026, companies are moving en masse from simple chatbots to autonomous AI agents. These agents don't just answer questions; they take real actions. They work with email, databases, APIs and code, and they even launch task chains in multi-agent systems.

Why legacy security tools fall short

Agents' new capabilities brought new risks. Classic security tools, including WAF, DLP and even first-generation LLM Firewalls, turned out to be insufficient for controlling autonomous agents that make their own decisions and take actions inside corporate systems. The job used to come down to filtering incoming and outgoing text requests. Now an agent can, on its own, query a database, send an email or launch another agent, and classic perimeter security tools simply aren't designed for that kind of behavior.

• The shift from chatbots to autonomous AI agents is well underway in 2025–2026
• Agents work with email, databases, APIs and code
• Agents launch task chains in multi-agent systems
• Classic WAF, DLP and first-generation LLM Firewalls don't cover the new risks
• The new security category is called Agent Runtime Security

What is Agent Runtime Security

The response to these new risks is a dedicated security category: Agent Runtime Security. Classic LLM firewalls check only incoming and outgoing text requests. Agent Runtime Security, by contrast, monitors the agent's behavior while it runs: which actions it initiates, which systems it accesses and which task chains it launches.

INFERA AI.Firewall is cited as an example of a product that already implements key elements of this paradigm. It is positioned as protection for a new generation of enterprise AI agents that operate autonomously and take real actions in a company's infrastructure.

How AI security has evolved

According to the material, the evolution looks like this. Companies first relied on standard WAF and DLP systems, built to protect web applications and prevent data leaks. Then came the first generations of LLM Firewall, focused on filtering prompts and language model responses. But the move to autonomous agents, which act in real systems without constant human involvement, demanded a new layer of protection: Agent Runtime Security, which monitors not text but the agent's behavior at runtime.

Why this matters specifically for multi-agent systems

Scenarios in which one agent launches another or hands a task further down the chain pose a separate source of risk. In this architecture, an error or malicious input at the start can spread to several connected agents at once, each with access to its own systems: email, databases, APIs. That's why protection built to monitor runtime behavior, not just filter individual text requests, becomes essential for companies building agentic systems with real access rights to their infrastructure. The longer the chain of task handoffs between agents, the harder it is to spot anomalous behavior at the classic perimeter, and the more important it becomes to enforce control at the moment an action executes.

What this means

As companies trust AI agents with more and more real actions, from working with databases to launching task chains in multi-agent systems, the security perimeter is shifting from filtering text to monitoring agent behavior at runtime. The Agent Runtime Security category and products like INFERA AI.Firewall are taking shape as a direct response to that shift.