INFERA AI.Firewall and Agent Runtime Security approach for protecting business AI agents

In 2025–2026, companies are massively transitioning from simple chatbots to autonomous AI agents that not only answer questions but perform real actions — work with email, databases, APIs, code, and even launch task chains in multi-agent systems.

Why old security tools don't work

Along with new agent capabilities came new risks. Classical security tools — WAF, DLP, and even first-generation LLM Firewalls — proved insufficient to control autonomous agents that make decisions independently and perform actions in corporate systems. If previously the task was limited to filtering incoming and outgoing text requests, now an agent can initiate a database call, send an email, or launch another agent on its own — and classical perimeter security tools simply aren't designed for such behavior.

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

What is Agent Runtime Security

The response to new risks was the emergence of a separate security direction — Agent Runtime Security. Unlike classical LLM firewalls that only check incoming and outgoing text requests, Agent Runtime Security controls agent behavior directly during execution: what actions it initiates, which systems it accesses, and what task chains it launches.

As an example of a solution that has already implemented key elements of this paradigm, the product INFERA AI.Firewall is mentioned — a tool positioned as protection for new-generation corporate AI agents working autonomously and performing real actions in company infrastructure.

How AI system protection has evolved

According to the material, the path of security development looks like this: initially, companies applied standard WAF and DLP systems designed to protect web applications and prevent data leaks. Then came the first generations of LLM Firewall, focused on filtering prompts and responses from language models. But with the transition to autonomous agents that act in real systems without constant human participation, a new level of protection became necessary — Agent Runtime Security, which controls agent behavior at runtime rather than text.

Why this matters for multi-agent systems

A separate source of risk is scenarios where one agent launches another or passes a task further down the chain. In such architecture, an error or malicious impact on input can spread immediately to multiple connected agents, each with access to its own systems — email, databases, APIs. This is why protection designed to control behavior at runtime, rather than just filtering individual text requests, becomes essential for companies building agent systems with real access rights to infrastructure. The longer the task chain between agents, the harder it is to notice a behavior deviation at the classical perimeter, and the more important it is to control at the moment of action execution.

What this means

As companies entrust AI agents with more and more real actions — from database work to launching task chains in multi-agent systems — the security perimeter shifts from filtering text to controlling agent behavior during execution. The Agent Runtime Security category and solutions like INFERA AI.Firewall are forming precisely as a response to this shift.