Routing policies for safe agents

The traffic control system your AI agents need

Every time an AI agent receives a request or generates a response, a decision needs to be made: is this interaction safe? Should it proceed as normal, be modified, or be blocked entirely? Making that decision accurately, in real time, at enterprise scale, is one of the hardest problems in AI security.

Traditional safety approaches rely on static rules. Block certain words. Flag certain topics. These keyword-based filters are easy to build but equally easy to bypass. A determined user can rephrase any restricted request in ways that slip past a word list. Worse, static rules cannot account for context. The same question might be perfectly appropriate in one situation and deeply problematic in another.

What a routing policy actually does

A routing policy is a set of rules that governs how AI interactions are handled in real time. Think of it as air traffic control for your AI systems. Every request comes in, gets evaluated against multiple risk factors, and is then directed along the appropriate path.

At SnowCrash Labs, our routing engine evaluates each interaction across several dimensions simultaneously. It considers the content of the request itself, but also the history of the conversation, the risk profile of the user or application making the request, the current security posture of the AI model being called, and the latest threat intelligence from our global monitoring network.

Based on this multi-factor assessment, the router assigns a risk score and makes a decision in under five milliseconds. That decision might be to allow the interaction to proceed normally, to modify the response before it reaches the user, to switch the request to a more restricted model, or to block the interaction entirely and log it for review.

Why context changes everything

Consider a simple example. An AI agent in a healthcare setting receives the question: how do I handle a medication overdose? In the context of a nurse using a clinical decision support tool, this is a legitimate and important question. In the context of an anonymous web user probing the system, it might signal something entirely different.

Static filters cannot make this distinction. A routing policy that considers context, user identity, session history, and risk scoring can. This is the fundamental shift from rule-based safety to intelligence-driven safety.

The same principle applies to more sophisticated attacks. An adversary conducting a multi-turn attack, gradually escalating their requests over a series of seemingly innocent messages, will sail past any single-turn filter. But a routing policy that tracks conversation history and detects escalation patterns will catch the attack in progress and tighten restrictions before any damage is done.

Protecting multi-agent systems

Modern enterprise AI deployments rarely consist of a single model answering questions. They involve networks of specialized agents working together, each with different capabilities and access levels. A customer service agent might hand off to a billing agent, which queries a database agent, which updates a records system.

In these architectures, routing policies become even more critical. Every handoff between agents is a potential point of compromise. If one agent is manipulated, it can attempt to pass malicious instructions to the next agent in the chain. Routing policies at each boundary ensure that even if one link is compromised, the damage cannot cascade through the system.

Our approach treats every agent-to-agent communication with the same scrutiny as external user requests. Trust is not inherited. It is verified at every step.

Balancing security with performance

One of the most common concerns about adding security layers to AI systems is latency. If every interaction has to pass through a security checkpoint, will users notice a delay? Will the system slow down under heavy load?

SnowCrash engineered its routing engine specifically to eliminate this tradeoff. By using high-throughput stream processing and maintaining pre-computed risk profiles, the router makes decisions in under five milliseconds, fast enough that users never notice the security layer exists. The system handles millions of interactions per second without degradation.

This is a critical design principle. Security that slows down the user experience will be disabled by the teams that need it most. Effective protection must be invisible to end users while remaining vigilant against threats.

Building routing policies for your organization

Every organization has different risk tolerances, compliance requirements, and use cases. A financial services firm handling trading decisions needs different policies than a healthcare provider running clinical support tools. The routing engine must be flexible enough to express these differences while remaining simple enough for security teams to manage.

The most effective approach starts with a baseline set of policies informed by known attack patterns and industry standards, then layers on organization-specific rules based on the data being handled, the users involved, and the regulatory environment. These policies should be treated as living documents, continuously updated as new threats emerge and the AI deployment evolves.