Runtime Control for AI Agents

Alex Casalboni's examination of runtime control for AI agents reveals the inadequacies of prompt-based defenses in securing autonomous agents, as adaptive attacks bypass them over 90% of the time, leading to vulnerabilities such as execution poisoning and unauthorized actions. Instead of relying on input filtering, Casalboni advocates for a governance model focusing on the "action path" of agents, controlling how they call tools and what permissions they hold, through a 5-layer runtime stack that includes approval, authorization, policy checks, containment, and observability. This framework enables real-time management of agent capabilities using feature flags and the Model Context Protocol (MCP), which integrates security checks and authorization boundaries directly into the development process, thus maintaining deployment velocity without sacrificing control. By treating agent actions as dynamic software capabilities, the approach provides a more robust security solution that addresses the shortcomings of static orchestrators and LLM firewalls, allowing for scalable, resilient AI governance.

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