Distributed systems are built around assumptions about workload behavior: connections have reasonable lifetimes, retries eventually stop, traffic spikes have recognizable causes, and application code produces somewhat predictable query patterns.

AI agents violate many of these assumptions. They can fan out rapidly, retry failures indefinitely, create large numbers of short-lived connections, and generate novel queries at runtime. In a shared database platform, behavior like this can turn a small application bug into a noisy-neighbor problem, a retry storm, or an expensive distributed failure.

In this talk, I’ll use lessons from operating a Distributed Postgres platform to examine what happens when unpredictable workloads meet shared distributed infrastructure. We’ll look at connection and resource management, admission control and backpressure, failure containment, rate limiting, and the signals needed to distinguish an overloaded system from a badly behaving client.

Key Takeaways:

• Recognizing Agentic Traffic Patterns: Understand how autonomous AI workflows change connection lifecycles, concurrency, retry behavior, and query patterns. 
• Building Guardrails for Critical Infrastructure:  Use admission control, backpressure, timeouts, quotas, and isolation to prevent one client or workload from turning into a system-wide failure.
• Monitoring and Debugging Agent Workloads:  Catch agent-driven problems before they become expensive incidents.