Chaos engineering has been standard practice in distributed systems for over a decade - kill a node, inject latency, watch what breaks, fix it before production does. Agentic AI systems now chain LLM calls to external tools (via MCP or similar), but there's no equivalent discipline yet for what happens when those tools misbehave. In this 30-minute session, I'll apply classic chaos-engineering principles - fault injection, steady-state hypothesis, blast-radius containment - to an actual agent pipeline: tool calls that time out, return malformed data, or silently disappear mid-chain. I'll walk through what broke loudly vs. what failed silently and went undetected, share the one number from testing that surprised me most, and leave the audience with a minimal "chaos toolkit for agents" checklist they can apply to their own systems.
Hazel Mahajan is a software engineer specializing in backend systems, distributed architectures, and cloud infrastructure. She has worked with technologies including Java, Python, Go, Kafka, AWS, Docker, and Kubernetes, with experience building scalable APIs and event-driven systems. She recently completed an MS in Computer Science and Engineering at the University at Buffalo and previously worked as a Software Engineer at Delhivery.
