Closing the Visibility Gap: Lessons from Safety Critical Agentic Systems

AI agents are moving to production, revealing a "visibility gap" traditional monitoring can’t bridge. This talk shares lessons from shipping agentic AI in safety-critical workflows where agents reason, plan, and execute actions. We’ll explore building observable systems that provide high signal telemetry into non-deterministic decision making. We’ll examine production agent architecture, combining LLMs with structured reasoning and guardrails, and how to instrument test time compute to track iterative refinement. The session covers designing interoperable tool interfaces that emit rich execution traces, orchestrating specialized agents with reliable handoffs, and building auto-correction loops that parse feedback to adapt in real-time. Finally, we address evaluation, why traditional metrics fail and how to design domain aware benchmarks that treat reasoning traces as a first class observability primitive. Drawing from production deployments, we’ll discuss how observability helps agents uncover system issues missed by humans and why the future of software depends on agent native infrastructure.