Operating Autonomous Agents
A series on the infrastructure required to run AI agents as production systems: control planes, gateway observability, runtime safety, cost governance, evals, memory, and kill switches. Plus notes on adjacent topics.
The Agent Control Plane
Autonomous AI agents are becoming a new production substrate. The hard enterprise problem is not just building smarter agents; it is operating them with identity, observability, policy enforcement, cost attribution, runtime isolation, evals, and kill switches.
Agent Observability Belongs at the Gateway
Client-side logs are not enough for enterprise AI agents. The model gateway is the best choke point for observing model calls, tool intent, cost, risk, and agent behavior.
AI Cost Control Is an Engineering Discipline
AI spend will not be controlled by asking engineers to use models less. It needs engineering systems: attribution, model routing, prompt caching, context management, budgets, evals, and outcome-based measurement.
Kill Switches for Autonomous AI Agents
Autonomous agents need targeted kill switches. Revoking global credentials or shutting down the whole platform is too blunt for enterprise-scale agent operations.
Agent Memory Needs Benchmarks, Not Vibes
Memory systems should prove measurable lift over baseline: higher success rates, lower cost, faster convergence, and no unacceptable safety regressions.
Why Secure Agent Execution Becomes Foundational Infrastructure
As AI agents move from demos to production, the sandbox underneath them becomes one of the most important pieces of infrastructure we build. Isolation, capability boundaries, and auditability stop being features and start being the substrate.
Systems Thinking Across Domains
The same patterns that make software architecture work — incentive alignment, risk management, feedback loops — show up everywhere from real estate to AGI research.