Leon Garcia-Camargo

Firetiger runs a fleet of agents that investigate incidents, monitor deployments, and dig through telemetry on behalf of our customers. Every one of those agents is, at its core, a loop around an LLM, which means our single biggest cost of goods sold is inference. The models we choose to

The Problem With Long-Running Agents Firetiger has many agents with long task horizons (persistent goals which evolve over months). Some are user-defined: “I want my api service to get healthier…” or “I want to ensure our agent output doesn’t degrade…” Agents monitor the system, propose changes, see

The hardest part of building autonomous agents is not making them smart. It is deciding what to do when they are smarter than what you designed for. As we build agents at Firetiger, we keep running into this problem. We always ask ourselves whether we should further constrain our agents