Moving Beyond the Prototype
Building a demo of an AI agent is easy. Building one that runs reliably in production is a different beast entirely. Here are the core engineering practices we follow at FrontierAI.
1. Robust Error Handling
Agents can and will fail. They might hallucinate, get stuck in loops, or encounter API errors. Production systems need:
- •Self-correction mechanisms: Agents should be able to detect when they've failed and retry with a different strategy.
- •Circuit breakers: Prevent run-away costs or infinite loops.
- •Human-in-the-loop fallback: Seamlessly escalate to a human when confidence is low.
2. Observability is Key
You can't fix what you can't see. We implement comprehensive tracing (using tools like LangSmith or Arize) to visualize the agent's "thought process." This allows us to debug reasoning errors, not just code errors.
3. Deterministic Testing
Testing stochastic systems is challenging. We rely on evaluation frameworks that grade agent performance against a "golden dataset" of input/output pairs, measuring correctness, latency, and cost.