Engineering Reliable AI Agents and Systems
Reliable AI depends on the systems around the model, not just the model itself.
Summary
Reliable AI requires infrastructure that controls identity, compute, security checks and long-running work rather than relying on model capability alone. Agent authentication needs separate identities, scoped short-lived credentials, delegation policies and audit, while BenchPod replaces guessed vLLM settings with measured benchmarks and recovery tools. Hunter Agent adds contextual security analysis beyond pattern-based SAST, finding business-logic and authentication flaws that ordinary scans miss. Long-running Claude projects show how verifiable tests, tool access and faster compute can sustain thousands of hours of agent work, although the costs can reach thousands of dollars.