Systems where AI agents execute multi-step tasks autonomously, raising interconnected questions about harness architecture, state management, reliability engineering, human oversight, and the organizational context those agents operate within.

Benchmarks measure model or system capability, but their results are only as meaningful as their design — a recurring problem across LLM, multi-agent, and vision tasks, where tests built for one context are routinely applied to contexts they cannot capture.

Context engineering is the practice of deliberately constructing what an LLM receives in its context window — structuring, compressing, persisting, and retrieving information so agents produce reliable output across tasks and sessions.

LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

Reliability in software systems is achieved through structural constraints and environmental design rather than prompting, validation, or testing alone, as sources from agent engineering to durable execution consistently show.