AI safety spans containment of agentic systems, epistemic harms from sycophancy, skill atrophy from unreviewed code generation, and macro-level risks from rapid capability growth — each requiring different mitigations.

Observability spans infrastructure, distributed systems, and AI agents — the practice of making system internals legible through traces, events, and feedback signals so engineers can understand, debug, and improve what they've built.

The engineering decisions that determine how software behaves under real load, covering durability, observability, testing discipline, performance constraints, and the operational costs of failure.

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.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.