AI Alignment is the field and engineering practice of making AI systems behave in accordance with human intentions, values, and safety constraints. In 2026,
Alignment makes models pursue intended goals safely: post-training techniques (RLHF, constitutional and AI-feedback methods) teach helpfulness and refusal behaviors, while evaluation and red-teaming probe the gaps. For agents, alignment extends to bounded autonomy: goals, constraints, and oversight that keep multi-step behavior within human intent.
Alignment determines whether capability is an asset or a hazard: misaligned outputs erode trust, and misaligned agents act wrongly at machine speed. It's both a frontier research problem and a daily engineering practice: every system prompt, guardrail, and approval gate is applied alignment.
Reinforcement learning from human feedback: humans rank model outputs, a reward model learns those preferences, and the model is optimized against it, the breakthrough that turned raw predictors into helpful assistants, since extended by AI-feedback variants.
Human intent is underspecified; models optimize proxies and find loopholes (reward hacking, sycophancy); and capability growth keeps raising the stakes. Each fix surfaces subtler failure modes: alignment is ongoing engineering, not a solved box.
Specify desired behavior testably (evals), constrain agents to least privilege with oversight on consequential actions, monitor for drift and manipulation, and treat system prompts and guardrails as versioned safety artifacts.