Agentic AI
Agentic AI describes systems designed to pursue goals autonomously—planning actions, using tools, and adapting based on feedback without waiting for human instruction at each step. The term distinguishes these systems from passive AI that only responds when prompted. An agentic AI system might monitor your analytics dashboard, notice a traffic drop, investigate the cause by checking server logs and search console data, draft a diagnosis, and propose a fix—all without being asked. The concept draws from decades of research in autonomous agents, but the current wave is powered by large language models that can reason about tool use in natural language. The gap between agentic AI in demos and agentic AI in production is significant. Demos show the happy path. Production requires handling failures gracefully, knowing when to escalate to a human, and operating within security and compliance boundaries that most agent frameworks were not designed for.
Related terms:
Chain-of-Thought
Chain-of-thought prompting, introduced by Google Research in 2022, transforms AI from an answer machine into a reasoning partner by explicitly modeling the...
Pace Layers
Pace layers separate complex systems into fast-changing parts that learn and slow-moving parts that stabilize, helping teams choose experiments vs. strategy.
RLHF
Reinforcement Learning from Human Feedback (RLHF) trains a reward model on human preference comparisons and uses reinforcement learning to align language...