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:
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.
Linting
Linting is the automated analysis of code or any structured output to flag errors, enforce style rules, and catch problems before production.
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard from Anthropic that standardizes how AI models connect to external tools and data sources via a...