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:
Generative AI
Generative AI refers to AI systems that learn statistical patterns from training data to create new content—such as text, images, code, audio, or video—rather than classifying or analyzing existing data. This marks a shift from earlier discriminative models like spam filters and recommendation engines, with tools like ChatGPT, DALL-E, Midjourney, and Stable Diffusion driving its rapid mainstream adoption.
Private Tokens
Proprietary organizational data and institutional knowledge that generic AI can’t access—encompassing conversational transcripts, internal documentation, digital communications, and unwritten tribal wisdom. When integrated into custom AI systems, these private tokens deliver unique customer insights, brand voice patterns, and strategic intelligence to power competitive marketing automation.
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 problem-solving process step by step. By decomposing complex queries into sequential reasoning steps and making implicit thinking explicit, it fundamentally improves AI performance.