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:
AI Governance
AI governance comprises the policies, processes, and technical controls that organizations use to manage the risks of AI deployment, from deciding appropriate use cases and evaluating models to ensuring accountability, data privacy, bias mitigation, and regulatory compliance under frameworks like the EU AI Act. Without clear governance, “shadow AI” can proliferate as employees use unmonitored AI tools with no oversight or audit trails.
Multimodal AI
Multimodal AI refers to models that process and generate multiple data types—text, images, audio, and video—within a single system. By integrating modalities internally, it can, for example, read a photo, transcribe speech, and respond in text in one call.
Temperature
Temperature is a parameter controlling a language model’s randomness: at 0 it always picks the most probable next token for deterministic, reliable output, at 1 it samples more broadly for varied, creative results, and above 1 it becomes increasingly random. Choosing the right temperature (e.g., 0 for consistent data extraction or 0.7–0.9 for brainstorming) balances reliability and diversity.