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Agentic AI refers to AI systems that can autonomously pursue goals, make decisions, and take actions without constant human supervision. These systems can plan, execute, evaluate results, and adjust their approach based on outcomes.

Unlike simple question-answer AI, agentic systems can break down complex tasks, use tools, gather information, and iterate until they achieve their objectives. They exhibit goal-directed behavior and can handle unexpected situations adaptively.

For AI builders, agentic AI represents the evolution from AI assistants to AI agents that can handle complete workflows. Building agentic systems requires careful consideration of safety, reliability, and monitoring to ensure they operate within intended boundaries.

AgenticOps encompasses the practices, tools, and processes for deploying, monitoring, and maintaining AI agents in production environments. It's the AI equivalent of DevOps, focusing on reliability, observability, and continuous improvement of autonomous AI systems.

Key aspects include agent monitoring, performance tracking, error handling, safety controls, and iterative improvement based on production data. It ensures AI agents operate within defined parameters while maintaining effectiveness and reliability.

For AI builders, AgenticOps is essential for scaling from experimental agents to production systems. It involves implementing logging, metrics, alerts, and controls to ensure agents perform reliably and safely in real-world conditions.

An AI agent is an autonomous system that perceives its environment, makes decisions, and takes actions to achieve specific goals. Unlike traditional AI that responds to prompts, agents can initiate actions, use tools, and work independently toward objectives.

AI agents combine language understanding with the ability to interact with external systems, APIs, and tools. They can perform multi-step tasks like research, data analysis, or process automation without step-by-step human guidance.

For AI builders, agents represent a paradigm shift from building AI-powered features to creating AI colleagues that can handle entire job functions. This requires robust error handling, monitoring, and clear boundaries to ensure reliable, safe operation.

AI hallucination occurs when an AI model generates information that sounds plausible but is actually false or nonsensical. The AI confidently presents made-up facts, incorrect dates, fictional citations, or impossible scenarios as if they were true.

This happens because AI models predict the most statistically likely next words based on patterns in training data, not by verifying facts. They can mix up information, create fictional entities, or generate technically impossible code that looks correct.

For AI builders, understanding and mitigating hallucinations is crucial. Techniques include using RAG to ground responses in real data, implementing fact-checking mechanisms, and designing prompts that reduce the likelihood of hallucinations.

An AI workflow is a sequence of connected steps where AI processes data, makes decisions, and triggers actions to complete complex tasks. It combines multiple AI capabilities, tools, and logic to automate end-to-end processes.

Workflows can include AI-powered steps like content generation, data extraction, and decision-making, along with traditional automation like data transformation, API calls, and conditional logic. They enable building sophisticated applications that handle real-world complexity.

For AI builders, workflows are the blueprint for production AI systems. They provide structure, reliability, and observability for AI operations, turning experimental AI capabilities into dependable business processes.