31 articles in this category
Agentic AI represents a fundamental shift from passive AI systems that wait for your commands to autonomous agents that set goals, plan multi-step tasks, and act independently. Unlike traditional chatbots, agentic AI systems perceive their environment, reason about complex problems, and take purposeful action with minimal supervision.
Multi-agent systems represent the next evolution in enterprise AI, where specialized agents work together to handle complex workflows. This advanced guide covers everything you need to understand agentic AI, from foundational concepts to production deployment with leading frameworks.
Learn how agent memory works in AI systems. This guide covers short-term vs long-term memory types, persistent storage approaches, episodic, semantic, and procedural memory, plus the leading tools and frameworks for building agents that actually remember.
Agent reflection lets AI agents critique their own output, learn from mistakes, and improve without retraining. This guide covers how the Reflexion pattern works, implementation methods, real-world use cases, and common failure modes to avoid.
Discover how agent planning and AI reasoning work together to help modern AI systems solve complex problems through task decomposition, deliberation, and advanced planning algorithms.
The agentic loop is the fundamental cycle that transforms AI from passive responders into active problem solvers. Learn how the think, act, observe pattern enables agents to reason through complex tasks, take actions, and adapt based on real-world feedback.
Goal-based agents pursue specific objectives while utility-based agents optimize decisions across multiple factors. Learn how each type works, their key differences, and when to use them in your AI projects.
Learn how goal-based agents work, why they outperform reactive AI systems, and where they're transforming industries from autonomous vehicles to healthcare.
Learn how tool use and function calling work in AI agents. This comprehensive guide covers JSON schemas, implementation across major LLM providers, and best practices for building effective agent tools.
AI assistants wait for your prompts and help complete tasks under your direction. AI agents work autonomously toward goals with minimal input. This comprehensive analysis breaks down when each makes sense for your workflow.
Confused about the difference between AI assistants, copilots, and agents? This guide breaks down the three types of AI helpers, their autonomy levels, and helps you choose the right tool for your workflow.
AI autonomy levels describe how independently an AI system can operate without human involvement. Learn about the five-level framework from basic assistants to fully autonomous agents, and discover which autonomy level fits your specific use case.