How to Safely Provide AI Tools with Actions and Functions In the rapidly evolving world of artificial intelligence, empowering AI tools with actions and functions—such as API calls, data processing,…
Task Decomposition in AI: Hierarchical Planning, Subgoals, and Scalable Workflows Task decomposition in AI is the practice of breaking a complex objective into structured, manageable subtasks that can be executed,…
Human-in-the-Loop for AI Agents: Governance, Workflows, Metrics, and Real-World Design Patterns Human-in-the-Loop (HITL) for AI agents refers to the deliberate insertion of human oversight, review, and decision-making into autonomous or…
Agentic AI for Customer Support: Autonomous, Safe, and Scalable Service Automation Agentic AI for customer support refers to autonomous, goal-driven AI systems that don’t just answer questions—they plan actions, consult…
Memory for AI Agents: Architectures, Retrieval Strategies, and Responsible Governance Memory for AI agents is the capability to persist, retrieve, and apply past information across interactions, enabling context-aware, personalized, and…
Future of Agentic AI: Autonomous AI Agents, Multi‑Agent Orchestration, and the Road Ahead Agentic AI refers to AI systems that don’t just respond—they plan, act, and learn across multiple steps…
Top Frameworks for Building AI Agents: LangChain, LlamaIndex, AutoGen, Semantic Kernel, and More AI agents are software systems that use large language models (LLMs) to perceive context, reason, take actions…
What Is Agentic AI? Definition, Architecture, Use Cases, and Safety Best Practices Agentic AI refers to AI systems that can pursue goals by planning, taking actions, and adapting based on…