AI Agents
Autonomous agents, LLM applications, and intelligent systems
CrewAI Multi-Agent Systems and Role-Based Automation in Python
Master CrewAI in Python for role-based multi-agent teams. Learn agent roles, sequential and hierarchical processes, tool calling, memory, and cost control.
How AI Meeting Summaries Work Under the Hood
Discover how modern AI meeting engines transcribe multi-speaker audio, execute acoustic diarization, and extract structured action items without hallucinations.
AI Agents Explained: What They Are and How They Actually Work
An AI agent is an LLM that can take actions and loop until a goal is achieved. The four components, the ReAct loop, what production agents actually do, and honest limits.
Multi-Agent Systems: When You Need More Than One AI Agent
Multi-agent systems coordinate specialized agents to handle tasks too complex for one agent. Four coordination patterns, real use cases, frameworks, and the hard problems that come with distribution.
How to Build an AI Agent: A Practical Guide for Developers
Building an AI agent requires an LLM with tool calling and a loop that runs until completion. Five steps with working code, common failure patterns, and when to use a framework vs. build from scratch.
Mastering Perplexity Sonar API for Real Time Web Grounding and Agentic Search
A complete engineering guide to integrating the Perplexity Sonar API, enabling real-time web grounding, live citation extraction, and high-precision agentic search.
Command R+: Cohere's RAG-Optimized LLM for Enterprise Search
Command R+ is purpose-built for RAG with inline citation generation, multi-step tool use, and 128k context. Here's how to implement grounded generation with source links.
Gemini 2.0 Flash: Google's Fastest Agentic Model
Gemini 2.0 Flash is 2x faster than 1.5 Flash with native tool use, a 1M token context, real-time multimodal streaming, and a thinking mode for hard problems.
ReAct: The Prompting Pattern That Powers Modern AI Agents
ReAct interleaves reasoning traces with actions, enabling LLMs to use tools while maintaining a reasoning chain - the foundational pattern behind LangChain agents and modern AI assistants.
LangGraph: Build Production-Ready LLM Agents That Don't Break
LangGraph replaces fragile LangChain agent chains with explicit state machines - giving you cycles, branching, persistence, and deterministic control flow.
Building Intelligent AI Agents with LangChain and GPT-4
Learn how to create autonomous AI agents that can reason, plan, and execute tasks using LangChain, GPT-4, and modern LLM orchestration techniques.
