AI Agents
Autonomous agents, LLM applications, and intelligent systems
5 min readLangChain Complete Guide 2026: When to Use It and When Not To
LangChain is a powerful LLM framework, but its complexity is frequently criticized. Here is when it genuinely helps and when you should skip it entirely.
11 min readHow 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.
10 min readMulti-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.
9 min readAI Agents vs AI Assistants: What's the Actual Difference?
Assistants respond to requests. Agents pursue goals autonomously. The technical differences, when you actually need an agent vs. an assistant, and an honest 2026 state-of-the-art.
10 min readAI 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.
7 min readPerplexity Sonar Online: The Search-Augmented LLM API for Real-Time Data
Perplexity's Sonar API returns LLM-generated answers with inline citations from live web search - an OpenAI-compatible endpoint that replaces custom RAG pipelines for real-time data retrieval use cases.
7 min readCommand 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.
8 min readLLM Function Calling: OpenAI, Anthropic, and Gemini Side by Side
Function calling lets LLMs invoke your code with structured arguments - here is the complete guide with parallel calls, error handling, and cross-provider format differences.
7 min readGemini 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.
9 min readReAct: 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.
8 min readLangGraph: 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.
8 min readAutoGen: Microsoft's Framework for Conversational Multi-Agent AI
AutoGen enables multi-agent AI through structured conversation between specialized agents, with built-in code execution, group chat, and a no-code Studio interface.
8 min readCrewAI: Build Multi-Agent Teams Where Each Agent Has a Role and Goal
CrewAI structures multi-agent AI around roles, goals, and backstories - giving each agent a defined identity and letting them collaborate on complex tasks autonomously.