The LangGraph SQL agent lets you query any database in plain English. Here is how it works, how to set it up, and the security considerations you cannot ignore.
A supervisor agent receives a task, breaks it down, delegates to specialist sub-agents, and collects results. Without one, multi-agent systems derail. Here is how it works.
A marketing strategy agent takes a brief and outputs a campaign strategy, content calendar, and channel plan. Here is how to build one that actually works.
Self-RAG is the pattern where an AI agent reflects on its own output and decides whether to retrieve more information. Here is how it works and when to use it.
A practical walkthrough of building a lead scoring agent using CrewAI – what data to feed it, how to define scoring criteria, and how to connect it to your CRM.
An honest comparison of the four main AI agent frameworks – what each is genuinely best at, where each falls short, and which to start with.
Stateless agents forget everything after each run. Teachable agents get better every time they run. Here is how to build memory into your agent stack.
Most RAG systems fail because of bad retrieval, not a weak model. Adaptive RAG, corrective RAG, and self-RAG explained for founders building AI products.
A supervisor agent that delegates to specialists sounds simple. Getting it right is the hard part. Here’s how to design hierarchical AI agent teams that actually work.
The plan-and-execute pattern separates planning from execution in AI agents – and it’s one of the most important architectural ideas for handling complex tasks. Here’s how it works.
