Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build an AI agent that aggregates logs from multiple services, extracts patterns using clustering and LLM analysis, correlates events across systems, and generates actionable alerts.
Build an AI agent that helps customers check loyalty points balances, browse reward catalogs, redeem rewards, and understand how to earn points faster — increasing program engagement and customer retention.
Build an AI-powered market research agent that gathers data from multiple sources, performs competitive analysis, detects industry trends, and generates structured reports with visualizations for strategic decision-making.
Build an AI agent that explains medical and dental bills in plain language, processes secure payments, sets up payment plans for patients, and handles billing dispute workflows with full Python implementation.
Learn how to build an AI agent that manages member registrations, automates renewal reminders, tracks engagement scores, and personalizes communication for community organizations.
Explore the three types of memory that AI agents use — short-term, long-term, and episodic — with practical Python implementations and guidance on when to use each type.
Build an AI agent for moving companies that generates accurate quotes from room-by-room inventories, estimates cubic footage, assigns crews, and provides real-time customer updates on move day.
Create an AI agent for online learning platforms that handles student onboarding, monitors progress, detects when learners are stuck, and provides targeted help resources.
Learn how AI agent operating systems orchestrate, schedule, and manage large fleets of digital workers. Understand the OS-level abstractions — process management, resource allocation, and inter-agent communication — that make scalable agent deployment possible.
