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
Learn how to build an AI agent that tracks construction project milestones, processes photo documentation, sends delay alerts to stakeholders, and generates automated progress reports.
Create a multi-stage content publishing agent that drafts articles, routes them through AI reviewer agents, tracks versions, manages edits, and publishes to a CMS via API.
Learn how to build an AI agent that parses legal contracts, extracts key clauses, scores risk levels, and generates executive summaries using Python and LLMs.
Design structured conversational flows with the OpenAI Agents SDK including state machines, slot filling, context tracking, and graceful conversation control for multi-turn interactions.
Design and implement a full Conversations API for AI agent chat sessions. Covers resource modeling, conversation lifecycle, message threading, metadata management, and FastAPI implementation patterns.
Build an AI agent that analyzes cloud resource usage across AWS, GCP, and Azure, identifies waste, recommends rightsizing, and suggests reserved instance purchases for maximum savings.
Learn how to build an AI agent for court systems that provides case lookup, hearing date information, filing requirements, and attorney resources while maintaining strict accuracy standards for legal information.
Build an AI agent that reacts to CRM webhook events to score leads, automate follow-ups, and trigger notifications when deals progress through your sales pipeline.
Create an AI agent that accepts CSV file uploads, uses pandas for statistical analysis, answers natural language questions about the data, and generates clear reports with charts — all without the user writing any code.
