DeepLearning.AI and LangChain · on DeepLearning.AI
Introduces four loops around an AI agent (the agent loop, verification loop, event-driven loop and hill-climbing loop) and adds each in turn to a requirements-review agent built with LangChain Deep Agents. For developers who already have a working agent and want systematic ways to make it more reliable over time.
Developers and builders. This is an intermediate course in AI Agents & Developer Tools, running about ~1.3 hours (8 video lessons, 4 code examples).
Free to audit. Videos are free with a DeepLearning.AI account; the two graded assignments and accomplishment require DeepLearning.AI Pro ($25/month billed annually or $30/month billed monthly). Price seen 2026-11-01.
An influential essay series, modelled on the 12-Factor App, arguing for owning your prompts, context window and control flow, treating tools as structured outputs, and keeping agents small, stateless and pausable. It suits engineers moving agents from demo to production who want design principles rather than another framework tutorial.
Google and Kaggle's intensive pairs a daily whitepaper with hands-on codelabs on foundation models, embeddings, agents, domain-specific LLMs, and MLOps using Gemini and the Agent Development Kit. For developers who want a dense, free bootcamp-style sprint through Google's GenAI stack.
Builds a healthcare multi-agent system from agents written in different frameworks, wraps each in an A2A server, builds A2A clients, and orchestrates them in sequential and hierarchical workflows using Google ADK, LangGraph, BeeAI and Microsoft Agent Framework. For developers who need agents from different teams or vendors to discover and talk to each other.