DeepLearning.AI, Google Cloud and IBM Research · on DeepLearning.AI
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.
Developers and builders. This is an intermediate course in AI Agents & Developer Tools, running about ~1.5 hours (14 lessons, 8 code examples).
Free to audit. Videos are free with a DeepLearning.AI account; the graded quiz 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.
Andrew Ng's course on building agentic workflows in Python around four design patterns (reflection, tool use, planning and multi-agent collaboration), plus evaluation, error analysis and production deployment. DeepLearning.AI's most complete framework-agnostic agent course, for Python developers who want to design agents that work reliably.