Practical, code-first guides to designing, building, and shipping AI agents and multi-agent systems. Start with a framework, then move on to architecture and reliability patterns for production.
Start here
- BeeAI Framework tutorial: build Python AI agents step by step — agents, tools, workflows, structured outputs, and multi-agent apps.
- watsonx Orchestrate tutorial: build a multi-agent system step by step — agentic AI with IBM watsonx Orchestrate.
- Universal A2A Agent tutorial — connect agents across frameworks with the Agent-to-Agent protocol.
Architecture & reliability
- Why AI agents need a kernel, not a framework — a deeper look at agent runtimes.
- Deploying GenAI without hallucinations — grounding, evaluation, and guardrails for production.
Applied agents
- AI Medical Chatbot — a domain-specific assistant end to end.
- watsonx Assistant with Milvus as a vector database — retrieval-augmented agents.
Related topics
Explore IBM watsonx, Hugging Face & LLMs, and Generative AI. For daily ranked AI repositories, papers, and packages, see the AI Rankings.
FAQ
What is the best framework to start building AI agents? For Python developers, the BeeAI Framework is a friendly starting point; for enterprise/IBM stacks, use watsonx Orchestrate.
How do I make agents from different frameworks talk to each other? Use the Agent-to-Agent (A2A) protocol — see the Universal A2A Agent tutorial.