AI engineering: Agents

Days

Hours

Minutes

Seconds

Work with reasoning models to build production-grade AI agents that can autonomously tackle complex workflows, from deep analysis to support agents. Learn to architect and evaluate robust systems that combine LLMs, tools, memory, and planning to ship real-world applications.

3 Hours

Free

Learnings & outcomes

Design reliable agent architectures combining tool integration, memory systems, and autonomous workflows
Master multi-agent collaboration through orchestrator-worker patterns and structured hand-offs
Evaluate agent performance across accuracy, latency, and cost using reproducible benchmarking methods
Implement safeguards against prompt injection, infinite loops using validation and human in the loop

Curriculum

Why “Agent” is a slippery term
Foundations—from single-step LLMs to key building blocks
Memory, context, and retrieval
Key patterns for agentic workflows—tool use, planning, and correction
Single-agent arhitectures—balancing tools, memory, and autonomy
Multi-agent collaboration
Guardrails, safety, and prompt injection
Evaluation and benchmarking
Production considerations

In partnership with

Course Instructors:

Ilan Bigio

OpenAI

Applied AI

I graduated from Brown University, spent 3 years at Google, and am currently working at OpenAI. I have a passion for teaching and learning, and spend an astounding amount of time on my pet projects.

Bharat Ramanathan

Weights & Biases

AI Engineer

Bharat is an AI Engineer at Weights & Biases, where he built and manages Wandbot, a technical support bot that can run in Discord, Slack, ChatGPT and Zendesk. Currently also pursuing a Data Science Master's at Harvard Extension School. Bharat is an outdoor enthusiast who enjoys reading, rock climbing, swimming, and biking.

Anish Shah

Weights & Biases

AI Engineer

Anish loves turning ML ideas into ML products. Anish started his career working with multiple Data Science teams within SAP, working with traditional ML, deep learning, and recommendation systems before landing at Weights & Biases. With the art of programming and a little bit of magic, Anish crafts ML projects to help better serve our customers, turning “oh nos” to “a-ha”s!

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AI engineering: Agents

Learnings & outcomes

Curriculum

Ilan Bigio

Bharat Ramanathan

Anish Shah

LLM apps: Evaluation

RAG++ : From POC to production

Developer’s guide to LLM prompting

LLM engineering: Structured outputs

Building LLM-powered apps

Training and fine-tuning LLMs

Weights & Biases 101: Weave

The Platform

Article

Resources

Company

Use cases

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