BR Academy

Building AI Agents: Tool Use, RAG and Multi-Agent Systems

Master production AI agents: tool use, RAG, multi-agent orchestration, safety, and deployment with Python and TypeScript.

Intermediate24 lessons8 hoursCertificate

About this course

You know LLMs. Now build agents that actually work in production.

Most AI agents fail because they hallucinate, waste money on tokens, or break under load. This course teaches you to ship reliable, cost-controlled agents that solve real problems.

You’ll learn function calling and structured outputs to ground LLM reasoning in deterministic logic. You’ll design RAG pipelines that reduce hallucination and ground agents in your data. You’ll orchestrate multiple specialized agents with planning loops and memory. You’ll evaluate performance, control costs, defend against prompt injection, and deploy as a scalable service with observability.

By the end, you’ll have built and shipped a live multi-agent system—customer support, research, or data analysis—with tool integration, RAG, multi-agent routing, cost tracking, and production monitoring.

For developers and technical founders who ship Python or TypeScript services and want to move beyond toy chatbots to production AI agents.

What you will be able to do

Can implement function calling and structured outputs to ground LLM responses in deterministic logic
Can design and build a RAG pipeline: chunking, embedding, vector storage, retrieval, and reranking
Can architect multi-agent systems with planning loops, memory, and handoff protocols
Can evaluate agent performance with metrics (accuracy, latency, cost per call) and build harnesses
Can deploy an agent as a scalable service with observability, rate limiting, and cost tracking
Can defend against prompt injection, jailbreaks, and unsafe outputs with validation layers
Can integrate real tools: APIs, databases, code execution, web search, and knowledge bases

Curriculum 7 modules · 24 lessons

1Foundations u2013 LLM Capabilities and Limits3 lessons

LLM APIs and token economics Preview15 min
Hallucination, reasoning gaps, and when agents help 15 min
The planning loop, tool use, and memory 15 min

2Tool Use and Function Calling4 lessons

OpenAI, Anthropic, and open-source function schemas 18 min
Forcing JSON responses and validating agent decisions 18 min
Wrapping APIs, databases, and code execution safely 20 min
Sequential, parallel, and conditional tool chains 18 min

3Retrieval-Augmented Generation (RAG)5 lessons

Semantic, sliding-window, and recursive chunking 18 min
Choosing models, dimensionality, and similarity search 18 min
Pinecone, Weaviate, Milvus, and local alternatives 18 min
BM25, dense retrieval, and cross-encoder reranking 20 min
Measuring retrieval quality and end-to-end accuracy 18 min

4Memory, Planning, and Multi-Agent Orchestration4 lessons

Short-term, long-term, and episodic memory patterns 18 min
ReAct, chain-of-thought, and tree-of-thought 20 min
Hierarchical, peer-to-peer, and market-based orchestration 20 min
Routing decisions, context passing, and state management 18 min

5Evaluation, Cost Control, and Observability3 lessons

Unit tests for agents, golden datasets, and metrics 20 min
Token budgets, caching, batching, and model selection 18 min
Tracing agent calls, debugging failures, and monitoring drift 18 min

6Safety, Security, and Deployment4 lessons

Attack vectors and input validation strategies 18 min
Filtering unsafe responses and enforcing constraints 18 min
FastAPI, async patterns, rate limiting, and scaling 20 min
Monitoring, alerting, cost tracking, and user feedback loops 18 min

7Capstone u2013 Build and Ship a Multi-Agent Service1 lesson

Design and deploy a live multi-agent system 60 min

Taught by Block Relations

Research & Education · Built for a full-stack engineer or technical founder who ships Python/TypeScript services, understands LLM basics (prompts, tokens, APIs), and wants to architect agents that reliably solve real problems—not toy chatbots. They need to reduce hallucination, control costs, orchestrate multiple specialized agents, and deploy safely to production.