BR Academy

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

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

1Foundations u2013 LLM Capabilities and Limits0/3

LLM APIs and token economics15m
Hallucination, reasoning gaps, and when agents help
The planning loop, tool use, and memory
Module quiz

2Tool Use and Function Calling0/4

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

3Retrieval-Augmented Generation (RAG)0/5

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

4Memory, Planning, and Multi-Agent Orchestration0/4

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

5Evaluation, Cost Control, and Observability0/3

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

6Safety, Security, and Deployment0/4

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

7Capstone u2013 Build and Ship a Multi-Agent Service0/1

Design and deploy a live multi-agent system
Module quiz

Tool Use and Function Calling·Lesson 6 of 24·20 min

Wrapping APIs, databases, and code execution safely

This lesson is for enrolled learners

Concept A tool library is the set of functions you actually expose to the agent, and how you wrap them determines how safely and reliably the whole system behaves. The core principle: the LLM decides intent (which tool, what arguments), but your wrapper code enforces safety (what's actually allowed to happen). Never let a model-generated argument flow directly into a……

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