Session 5: Build AI-Powered Agents Fast with Foundry Local

Abstract

Design and orchestrate multi-role AI agents leveraging Foundry Local’s low-latency, privacy-preserving runtime. You’ll define agent roles, memory strategies, tool invocation patterns, and execution graphs. The session introduces scaffolding patterns you can extend with Chainlit or LangGraph. Starter project extends the existing agent architecture sample to add memory persistence + evaluation hooks.

Learning Objectives

• Define Roles: System prompts & capability boundaries
• Implement Memory: Short-term (conversation), long-term (vector / file), ephemeral scratchpads
• Scaffold Workflows: Sequential, branching, and parallel agent steps
• Integrate Tools: Lightweight function tool calling pattern
• Evaluate: Basic trace + rubric-driven outcome scoring

Prerequisites

• Sessions 1–4 completed
• Python with foundry-local-sdk, openai, optional chainlit
• Local models running (at least phi-4-mini)

Cross-Platform Environment Snippet

Windows:

py -m venv .venv
 .\.venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install foundry-local-sdk openai

macOS:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install foundry-local-sdk openai

If running agents from macOS against a remote Windows host service: