Advanced Architectures
Multi-agent systems, tool use, memory, and the frontier of autonomous AI
Session Topics
Multi-Agent Systems
Orchestrator-worker patterns, peer-to-peer agent networks, specialised agent roles, and how agents coordinate on complex tasks.
Tool Use & Function Calling
Giving agents access to search engines, databases, APIs, code interpreters, and the world — the bridge between language and action.
Memory Systems
Short-term (context window), long-term (vector stores), episodic (conversation history), and semantic (knowledge graphs) memory for AI agents.
Planning & Reasoning
ReAct (Reasoning + Acting), Tree-of-Thought, plan-and-execute architectures — how agents decompose complex goals into executable steps.
Agent Frameworks
LangChain, LangGraph, AutoGen, CrewAI — comparing frameworks by architecture philosophy, tooling, and production readiness.
Design Next-Generation Hiring AI Architecture
Design a cutting-edge architecture for your hiring AI using advanced multi-agent and tool-use techniques from Session 11. Justify architectural choices, estimate performance and cost, and compare to simpler alternatives.
A next-generation architecture design document with agent roles, tool definitions, memory strategy, and cost-performance trade-off analysis.
Key Takeaways
- Multi-agent systems are more capable than single agents on complex, multi-step tasks
- Tool use transforms LLMs from text generators into action-taking systems
- Memory architecture determines what an agent can 'remember' and reason about
- Planning is the skill that separates agentic AI from simple chat assistants