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Conversational AI & Agents

AI agents, multi-agent systems, LangChain, Dialogflow, voice interfaces, A2A protocol, and orchestration patterns.

45 concepts4 questions10 projects

Overview

Conversational AI and AI Agents represent the cutting edge of applied AI — building systems that can interact naturally with users, reason about complex tasks, and take autonomous actions using tools and APIs.

Key areas include chatbots and voice agents (Dialogflow, voice synthesis with ElevenLabs, multi-turn conversation management), AI agents (ReAct pattern, tool use, planning and execution), multi-agent systems (orchestration patterns, A2A protocol, AutoGen, CrewAI), and agent frameworks (LangChain, LangGraph, Google ADK).

Important concepts include agent architectures (single-agent vs. multi-agent), tool calling and function execution, memory management (short-term and long-term), safety and guardrails, and evaluation of agent behavior. This is a rapidly evolving field where the ability to build reliable, production-grade agents is highly valued.

Conversational AI & Agents

Overview

ML Concepts

AI Orchestration: How do you coordinate multiple AI agents in workflows?

Deep-Dive Concepts (from Projects)

How Embeddings Work

Vector Similarity Explained

Chunking Strategies Compared

Two-Stage Retrieval Architecture

Prompt Engineering for RAG

Evaluating RAG Systems

Multi-Agent Architecture

LangGraph State Machines

Agent Communication Patterns

Tool Integration and Error Handling

LangSmith Observability: Complete Guide

Production Optimization

Model Context Protocol (MCP) Explained

AI Voice Agent Architecture

Tool Orchestration in Multi-Tool AI Systems

Webhook Design for AI Agents

Client-Side Tool Integration for Voice Agents

Production Voice Agent Deployment

AWS Bedrock Architecture Deep-Dive

RAG with Bedrock Knowledge Bases

Content Safety with Bedrock Guardrails

Gemini 1.5's 2M Token Context Window

Multi-Cloud AI Abstraction Patterns

Azure OpenAI Service Architecture

Three-Cloud Decision Framework

Cost Optimization Strategies for Cloud AI

MCP Protocol Deep Dive

Copilot Studio Architecture

Power Platform Security Model

AI Builder Capabilities

Dataverse for AI Workloads

Low-Code AI Governance

The A2A Protocol Architecture

Agent Discovery and Capability Negotiation

AutoGen Architecture: Conversation-First Design

Human-in-the-Loop Patterns

Document Processing Pipelines

Multi-Agent vs Single Agent Systems

Verification Confidence Scoring

The Agent Loop

Designing Effective Agent Tools

Multi-Agent Orchestration Patterns

NLU Pipeline: From Text to Intent

Containment Rate: The Key CCAI Metric