AI Solution Delivery
Master AI solution delivery for enterprise clients. Covers client discovery, solution scoping, SOW writing, rapid prototyping, customer data integration, deployment in customer environments, stakeholder communication, delivery risk management, POC to production transition, knowledge transfer, post-delivery support, and a full delivery capstone. All labs use hosted model SDKs (OpenAI, Gemini, Anthropic) via proxy URLs on K8s.
Learning Path
8 phases • 12 chaptersFoundations
Python essentials and development environment for agent development
Tools & Topics
Virtual environments, async programming, type hints, Pydantic, error handling, testing, debugging, logging, project structure
Goals
- •Set up professional development environments
- •Write async Python code fluently
- •Use type hints and Pydantic for robust data handling
- •Implement error handling, testing, logging, and debugging
Chapters
LLM Fundamentals
Core LLM concepts: API clients, token economics, caching, and function calling basics
Tools & Topics
LLM APIs, OpenAI/Anthropic/Gemini clients, prompt caching, token economics, function calling basics
Goals
- •Call multiple LLM providers (OpenAI, Anthropic, Gemini)
- •Implement prompt caching and token cost management
- •Build function calling and tool definitions
- •Understand token economics and cost optimization
Chapters
Agent Fundamentals
Agent patterns: ReAct, planning, tool execution, sandboxing, web navigation, and MCP protocol
Tools & Topics
ReAct loop, planning patterns, tool execution, sandboxing, web navigation, MCP servers, MCP clients, tool routing
Goals
- •Create agent loops with ReAct and planning patterns
- •Build and consume MCP servers for tool integration
- •Implement sandboxing and web navigation
- •Design structured outputs and prompts
Chapters
Agent State & Memory
Memory systems, RAG patterns, context optimization, and LangGraph state machines
Tools & Topics
Short-term memory, long-term memory (RAG), agentic RAG patterns, semantic memory, context optimization, state graphs, conditional edges, checkpointing, human-in-the-loop, streaming, subgraphs
Goals
- •Implement short-term and long-term memory
- •Build RAG and agentic RAG systems
- •Create state machines with LangGraph
- •Implement checkpointing, streaming, and human-in-the-loop
Chapters
Multi-Agent Systems
Multi-agent patterns, guardrails, evaluations, and observability
Tools & Topics
Supervisor pattern, hierarchical pattern, reflector pattern, input guardrails, output guardrails, prompt injection defense, evaluations, benchmarking, tracing, observability
Goals
- •Implement supervisor, hierarchical, and reflector patterns
- •Build input and output guardrails
- •Defend against prompt injection attacks
- •Evaluate agents with benchmarks
Chapters
Production & Operations
Production deployment: APIs, containers, databases, scaling, CI/CD, and monitoring
Tools & Topics
FastAPI, Docker, production databases, scaling, CI/CD, monitoring, alerting, model routing, fallbacks, system design
Goals
- •Serve agents via FastAPI with Docker
- •Deploy to Kubernetes with CI/CD
- •Monitor with Prometheus/Grafana
- •Build multi-tenant agent platforms
Chapters
Advanced Topics
Alternative frameworks, protocols, specialized agents, autonomous workflows, and cutting-edge capabilities
Tools & Topics
CrewAI/AutoGen, A2A protocols, GraphRAG, local models, vision agents, voice agents, code agents, autonomous workflows, streaming data, agent swarms
Goals
- •Use alternative frameworks (CrewAI, AutoGen)
- •Implement A2A protocol for agent communication
- •Build GraphRAG for complex knowledge
- •Build vision, computer use, and voice agents
Chapters
Agent Production Excellence
Production excellence: trajectory evaluation, safety, cost control, enterprise patterns, and governance
Tools & Topics
Agent trajectory evaluation, safety boundaries, cost control, enterprise agent patterns, load testing, versioning, fleet dashboards, autonomous agent governance
Goals
- •Score multi-step agent reasoning with LLM-as-judge pipelines
- •Build safety boundaries with permissions and kill switches
- •Implement per-agent cost budgets and cost-aware routing
- •Deploy enterprise agent patterns for document processing and code review