The third bootcamp in our A.I. bootcamp series provides a complete, practical introduction to building modern AI agents that can reason, plan, use tools, retrieve information, collaborate, and operate safely in real-world settings. Learners begin by understanding what AI agents are and how they differ from traditional software and simple LLM applications, then explore core agent architectures, the modern AI agent stack, and the fundamentals of using LLMs as reasoning engines. Through guided exercises, participants build agents that use function calling, retrieval, vector databases, and memory systems to maintain context and act autonomously. The course also explores advanced capabilities such as multi-step reasoning, task planning, and multi-agent workflows enabling participants to create agents that decompose tasks, self-correct, and coordinate with other agents. The final modules focus on production readiness, including reliability, safety guardrails, evaluation, monitoring, and API-based deployment. By the end of the course, participants will develop a strong foundation for designing and deploying real-world agentic AI systems.

By the end of the course, participants will be able to:

1. Design and architect AI agent systems for real-world applications
2. Implement agents using modern frameworks and best practices
3. Build multi-agent systems with effective coordination
4. Implement memory and state management for agents
5. Deploy production-ready agents with proper guardrails and monitoring
6. Evaluate and iterate on agent performance

• Introduction to AI Agents
  •  What are AI Agents?
  • Definition and characteristics (autonomy, reactivity, proactivity, social ability)
  • Agents vs. traditional software vs. simple LLM applications
  • Real-world use cases and applications
  •  Exercise: Brainstorm and pick a real-world problem (e.g., customer support, research automation) and sketch what an agent could do. Share “pain points” for building such an agent in production. 
• Agent Architectures and AI Agent Stack
  •  Simple reflex agents, Model-based agents, Goal-based agents, Utility-based agents, Learning agents
  • Modern AI Agent Stack: LLMs as reasoning engines, Tools and function calling, Memory systems, Planning and orchestration
  • Hands-on: Build a simple agent and define its tools, memory, planning needs. 
• LLM Fundamentals for Agents
  • Language Models as Agent Brains - How LLMs work (high-level overview), Prompting strategies for agents, System prompts and role definition, Few-shot learning and in-context learning
  • Function Calling & Tool Use - Function calling API mechanics, Designing tool schemas, Handling tool outputs, Error handling and fallbacks
  • Hands-on: Build a simple LLM-powered assistant with function calling 
• Retrieval-Augmented Agents
  •  Why retrieval matters for grounding
  • Embeddings and vector databases
  • Query rewriting and retrieval planning
  •  Hands-on: Create embeddings from text; build a retrieval function + connect it to the agent, RAG agent that answers from the documents 
• Agent Memory Systems and Context
  • Types of Memory - Short-term memory (conversation context), Long-term memory (episodic and semantic), Working memory and attention mechanisms
  • Conversation Management - Context window management, Memory summarization, Relevance scoring
  • Using memory to maintain agent state across steps
  • Hands-on: Implement a memory system for an agent using vector embeddings 
• Multi-step Reasoning and Planning Agents
  • Chain-of-thought vs. chain-of-action
  • Planning algorithms in agent frameworks
  • Task decomposition, self-evaluation, reflection
  • Hands-on: Build a “Planner Agent” that breaks tasks into subtasks. Implement self-correction/reflection loop 
• Multi-Agent Systems
  • Multi-Agent Architectures - Hierarchical agents, Collaborative agents, Competitive agents, Communication protocols
  • Orchestration Patterns - Sequential execution, Parallel execution, Debate and consensus mechanisms
  • Workflow Agents (Supervisor + Workers)
  • Exercise: Create a two-agent system where agents collaborate to solve a problem 
• Production Considerations: Safety, Reliability, Evaluation and Deployment
  • Safety and Guardrails - Prompt injection prevention, Output validation, Tool access control, Sandboxing and security
  • Reliability and Error Handling - Retry strategies, Fallback mechanisms, Monitoring and logging, Cost management and rate limiting
  • Evaluation and Testing - Unit testing agents, End-to-end testing, Evaluation metrics (accuracy, latency, cost, Human-in-the-loop validation
  • Deployment Strategies - API design for agents, Streaming responses, Async processing, Scaling considerations
  • Exercise: Add guardrails and monitoring to a previous agent 
• Final Project & Wrap-Up