DAY 1 — Foundations of Designing AI Agents
Module 1: Introduction to Modern AI Agents
- From LLM applications to agentic systems
- Single-agent vs multi-agent patterns
- Agent maturity levels
- Core agent capabilities: perception, reasoning, acting, learning
Module 2: The Cognitive Loop & Agent Development Lifecycle
- Perceive → Interpret → Reason → Act → Learn
- Mapping cognition to implementation building blocks
- Agent development lifecycle: Requirements → Architecture → Build → Test → Deploy → Monitor
- Lab 1: Build a Minimal Cognitive Loop Agent
Module 3: Agent Architectures & Memory Systems
- Planner–Executor models
- Working memory and long-term memory
- Semantic memory via vector DBs
- Lab 2: Add Memory to an Agent
Module 4: The Art of Agent Prompting
- System, developer, user prompt separation
- Role/Persona engineering
- Chain-of-Thought and Tree-of-Thought prompting
- Lab 3: Prompt Engineering for Agents
DAY 2 — Advanced Architectures, Protocols & Deployment
Module 5: MCP & Agent-to-Agent Protocols
- The role of protocols in agent reliability
- MCP: tools, resources, schemas, contexts
- A2A: message envelopes, metadata, routing
- Lab 4: Build an MCP-Enabled Agent
Module 6: Multi-Agent Orchestration
- When multi-agent systems outperform single agents
- Planner–Executor–Verifier topologies
- Lab 5: Planner + Executor Multi-Agent Workflow
Module 7: Agentic Workflows
- Agent vs workflow vs hybrid models
- Human-on-the-loop and human-in-the-loop patterns
- Integrating agents into existing business processes
Module 8: Evaluating & Debugging Agents
- Tool-selection failures, hallucinations, broken plans
- Trace-based debugging workflows and behavioral test suites
- Lab 6: Debug a Misbehaving Agent
Module 9: Deploying Agents into Production
- Deploying as APIs via FastAPI
- Observability, logging, security hardening, and governance
- Lab 7: Deploy an Agent Using FastAPI
DAY 3 — Applied Workshop (“Choose Your Channel”)
Participants select a single track aligned with their professional role:
- Track A: The Data Analyst (BI Agent) — Build an agent that transforms raw data into insights using pandas, matplotlib/seaborn
- Track B: The Software Engineer (Coding Agent) — Build a test-driven code-generation agent with iterative refinement
- Track C: The Enterprise Operator (Service/Chat Agent) — Build a context-aware enterprise chatbot with RAG and escalation
Lab 8 (Capstone): Domain-Specific Deployment
- Package the agent into an API or deployment target
- Handle a surprise scenario introduced by the instructor
- Test, refine, and optionally demo your final solution
