Designing and Deploying AI Agents: Architectures, Protocols, and Case Studies

 Artificial Intelligence has entered the era of agentic systems—software entities capable of perceiving, reasoning, planning, acting, and learning. This course provides a rigorous and practical foundation for designing, building, and deploying modern AI agents in real-world environments. Over three days, participants will learn the core architectures and protocols behind intelligent agents, build agents that use tools, memory, retrieval, and multi-step reasoning, implement MCP and Agent-to-Agent (A2A) communication, debug, test, and deploy agentic systems, and apply skills to role-based real-world case studies in an applied workshop. This course blends theory, engineering practice, and hands-on development to create production-ready agent solutions.

Designing and Deploying AI Agents: Architectures, Protocols, and Case Studies Benefits

• Course Benefits

  • Organizations struggle to move beyond basic LLM integrations (chatbots, summarizers) to autonomous, multi-step agentic systems that can reason, plan, and use tools reliably in production environments
  • Developers and architects lack practical knowledge of emerging agent communication protocols (MCP, A2A) and multi-agent orchestration patterns, leading to fragile, unreliable agent pipelines
  • Teams face critical challenges in debugging, evaluating, and safely deploying agents—including hallucinations, broken plans, tool-selection failures, and lack of observability and governance frameworks

   Prerequisites

  • Experience with Python. Basic familiarity with APIs and JSON.
  • Comfort working in Linux/VM environments.
  • Familiarity with LLMs or ML concepts is helpful but not mandatory.

Designing and Deploying AI Agents Training Outline

Learning Objectives

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