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Assignment Overview Scenario: You are a research engineer at an academic institution building tools to help researchers manage and analyze scientific literature. Your task is to create an advanced MCP server that not only provides access to research papers but also uses AI sampling (server-initiated LLM calls) to generate intelligent summaries, extract key findings, and compare papers. This assignment builds on Assignment 1 by adding Module 4 concepts: sampling, production pa

Assignment Overview Scenario: You are a software engineer at a productivity software company. Your team is developing an AI-powered personal assistant that helps users manage their daily tasks, projects, and goals. Your task is to build an MCP server that provides intelligent task management capabilities to Claude Desktop, allowing users to interact with their task lists using natural language. Learning Objectives: Implement MCP tools for CRUD operations on task data Design a

Purpose In Assignment 1, you built the core protocol and task-handling pieces. In this assignment, you will extend that foundation into a more realistic system that supports streaming responses, multi-turn conversation continuity, framework integration, observability, and production-minded orchestration. This assignment draws heavily on Chapters 6–7 and asks you to show how FastA2A behaves when it is used as the coordination layer for real application workflows. Connection to

Introduction During a disaster, incident commanders must process conflicting data from dozens of sources, coordinate resources across multiple agencies, and make life-safety decisions under extreme time pressure. Traditional tools and static decision trees cannot keep pace with rapidly evolving, multi-agency incidents. AI-Powered Emergency Response Agents built on Retrieval-Augmented Generation (RAG) address this by continuously retrieving real-time situational data, historic

Purpose This assignment requires you to build a working multi-agent service using the FastA2A design principles covered in Chapters 1–5. You will implement agent identity, capability metadata, structured messages, tasks, context handling, routing, and a basic request/response workflow that mirrors the FastA2A protocol model. Your solution should demonstrate how a real agent system moves from discovery to execution and then back to the client with a stable, inspectable task li

Introduction Programming industrial robots requires mastery of multiple proprietary controller languages, motion planning algorithms, safety standards, and constantly evolving vendor documentation. This knowledge burden slows deployments, escalates costs, and creates dangerous skill gaps across automation teams. Robot Programming Assistance Systems powered by Retrieval-Augmented Generation (RAG) address this by dynamically retrieving relevant vendor documentation, safety stan

Duration: 10–14 days following Assignment 1 Type: Individual Assignment Difficulty Level: Advanced Marks: 100 This assignment requires you to create an original, domain-specific cost optimization strategy for a real-world LLM application that you research or design. Unlike Assignment 1 (general framework), Assignment 2 demands independent research, creative problem-solving, and domain expertise. Learning Objectives Research real LLM cost challenges in a specific industry. Des

Course: LLM Cost Engineering — From Token Economics to Production Monitoring Student Level: Undergraduate Year 3 / Postgraduate Submission Platform: Moodle (Learning Management System) Individual / Group: Individual Assignments Purpose This assignment requires you to design and implement a comprehensive cost optimization framework for a real or hypothetical LLM-powered application. You will leverage every core concept introduced across Chapters 1–9: understand token economics

Course: Agent Discovery & Agent Cards Level: Beginner to Medium Type: Individual Duration: 5 to 7 days Objective This assignment tests your understanding of why agent discovery exists, what every field in an Agent Card communicates, and how to publish a valid, discoverable Agent Card from an A2A server. By completing this assignment, you will have written Agent Cards from scratch, identified and fixed common authoring mistakes, served cards via both FastAPI and FastA2A, and b

ASSIGNMENT REQUIREMENT DOCUMENT Course: Agent-to-Agent (A2A) — Multi-Agent Systems in Python Student Level: Undergraduate Year 3 / Postgraduate Submission Platform: Moodle (Learning Management System) Individual / Group: Individual Assignments Total Assignments: 2 This document contains the full specifications for Assignment 1 . Read every section carefully before you begin. You will be assessed on the quality of your implementation, the depth of your analysis, and the c

Purpose In this assignment, you move beyond isolated chunking techniques to design a complete, adaptive chunking system that intelligently detects document types and selects or combines chunking strategies accordingly. This simulates how chunking is actually deployed in production RAG systems — not as a fixed function, but as a design decision that adapts to input characteristics. Connection to Course Learning Outcomes (CLOs) CLO Description Relevance CLO 1 Identify structu

Course: Metadata Filtering Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Objective The objective of this assignment is to help you: Understand why metadata filtering is essential for production RAG systems Design a metadata schema for a real-world knowledge base Implement metadata-preserving chunking so that chunk-level metadata is never lost Build and apply pre-filters using ChromaDB's filter syntax Compare pre-filtering vs post-filtering a

Course: LLM Foundational Course Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Total Marks: 100 Objective The objective of this assignment is to help you: Implement conversation memory that manages context windows Control LLM output using temperature, max_tokens, and stop sequences Build a complete agent that combines conversation history with semantic search Handle multi-turn conversations with proper context management Track usage and costs

Course: LLM Foundational Course Level: Medium Type: Individual Assignment Duration: 5–7 days Total Marks: 100 Objective The objective of this assignment is to help you: Understand tokenization and how it affects API costs Implement token counting and cost calculation functions Build a vector database from scratch using embeddings Perform semantic search to retrieve relevant documents Create a simple RAG system that answers questions using retrieved context Think pra

Course: MCP Fundamentals Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Objective The objective of this assignment is to help you: Understand advanced agentic MCP capabilities (Sampling, Elicitation, Roots) Design multi-agent systems with appropriate orchestration patterns Analyze security implications of agentic workflows Implement human-in-the-loop design patterns Reason about long-running workflows and error handling Think critically about prod

Course: MCP Fundamentals Level: Medium Type: Individual Assignment Duration: 4–5 days Objective The objective of this assignment is to help you: Understand the architectural problem MCP solves and why earlier approaches failed Master the distinction between Tools, Resources, and Prompts Apply primitive selection logic to real-world integration scenarios Design MCP Server architectures with appropriate primitives Analyze trade-offs in transport mechanisms and deployment m

Objective In this assignment, you will move beyond isolated chunking techniques and design a complete, adaptive chunking system that intelligently selects or combines strategies based on the input document type. This is closer to how chunking is actually used in production systems. Problem Statement Most tutorials treat chunking strategies independently: Fixed-size chunking Overlapping chunking Sentence-based chunking Token-aware chunking Semantic chunking However, in real-w

Course: Chunking Strategies for Production RAG Systems Level: Medium → Advanced Type: Individual Assignment Duration: 5–7 days Objective The objective of this assignment is to help you: Understand and implement multiple chunking strategies Analyze trade-offs between different approaches Design a hybrid chunking pipeline Evaluate chunking quality in a Retrieval-Augmented Generation (RAG) context Think like an engineer building production-ready systems Problem Statement You a