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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: Medium to Advanced Type: Individual Duration: 7 to 10 days Objective This assignment tests your ability to build the Client Agent side of agent discovery, design and operate a shared Agent Registry, and harden an agent discovery system against trust and security failures. By completing this assignment, you will have implemented the complete five-step Client Agent discovery workflow, built both exact and LLM-assisted skill matching,

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

Purpose In Assignment 1, you built the individual building blocks of a multi-agent system. In this assignment, you move beyond isolated concepts and design a complete, production-oriented orchestration engine  that can dynamically configure agent pipelines, select routing strategies based on system state, handle errors gracefully, and adapt to different workflow requirements at runtime. This assignment is closer to how multi-agent systems are actually designed in production e

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

ASSIGNMENT REQUIREMENT DOCUMENT Course Name:  Hybrid Search and Re-ranking — From Retrieval to Reliable Answers Institution:  [Institution Name] Semester:  [Semester / Term — e.g., Spring 2026] Instructor:  [Instructor Name] Student Level:  Postgraduate / Senior Undergraduate (Year 3–4) Submission Platform:  Moodle LMS Total Assignments:  2 Note to Students:  This assignment contains the complete requirements for one of the  course assignments . Read the entire document caref

Course:  LLM Observability — From Traces to Incident Response Chapters Covered:  7 – 10 (Trace-Based Debugging & Replay, Incident Response, LangSmith vs Langfuse in Real Teams, Final Lab & Packaging) Level:  Medium → Advanced Type:  Individual Assignment Duration:  7 – 10 days Prerequisite:   familiarity with trace schemas, metrics, and tracing concepts from Chapters 1–6 Objective By the end of this assignment you will be able to: Load, inspect, and diagnose production trace

Course:  LLM Observability — From Traces to Incident Response Chapters Covered:   1 – 6 (Why LLM Observability, Environment Setup, LangSmith Setup, Langfuse Setup, Instrumentation Design, Metrics & Dashboards) Level:  Medium → Advanced Type:  Individual Assignment Duration:  7 – 10 days Objective By the end of this assignment you will be able to: Articulate why traditional monitoring fails  for LLM applications and identify the observability gap. Design a Pydantic-based trace

Course:  Metadata Filtering Level:  Medium → Advanced Type:  Individual Assignment Duration:  7–10 days Objective The objective of this assignment is to help you: Build dynamic filter construction  driven by user context (role, department, organization) Implement query-time filter inference  — extracting metadata filters from natural language queries Design a hybrid search  system that combines keyword matching, vector similarity, and metadata filtering Enforce non-overridabl

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:  AI Backend Engineering with FastAPI  Assignment Type:  Applied Project Assignment  Difficulty Level:  Medium → Advanced  Estimated Time:  12–18 hours  Submission Mode:  Online LMS (Moodle / Canvas / Blackboard) Assignment Context Modern AI systems rarely consist of a single model endpoint. Instead, they involve multiple components working together such as: API orchestration model inference retrieval systems asynchronous processing background workflows logging and mon

Course:  AI Backend Engineering with FastAPI Assignment Type:  Capstone Implementation + Architecture Report Difficulty:  Medium → Advanced Estimated Effort:  10–15 hours Submission Platform:  LMS (Moodle / Canvas / Blackboard) Assignment Overview In this assignment, you will design and implement a production-style AI backend API  using the concepts introduced throughout this course. You will build a modular FastAPI system that integrates: LLM inference Retrieval-Augmented Ge