AI-CPT_S-446-paul.wilmoth-2026-09-23-11-10-47
Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.
Title of Course [Agentic AI]
Prefix and Number [AI / CPT_S 446]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites [AI 240 with a C or better; CPT S 321, 322, or 355 with a C or better; junior standing. ]
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [click here for best practices] [tbd]
Course Description
This course examines the design and development of software systems using agentic artificial intelligence. Students progress from AI-assisted development workflows to the design, orchestration, and coordination of AI agents that can participate in complex software development tasks. Topics include AI orchestration, agent-based system architecture, multi-agent coordination patterns, production agent pipelines, tool integration, Model Context Protocol (MCP), project planning, and the evaluation and integration of AI tools within software systems. Students apply these concepts through hands-on development activities spanning areas such as game development, web development, mobile development, and data science and machine learning. Emphasis is placed on selecting appropriate AI tools, structuring interactions among agents and external tools, developing reliable workflows, and integrating agentic capabilities into larger software projects.
The course uses an iterative, project-based development model. Early assignments develop foundations in AI-assisted programming, hybrid AI architectures, agent coordination, production pipelines, and MCP-based integration. Students then apply these techniques within a specialization track and develop a larger project through planning, implementation, peer review, and repeated development sprints. The course concludes with project presentation, cross-track analysis, and reflection on the use of agentic AI within the software development process.
Course Materials
Books:
Required Textbook: None. Course readings, technical materials, assignment instructions, and other resources will be provided through Canvas and the course GitHub repository.
Recommended Books: None.
Other Materials: [Students will use AI-assisted development tools, software development environments, Git and GitHub, and other tools or frameworks introduced during the course. Access requirements and technical instructions for specific AI tools, development platforms, Model Context Protocol (MCP) components, and project environments will be provided by the instructor through Canvas or the course GitHub repository]
Fees: [None]
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
|
Apply AI-assisted and agentic AI tools to support software design, implementation, testing, and development workflows (1e, 6a). |
Lectures, demonstrations, hands-on programming activities, and foundation assignments |
Programming assignments, project submissions, and peer review |
|
Design and implement software systems that integrate AI tools, agents, and conventional software components to address defined project requirements (2a, 2g, 6a). |
Architecture exercises, system-design activities, implementation assignments, and project development |
Design submissions, code submissions, and project milestones |
|
Analyze and apply orchestration and multi-agent coordination patterns for agentic software systems (1e, 6a). |
Agent orchestration exercises, architecture discussions, multi-agent programming activities, and implementation workshops |
Multi-agent assignments, technical reviews, and project implementation |
|
Evaluate and select AI tools, models, agent configurations, and integration approaches based on project requirements and technical constraints (1d, 2e). |
Tool comparison activities, design discussions, peer reviews, and specialization-track exercises |
Design documentation, peer evaluation, project decisions, and technical reflection |
|
Develop applications that integrate external tools and services through agent-oriented interfaces and Model Context Protocol (MCP) (2g, 6a). |
MCP demonstrations, integration exercises, hands-on labs, and track-specific development activities |
MCP implementation assignment, code submission, and project integration |
|
Plan, implement, evaluate, and refine an agentic AI project using iterative development and sprint-based practices (2e, 2g, 6a, 6b). |
Sprint planning, implementation workshops, progress reviews, peer feedback, and project development |
Sprint deliverables, peer reviews, project documentation, and final project |
|
Communicate and critically evaluate the design, implementation, and outcomes of agentic AI systems across different application domains (3b, 6b, 6c). |
Peer review, cross-track discussion, project presentations, and portfolio development |
Final presentation, portfolio, peer evaluation, and project reflection |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
|
Week 1 |
Introduction to Agentic AI and AI-assisted software development; AI orchestration concepts and advanced orchestration workflows |
Configure the development environment and apply AI-assisted programming techniques to an initial software task |
Assignment 01: AI-Assisted Programming |
| Week 2 [dates] |
AI system design; hybrid AI architectures; integrating AI components with conventional software systems |
Design and implement a software architecture that combines AI tools or components with traditional software elements |
Assignment 02: Hybrid AI Architecture |
| Week 3 [dates] |
From AI-assisted development to agentic systems; agent roles, interaction, and coordination patterns |
Analyze an existing AI-assisted workflow and redesign it using coordinated agents |
Multi-Agent Coordination Exercise |
| Week 4 [dates] |
Production agent pipelines; multi-agent workflow design; pipeline implementation and optimization |
Design and implement a multi-agent workflow in which agents coordinate to complete a software development task |
Assignment 03: Multi-Agent Project |
| Week 5 [dates] |
Application-domain specialization; agentic AI for game, web, mobile, or data science/ML development |
Select a specialization track and apply agentic development techniques to a track-specific problem |
Assignment 04: Track-Specific Development |
| Week 6 [dates] |
Model Context Protocol (MCP); connecting agents with tools, services, data, and external resources; MCP implementation |
Extend the track-specific application by integrating external capabilities through MCP or related agent-tool interfaces |
MCP Integration Assessment |
| Week 7 [dates] |
Agentic AI project planning; system architecture; project management; transition from structured assignments to sustained project development |
Define the scope, architecture, agent roles, tools, development plan, and expected deliverables for the final project |
Project Proposal |
| Week 8 [dates] |
Sprint planning; iterative development; peer review; project workflow and implementation practices |
Establish the project backlog, development priorities, agentic workflow, and initial implementation plan |
Project Sprint 1 |
| Week 9 [dates] |
Advanced agentic AI techniques; project implementation workshop; refinement of agent interactions and system architecture |
Continue project implementation and evaluate the effectiveness of agent coordination and tool integration |
Project Sprint 2 |
| Week 10 [dates] |
Advanced agentic AI techniques; implementation workshop; system integration and refinement |
Extend project functionality and integrate agentic components into the larger application |
Project Sprint 3 |
| Week 11 [dates] |
Agentic system implementation; peer review; iterative improvement and technical problem solving |
Evaluate project progress, incorporate peer feedback, and improve system implementation |
Project Sprint 4 |
| Week 12 [dates] |
Advanced implementation techniques; agent and tool integration; project refinement |
Continue implementation, testing, and refinement of the agentic AI system |
Project Sprint 5 |
| Week 13 [dates] |
Project integration; advanced agentic development techniques; implementation workshop |
Complete major system functionality and address technical issues identified through testing and review |
Project Sprint Review |
| Week 14 [dates] |
Project finalization; portfolio development; presentation preparation |
Finalize the agentic AI system, technical documentation, and project portfolio; prepare project presentation |
Final Project and Portfolio |
| Week 15 [dates] |
Project presentations; cross-track analysis; reflection on agentic AI development practices |
Present the completed project and analyze design choices, agentic workflows, and lessons learned across application tracks |
Final Presentation and Reflection |
Expectations for Student Effort
[Students should expect to spend approximately three hours per week attending class and a minimum of six additional hours outside of class. Outside work will include assigned readings and technical materials, programming and development activities, peer review, project planning, sprint-based development, testing, documentation, and preparation for project presentations and assessments. Because this is a hands-on, project-based
course, the amount of time required may vary depending on students’ prior experience with software development, artificial intelligence tools, and the technologies used within their selected project track.]
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
|
Foundation Assignments |
300 |
30% |
|
Peer Reviews and Development Reviews |
150 |
15% |
|
Project Sprint Milestones |
250 |
25% |
|
Final Project and Portfolio |
200 |
20% |
|
Final Presentation and Reflection |
100 |
10% |
Foundation Assignments: Students will complete a series of hands-on assignments during the first part of the course covering AI-assisted programming, hybrid AI architectures, multi-agent coordination, agent pipelines, application-specific development, and MCP-based integration.
Peer Reviews and Development Reviews: Students will participate in structured peer reviews throughout the semester. Reviews will focus on technical design, implementation choices, use of agentic AI tools, project progress, and constructive feedback. Peer review is intended to support iterative improvement of both individual assignments and project work.
Project Sprint Milestones: During the project phase, students will complete a sequence of sprint-based milestones documenting project planning, implementation progress, agent and tool integration, testing, and refinement. Sprint deliverables will include appropriate code, technical documentation, and evidence of progress toward the final system.
Final Project and Portfolio: Students will complete and document an agentic AI software project demonstrating the integration of course concepts. The final submission will include the completed system, supporting technical documentation, and a portfolio demonstrating the development process and major project outcomes.
Final Presentation and Reflection: Students will present their completed project and explain its architecture, agentic workflow, major design decisions, technical challenges, and outcomes. Students will also reflect on the effectiveness and limitations of the agentic AI techniques used during development.
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
94–100 |
C |
73–76.9 |
| A- |
90–93.9 |
C- |
70–72.9 |
| B+ |
87–89.9 |
D+ |
65–69.9 |
| B |
83–86.9 |
D |
60–64.9 |
| B- |
80–82.9 |
F |
0–59.9 |
| C+ |
77–79.9 |
[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
Attendance and Make-Up Policy
[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ] [insert your policy here]
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-[insert your sanction here]
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.