Overview
Learn to build working agentic systems and understand how agents will change software, organizations, services, and everyday work.
At a Glance
Class
Thursdays, 10:00 AM–12:00 PM, starting September 10, 2026
Location
MIT Media Lab, E14-633
Format
Weekly lectures, technical assignments, and hands-on project work
Commitment
Approximately 6–10 hours per week outside class
Who Should Apply
Students who want to build useful systems with AI and understand where agentic technology is going.
Strong applicants may bring experience from engineering, product, research, design, business, or other fields.
Everyone is expected to build with modern AI tools and agents, learn through experimentation, and contribute to working projects.
MIT students and eligible cross-registrants are welcome to apply.
What You Will Learn and Build
The course connects each topic to the Agentic Web: an ecosystem in which agents discover tools, exchange information, use services, and act for people and organizations.
Personal Agents
Build agents for research, coding, planning, learning, or repeated work using context, memory, personal data, and tools.
Agent Loops and Autonomous Work
Design agents that plan, act, evaluate results, recover from failure, and know when to ask a person for help.
Multi-Agent Systems and Organizations
Coordinate specialized agents through delegation, task routing, shared memory, communication, and review.
Products and Infrastructure for Agents
Build APIs, tools, services, and environments that agents can discover, use, and combine across the web.
Open and Local Models
Explore model choice, local inference, adaptation, privacy, latency, deployment, and cost-quality trade-offs.
Robotics, Embodied Agents, and World Models
Connect multimodal perception, simulation, spatial reasoning, planning, and action across physical and online environments.
The Agentic Web
Study identity, delegated authority, permissions, trust, payments, shared protocols, security, and agent-to-agent communication.
Applied Topics
Apply agentic systems to emerging forms of economic activity, organizational work, and collective decision-making.
Agentic Commerce
Explore markets where agents discover services, compare offers, negotiate, transact, and build reputation on behalf of people and organizations.
Agentic Organizations and Work
Design teams of agents that operate workflows, share knowledge, allocate tasks, and coordinate decisions with people.
Agentic Societies
Study identity, trust, governance, voting, norms, privacy, and dispute resolution when autonomous agents participate in collective systems.
Course sandbox: NANDA Town. Students will use this open Nanda at MIT Media Lab environment to create scenarios where agents talk, trade, vote, and team up; test protocols before deployment; and analyze how agent interactions succeed or fail.
Course format: Weekly classes combine instruction and discussion with regular building and testing. Students progress from an individual agent to a multi-agent team and then a connected agentic organization, culminating in a technical Demo Day.
How to Apply
The application has two required steps. Applications are reviewed in waves, and earlier applications receive priority.
Fall 2026 Information Session
August 20, 2026, 12:00–1:00 PM ET · Virtual
View details and register
Application Steps
- Complete the Fall 2026 Questionnaire (Step 1).
- Complete the Fall 2026 NANDA Town Assessment (Step 2).
- Complete any additional registration forms required by MIT or Harvard.
Application Waves
- Priority wave: August 10, 2026, by 11:59 PM ET.
- Second wave: August 25, 2026, by 11:59 PM ET.
- Final wave: September 1, 2026, by 11:59 PM ET.
- Late applications: reviewed on a rolling basis after September 1 only if seats remain.
How Applications Are Reviewed
We assess readiness to build, relevant experience, interest in the course, and expected contribution to a team.
In each wave, applicants may be accepted, rejected, or waitlisted. Earlier waves receive preference, so apply early if possible.
Admission is selective, and completing the application does not guarantee a place in the course.
Before You Submit
Complete both application steps; Step 1 alone is not a complete application.
Use the same full name and email address in both forms. If you have an MIT ID, use your primary MIT email and official MIT name.
MIT registration or Canvas access does not replace the course application.
Course Schedule
Class Meetings and Key Dates
- Weekly class: Thursdays, 10:00 AM–12:00 PM, in E14-633 at the MIT Media Lab.
- Information sessions and pre-course social: A virtual information session is scheduled for August 20, 2026. Additional sessions and social details may be announced.
- First class: Thursday, September 10, 2026.
- Final demo materials due: Thursday, November 19, 2026.
- Final presentations and Demo Day: Thursday, December 3, 2026.
Additional Information
Assignment deadlines and project milestones will be published on Canvas.
Workshop, mentor, and guest speaker dates will be announced in advance throughout the semester.
The course may count toward the Entrepreneurship and Innovation Minor. Contact eiminor_admin@mit.edu to confirm eligibility and requirements.
Projects and Demo Day
Fall 2026 is organized as a cumulative project course. Students move from a single agent to a coordinated team and then to an organization connected through the Agentic Web.
1. Individual Agent
Build an agent with useful context, memory, tools, and workflows for an individual user or repeated task.
2. Multi-Agent Team
Connect individual agents into a small team that communicates, coordinates work, and completes tasks collectively.
3. Agentic Organization
Combine the earlier work into an agentic organization that discovers and uses services and interacts with other teams through NANDA Town.
Final Deliverable
A working technical project, such as an infrastructure component, product, service, environment, or shared standard.
A clear interface for agents, documentation showing how they use it, and evidence that it works in realistic scenarios.
Support and Demo Day
Teams receive mentor feedback, technical workshops, and support sessions as their projects develop.
Final projects are presented at a technical Demo Day on December 3, 2026, with a focused 2–3 minute live demonstration.
View detailed project and Demo Day requirements
- Projects may include infrastructure components, agent-ready products, services, environments for agents, and security or reliability tools.
- Each project should solve a clear problem, explain what an AI agent can do with it, and integrate with relevant agents, infrastructure, or services.
- Possible focus areas include identity, reputation, permissions, payments, memory, monitoring, evaluation, adversarial testing, service marketplaces, and workflow automation.
- By November 19, each team must submit its final title, team list, project URL, presentation deck, and any table or booth needs.
- Teams should plan for three mentor meetings and arrive with specific questions, a working artifact, and clear next steps.
Grading
Grades are based on three areas: Innovation (20%), Agent Infrastructure and Products (30%), and Technical Building (50%). Attendance, quizzes, peer feedback, work with mentors, and preparation for Demo Day contribute to those areas.
| Grade | What it means |
|---|---|
| A+ | An exceptional technical project with original insight, strong evidence that agents can use it reliably, a polished demo completed before the submission deadline, clear documentation, helpful peer feedback, and three productive mentor meetings. |
| A | All required work completed on time, a strong working infrastructure component or prototype, a clearly documented interface for agents, solid technical execution, active participation, and an on-time Demo Day presentation. |
| A- | Complete and serious work with minor gaps in technical depth, testing, polish, documentation, or consistency. The demo works, but the project is less reliable or ambitious than A-level work. |
| B+ / B | Substantial participation and a partially working project, but one major area needs improvement: a missed milestone, limited technical implementation or testing, weak collaboration, or an incomplete final demo. |
| B- / C | Inconsistent participation, late or missing assignments, limited project progress, poor communication, or a demo that does not show a working system. |
| No credit / Fail | Required work or presentations are missing, academic integrity rules are violated, or the student repeatedly does not respond to course requirements and staff. |
Course Instructors
Course Team
Mentors and Guest Speakers
Fall 2026 mentors and speakers will be announced throughout the semester. Sessions will emphasize working systems, implementation choices, failures, deployment, reliability, and lessons from practice.
Background
The course evolved from Sandy Pentland's Development Ventures and has a long track record of helping students turn class projects into working products and ventures.
View previous course offerings
- Spring'26: AI Studio
- Fall'25: AI Studio
- Spring'25: AI Venture Studio
- Fall'24: MIT Foundations of AI Ventures
- Spring'24: AI Venture Studio
- Fall'23: AI Venture Studio
- Spring'23: AI + Web3 for Impact: Venture Studio
- Fall'22: AI + Web3 for Impact: Venture Studio
- Spring'22: AI for Impact: Venture Studio
- Fall'21: AI for Impact ~ Building Global Ventures Solving Societal-Scale Problems
- Spring'21: AI for Impact
- Fall'20: Global Ventures ~ Data and AI for Resilience after COVID19
- Spring'20: AI for Impact ~ Towards Solving Societal-Scale Problems via Media Ventures
FAQ
What backgrounds are a good fit?
Students join from engineering, product, research, design, business, and other backgrounds. Everyone is expected to build with modern AI tools and agents, contribute to working projects, and learn through experimentation.
What is the Agentic Web?
The Agentic Web is an ecosystem of connected AI agents, tools, services, and shared standards. Agents can discover services, exchange information, and combine tools to complete tasks across different systems.
Is collaboration allowed?
Yes. Peer groups and mentor feedback are part of the course. Students should help each other learn while submitting their own work and documenting team contributions clearly.
Do I need a startup idea?
No. The course focuses on learning and building agentic systems. Projects may become products or ventures, but they may also be research tools, open-source infrastructure, personal agents, or public-interest systems.
Videos and Previous Demo Days
Explore recent student demos and talks that provide context for the ideas, systems, and communities behind the course.