Oct 30 – Nov 1, 2026 MIT Media Lab

The Internet of Agents Hackathon

A hackathon on decentralized AI swarms — agents built by different labs, startups, and companies discovering each other, sharing capabilities, and coordinating on real scientific and engineering problems.

3 daysIn-person
2–5Per team
Agent swarms
4Challenge Areas
01

Purpose

Explore what becomes possible when collaborative, decentralized agentic systems are used to solve difficult, consequential scientific and technical problems.

The hackathon is itself an experiment in collective intelligence: agents collaborate within teams, teams exchange capabilities across teams, and successful capabilities propagate through the ecosystem.

02

Challenge

Teams identify a difficult scientific or technical problem and build a functioning agentic system to address it. Each project should demonstrate:

Problem

Identify a difficult, open-ended, consequential scientific or technical challenge.

Functioning System

Build a working agentic system — not just a concept or architecture.

Result

Demonstrate what the system actually accomplishes.

Validation

Provide convincing evidence, benchmarks, or real-world validation.

The goal is not simply to propose an idea or agent architecture, but to demonstrate a working system and what it can accomplish.

03

Resources & Technology

Teams have access to a common pool of resources to ground investigations from hour one.

R1

Scientific datasets

Shared corpora and domain data for grounding investigations.

R2

AI models & tools

Foundation models, agent frameworks, and open tooling to compose into swarms.

R3

Compute & tokens

A common pool of credits so experimentation is never bottlenecked.

R4

Hardware

Robots, microcontrollers, and sensors for agents that act in the world.

R5

Lab access

Cloud labs and experimental infrastructure for closing the loop on real experiments.

R6

Open problem space

Sponsor-led challenges provide concrete inspiration — unexpected approaches welcome.

Teams are free to use, reuse, modify, combine, or build their own technology stack. They may use existing frameworks, build on capabilities developed by other participants, integrate physical systems, or develop entirely new architectures. The competition is not about a particular technology stack, but about what teams can accomplish with agentic systems.

04

Challenge Areas

Scientific domains are intentionally open. We provide challenge seeds, problems, datasets, resources, or sponsor-led challenges to provide entry points without constraining what teams build.

A·01

Biology & Protein Design

Swarms that hypothesize, design, and iterate across sequence, structure, and function.

A·02

Robotics & Physical Agency

Agents that reach beyond the browser into robots, sensors, and cloud laboratories.

A·03

Manufacturing & Materials

From candidate discovery to fabrication — closing the loop between simulation and matter.

A·04

Open Scientific Challenge

Participant-defined problems. Bring the question you can't stop thinking about.

Teams can formulate their own problems and work across challenge areas. There is one overall competition, rather than separate tracks.

05

Judging

The rubric rewards systems where collective organization itself contributes capability, rather than systems that simply place multiple agents around a task.

Problem Significance & Complexity

How difficult, open-ended, consequential, and scientifically meaningful is the problem? Does solving it meaningfully advance a scientific or technical goal?

20%

Scientific/Technical Impact

What was actually achieved? Is the result novel, meaningful, rigorous, and potentially useful?

25%

Decentralized Agency

Are decision-making and capabilities meaningfully distributed across agents? Do agents have distinct information, resources, or capabilities? Can they coordinate and adapt without a central planner prescribing the entire trajectory?

20%

Collective Capability

Does organizing agents collectively add measurable capability? Where possible, demonstrate this through a simple comparison such as multi-agent vs. single-agent, collaborative vs. isolated, or decentralized vs. centralized.

25%

Execution and Validation

Is there a functioning system rather than just a concept? Is its performance demonstrated with convincing evidence, benchmarks, or real-world validation?

10%

Collaboration Bonus: +10 — Collaboration is not part of the core judging score. Teams can earn up to +10 points by creating capabilities that materially strengthen other teams, such as:

  • A tool or agent successfully reused by another team
  • Data, discoveries, or experiments that enable another team's result
  • Infrastructure that improves multiple systems
  • Capabilities that propagate through the ecosystem
06

The Hackathon as a Multi-Agent System

The hackathon operates across multiple levels:

Agents

Collaborate within teams to solve problems.

Teams

Exchange capabilities across teams.

Ecosystem

Successful capabilities propagate because they increase the effectiveness of other systems.

The competition therefore becomes a live experiment in decentralized collective intelligence: teams compete to build strong systems while also having an incentive to make the broader ecosystem stronger.

Swarm · Oct 30 – Nov 1, 2026 · MIT Media Lab

Join the swarm.

October 30 – November 1, 2026. Bring a problem — or come find one.

Apply now