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We are proud to be the

Headline sponsor

of

UK AI Agent Hack Ep5. x Conduct.AI

A week-long hackathon bringing together the UK's brightest builders to push the frontier of autonomous AI agents — backed by conduct.ai, Microsoft, fetch.ai, OpenAI and more.

June 28, 2026 to July 4, 2026

Imperial College London

Prizes

1st Place

£500

Cash Prize + Internship Interview Opportunity

2nd Place

£350

Cash Prize + Internship Interview Opportunity

3rd Place

£150

Cash Prize + Internship Interview Opportunity

Introduction

Fetch.ai is your gateway to the agentic economy. It provides a full ecosystem for building, deploying, and discovering AI Agents. With Fetch.ai, you can:

  • Build agents using the uAgents framework.
  • Register agents (built with uAgents or any other framework) on Agentverse, the open marketplace for AI Agents.
  • Make your agents discoverable and accessible through ASI:One, the world’s first agentic LLM.
What are AI Agents?

AI Agents are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.

The Three Pillars of the Fetch.ai Ecosystem

  • uAgents – A Python library developed by Fetch.ai for building autonomous agents. It gives you everything you need to create agents that can talk to each other and coordinate tasks.
  • Agentverse - The open marketplace for AI Agents. You can publish agents built with uAgents or any other agentic framework, making them searchable and usable by both users and other agents.
  • ASI:One – The world’s first agentic LLM and the discovery layer for Agentverse. When a user submits a query, ASI:One identifies the most suitable agent and routes the request for execution.

Challenge statement

ASI:One Agent Challenge - From Intent to Action

The Challenge:

Most AI applications stop at conversation. Your challenge is to build an AI agent that can be discovered through ASI:One, understand a user’s intent, and take meaningful action to solve a real-world problem. Your agent might coordinate services, automate a workflow, analyze live information, make recommendations, complete transactions, or collaborate with other specialized agents. The problem and approach are up to you, but the result should be more than a chatbot or a thin wrapper around an API.

What to Build

Build a single agent or multi-agent system that:

  • Solves a clearly defined, real-world problem.
  • Performs multi-step planning, decision-making, or orchestration.
  • Uses tools, APIs, data sources, or other agents to produce an executable outcome.
  • Is registered on Agentverse and discoverable and usable through ASI:One.
  • Allows the core use case to be demonstrated directly within an ASI:One conversation. You may use any framework, including the Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, or plain Python.

Mandatory Requirements

To be eligible for a prize:

  • Register at least one agent on Agentverse.
  • Implement the Agent Chat Protocol.
  • Make the agent discoverable and directly usable through ASI:One.
  • Demonstrate meaningful tool execution or agent-to-agent orchestration.
  • Complete the primary user workflow without requiring a custom frontend.
  • Submit a public GitHub repository with instructions for running or testing the project.

Bonus Points

Projects may receive additional consideration for:

  • Effective multi-agent collaboration.
  • Implementation of the Payment Protocol and a credible monetization model.
  • Strong reliability, error handling, and recovery from failed tool calls.
  • Creative use of real-time data or external services.
  • An agent that could realistically continue operating after the hackathon.

Deliverables

Submit the following through Devpost:

  • Public ASI:One shared chat session URL showing the complete workflow. Example
  • Agentverse Agent Profile URL(s) for each submitted agent. Example
  • Public GitHub repository URL.
  • Short demo video.
  • Brief description of the problem, target user, and outcome produced by the agent.
What to Submit
  1. Code

    • Share the link to your public GitHub repository to allow judges to access and test your project.
    • Ensure your README.md file includes key details about your agents, such as their name and address, for easy reference.
    • Mention any extra resources required to run your project and provide links to those resources.
    • All agents must be categorized under Innovation Lab.
      • To achieve this, include the following badge in your agent’s README.md file:

        ![tag:innovationlab](https://img.shields.io/badge/innovationlab-3D8BD3)
        
        ![tag:hackathon](https://img.shields.io/badge/hackathon-5F43F1)
        
  2. Video

    • Include a demo video (3–5 minutes) demonstrating the agents you have built.
architecture

Tool Stack

architecture

Quick start example

This file can be run on any platform supporting Python, with the necessary install permissions. This example shows two agents communicating with each other using the uAgent python library.
Try it out on Agentverse ↗

code-icon
code-icon
from datetime import datetime
from uuid import uuid4
from uagents.setup import fund_agent_if_low
from uagents_core.contrib.protocols.chat import (
   ChatAcknowledgement,
   ChatMessage,
   EndSessionContent,
   StartSessionContent,
   TextContent,
   chat_protocol_spec,
)


agent = Agent()


# Initialize the chat protocol with the standard chat spec
chat_proto = Protocol(spec=chat_protocol_spec)


# Utility function to wrap plain text into a ChatMessage
def create_text_chat(text: str, end_session: bool = False) -> ChatMessage:
content = [TextContent(type="text", text=text)]
   return ChatMessage(
       timestamp=datetime.utcnow(),
       msg_id=uuid4(),
       content=content,
   )


# Handle incoming chat messages
@chat_proto.on_message(ChatMessage)
async def handle_message(ctx: Context, sender: str, msg: ChatMessage):
   ctx.logger.info(f"Received message from {sender}")
  
   # Always send back an acknowledgement when a message is received
   await ctx.send(sender, ChatAcknowledgement(timestamp=datetime.utcnow(), acknowledged_msg_id=msg.msg_id))


   # Process each content item inside the chat message
   for item in msg.content:
       # Marks the start of a chat session
       if isinstance(item, StartSessionContent):
           ctx.logger.info(f"Session started with {sender}")
      
       # Handles plain text messages (from another agent or ASI:One)
       elif isinstance(item, TextContent):
           ctx.logger.info(f"Text message from {sender}: {item.text}")
           #Add your logic
           # Example: respond with a message describing the result of a completed task
           response_message = create_text_chat("Hello from Agent")
           await ctx.send(sender, response_message)


       # Marks the end of a chat session
       elif isinstance(item, EndSessionContent):
           ctx.logger.info(f"Session ended with {sender}")
       # Catches anything unexpected
       else:
           ctx.logger.info(f"Received unexpected content type from {sender}")


# Handle acknowledgements for messages this agent has sent out
@chat_proto.on_message(ChatAcknowledgement)
async def handle_acknowledgement(ctx: Context, sender: str, msg: ChatAcknowledgement):
   ctx.logger.info(f"Received acknowledgement from {sender} for message {msg.acknowledged_msg_id}")


# Include the chat protocol and publish the manifest to Agentverse
agent.include(chat_proto, publish_manifest=True)


if __name__ == "__main__": 
    agent.run()
Video introduction
Video 1
Introduction to agents
Video 2
On Interval
Video 3
On Event
Video 4
Agent Messages

Judging Criteria

  1. Functionality & Technical Implementation (25%)

    • Does the agent complete the intended workflow reliably?
    • Does it demonstrate meaningful tool use, planning, or multi-agent coordination?
  2. Use of Fetch.ai Technology (25%)

    • Is the agent properly registered on Agentverse and usable through ASI:One via the Chat Protocol?
    • Is the Fetch.ai integration central to the solution rather than added only for eligibility?
  3. Innovation & Creativity (20%)

    • Is the solution original or technically distinctive?
    • Does it use agents in a meaningful way rather than functioning as a basic chatbot or API wrapper?
  4. Real-World Impact & Usefulness (20%)

    • Does the project solve a clear and meaningful problem?
    • Would the outcome be genuinely useful to its intended users?
  5. User Experience & Presentation (10%)

    • Is the ASI:One experience intuitive and easy to follow?
    • Is the end-to-end demo clear, functional, and well presented?

Prizes

1st Place

£500

Cash Prize + Internship Interview Opportunity

2nd Place

£350

Cash Prize + Internship Interview Opportunity

3rd Place

£150

Cash Prize + Internship Interview Opportunity

Collaborators

partner-image
partner-image

Judges

Profile picture of Sana Wajid

Sana Wajid

Chief Development Officer - Fetch.ai
Chief Operations Officer - Innovation Lab

Profile picture of Attila Bagoly

Attila Bagoly

Chief AI Officer, Fetch.ai

Mentors

Profile picture of Dev Chauhan

Dev Chauhan

Developer Advocate

Profile picture of Gautam Kumar

Gautam Kumar

Developer Advocate

Profile picture of Tejus Gupta

Tejus Gupta

Developer Advocate

Profile picture of Rajashekar Vennavelli

Rajashekar Vennavelli

AI Engineer

Profile picture of Geetanshi Goel

Geetanshi Goel

Junior Software Engineer

Profile picture of Shyamji Pandey

Shyamji Pandey

Junior Software Engineer

Sounds exciting, right?

Schedule

Sunday, June 28

10:00 BST

Opening Ceremony

Imperial College London

Monday, June 29

09:00 BST

Workshops and Mentorship

Imperial College London

Wednesday, July 01

09:00 BST

Build & Submit

Imperial College London

Saturday, July 04

10:00 BST

Demo Day & Finals

Imperial College London