We are proud to be the
Headline sponsor
UK AI Agent Hack Ep5. x Conduct.AI
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
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.
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:
-
Code
- Share the link to your public GitHub repository to allow judges to access and test your project.
- Ensure your
README.mdfile 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.mdfile:
-
-
Video
- Include a demo video (3–5 minutes) demonstrating the agents you have built.
Tool Stack
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 ↗
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()
Important links
Fetch.ai Resources




Examples to get you started:
Judging Criteria
-
Functionality & Technical Implementation (25%)
- Does the agent complete the intended workflow reliably?
- Does it demonstrate meaningful tool use, planning, or multi-agent coordination?
-
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?
-
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?
-
Real-World Impact & Usefulness (20%)
- Does the project solve a clear and meaningful problem?
- Would the outcome be genuinely useful to its intended users?
-
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
Judges

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

Attila Bagoly
Chief AI Officer, Fetch.ai
Mentors

Dev Chauhan
Developer Advocate
Gautam Kumar
Developer Advocate

Tejus Gupta
Developer Advocate

Rajashekar Vennavelli
AI Engineer

Geetanshi Goel
Junior Software Engineer

Shyamji Pandey
Junior Software Engineer
Sounds exciting, right?
Schedule
10:00 BST
Opening Ceremony
Imperial College London
09:00 BST
Workshops and Mentorship
Imperial College London
09:00 BST
Build & Submit
Imperial College London
10:00 BST
Demo Day & Finals
Imperial College London

