AI Agents Summit 2025 - HackAIthon

どのテーマのhack も全般（General）に属します。

Overview: Imagine a world where most economic tasks are performed not by humans, but by AI agents - they collaborate, negotiate, and trade with each other in real time. A multi-agent swarm refers to a network of autonomous agents that operate collectively, often mimicking swarm intelligence found in nature. When empowered with economic capabilities, these agents can form a dynamic, decentralized agent-to-agent (A2A) economy, enabling services, resource exchanges, and microtransactions — all without human oversight.

Problem Space: The current digital economy is heavily dependent on centralized coordination and human-led decision-making. Even in decentralized systems, automation is mostly one-directional or isolated. By building an agent-to-agent economy, we aim to create a system where agents can:

• Discover other agents
• Offer/request services
• Make and accept payments
• Form temporary or long-term collaborations

We’re building toward a world of machine-driven coordination: 24/7 economies, algorithmic labor markets, and autonomous microservices. No intermediaries. No downtime. Let's build the future now!

Core Concepts:

• Swarm Intelligence: Coordination without central control — agents act individually with each other through shared goals, signals, or environments.
• Agent-to-agent (A2A) economy: Agents posting jobs, finding best offers, getting paid and paying each other - all through an agentic peer-to-peer market.
• Autonomous Bidding and Negotiation: Agents can negotiate prices, SLAs, and terms of engagement using smart contracts.
• Tokenized Microtransactions: Economic interactions between agents — real-time, low-friction, optionally gasless.

Use Case Examples:

• Autonomous Compute Networks: Agents bidding for compute time or GPU resources on decentralized networks.
• IoT Fleets: Drones, robots, or sensors autonomously negotiating bandwidth, routes, or data pricing with each other.
• Data Economy: Data-producing agents sell sensor data to agents needing it for model training or decision-making.
• Collaborative AI Agents: Multiple AI agents pooling intelligence or dividing tasks based on economic incentives.

Innovation Opportunities: Participants can explore (but not limited to):

• Integration of AI/ML models for smarter decision-making.
• Use of zero-knowledge proofs for privacy-preserving agents.
• Governance models for regulating autonomous agent behavior on-chain.
• Scalable agent coordination systems (multi-agent systems).