AI Agents Summit 2025 - HackAIthon

Overview: Autonomous agents are self-operating software entities capable of perceiving their environment, making decisions, and taking actions without human intervention. When coupled with web3 rails these agents become autonomous, with the capability to perform secure, transparent, and verifiable operations on-chain — opening up a new realm of decentralized automation.

Problem space: Legacy automation is opaque and centralized. On-chain agents offer composability, transparency and a native payment system. However, there's a need to develop robust frameworks, protocols, and use cases that demonstrate how these agents can interact meaningfully with on-chain data and systems. Let’s build the next generation of AI agents with real use cases.

What to Explore:

• On-Chain Execution: Agents triggering smart contracts, state changes, and on-chain events based on autonomous logic.
• Decentralized Identity (DID): Self-sovereign agents that authenticate and operate securely using decentralized identity systems
• Interoperability: Agents acting across ecosystems, both web3 and web2.
• Crypto-Native Finance: Agents with their own wallets, trading, staking strategies, or yield optimization logic.

Sample Use Cases:

• Portfolio-rebalancing bots
• DAO-native contributors with verifiable outputs
• Supply chain agents updating NFT-bound metadata
• On-chain arbitrators or negotiation agents

Examples of Use Cases:

• DeFi Portfolio Bots: Agents that automatically manage portfolios, swap tokens, or yield farm based on on-chain and off-chain conditions.
• DAO Workers: Agents acting as contributors to DAOs, executing assigned tasks, reporting outcomes, and receiving compensation.
• Autonomous Supply Chain Trackers: Agents updating product states on-chain based on real-time IoT data.
• On-chain Negotiation Agents: Bots that autonomously engage in buying/selling, bidding, or forming agreements through smart contracts.
• MCP servers. Bring payment rails to the MCP hype
• Bringing Web2 to Web3. Be creative, think outside of the box! Connect to existing legacy Web2 platforms to bring them the power of Web3

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)

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).