AgentNet

The description states that AgentNet is a governance control plane for AI agent execution. It connects the existing agent ecosystem without replacing it.

Key components mentioned:

• AGNTCY for agent discovery
• A2A for agent-to-agent communication
• MCP for tool access
• Agent runtimes for actual task execution

The system creates signed execution contracts before work begins and produces signed receipts afterward.

It is built using:

• Python-first control plane
• FastAPI
• PostgreSQL
• Redis
• NATS JetStream
• Ed25519 signatures
• OpenAI Agents SDK

Inference The author describes AgentNet as a deterministic system that does not use LLMs for authorization or policy decisions, aiming for explainability and reproducibility.

The description states:

• AgentNet addresses the "missing governance layer" in AI agent ecosystems.
• It builds on existing protocols (A2A, MCP) but adds a governance dimension.
• The author emphasizes that communication ≠ authorization, discovery ≠ permission, capability ≠ authority.

Inference The positioning is focused on governance infrastructure, not on building or deploying agents themselves. It positions itself as a control plane that enables safe collaboration between agents.

The description does not name specific customers or personas.

However, it implies:

• Developers working with multi-agent systems
• Organizations using AI agents across different runtimes and tools
• Entities seeking policy-enforced execution and auditability

Inference The target is likely technical teams or enterprises building or managing agent-based workflows where governance and accountability are critical.

Not evidenced.

The description does not mention:

• Revenue model
• Pricing structure
• Monetization strategy
• Customers or sales process

The system is described as:

• Built in Python using FastAPI, PostgreSQL, Redis, NATS JetStream
• Uses Ed25519 signatures for contract and receipt verification
• Designed to be deterministic (no LLMs used for policy decisions)
• Supports distributed systems considerations like stale workers, duplicate events, and evidence preservation

Inference The author shows technical depth in system design and security, particularly around cryptography and event-driven architecture.

Not evidenced.

The description states:

• It was submitted to the OpenAI 2026 hackathon
• It is a proof-of-concept or prototype
• No mention of users, customers, revenue, or adoption

Inference There is no evidence of traction beyond the hackathon submission. The project appears to be in early development.

The description does not name competitors or reference existing solutions in this space.

However, it mentions:

• A2A (Agent-to-Agent communication protocol)
• MCP (Model Control Protocol)
• AGNTCY (agent discovery)

These are known protocols in the agent ecosystem. AgentNet is positioned as a governance layer that works alongside them.

Inference AgentNet operates in a space where governance of multi-agent systems is emerging, but no clear competitive landscape is described.

1. No traction or revenue evidence: The project is presented as a hackathon submission with no commercial or adoption data.
2. Unproven market demand: No indication that there’s a real-world need for this governance layer beyond the author's conceptual framing.
3. Limited team size: Only one member listed (Vikas S), which may limit execution capacity.
4. Self-reported only: All claims are unverified and based on the author’s own description.

1. What specific use cases or workflows does AgentNet aim to support in production?
2. Has there been any real-world testing or feedback from developers using this system?
3. How does AgentNet plan to scale beyond a prototype, especially around identity, attestation, and runtime integrations?
4. Are there any existing partnerships or pilot programs with developers or enterprises?
5. What are the key assumptions about governance in multi-agent systems that underpin this project?