1. TL DR
2. Introduction
3. Verifiable Agent Actions AVS
4. Key Components
   1. AI Agent as Performer Node
   2. Proof Generation
   3. Validation by Attesters
5. Deployment Guide
6. Plugin for AI Frameworks
7. Example and References

TL DR

What Provide cryptographic proofs for arbitrary tasks completed by agents, facilitating verifiable evidence of agent actions in real-world systems.

Why This ensures accountability and transparency, mitigating the risks of errors or manipulation in AI Agent task execution and allowing users to verify tasks such as asset staking, cross-chain transfers, inference posts on Twitter, voting in a DAO proposal, and more.

How AI agent submits proof of task execution to the network operators. The operators individually verify the proof against pre-defined conditions and attest to its validity. The resultant attestations are aggregated, and results are subsequently logged on-chain for verifiable records

Introduction

AI agents are systems designed to perform specific tasks using machine learning models, APIs, and external integrations. Key components of AI agents include:

1. Types of AI Agents

• Automated - follow rule-based logic, execute predefined tasks, supervised.
  • Example - linear regressions, decision trees.
• Semi-autonomous - build, optimize, and execute tasks, unsupervised.
  • Example - artificial neural networks, K-means clustering.
• Autonomous - Leverage reinforcement learning to make decisions, with no human intervention.
  • Example - Q-learning, model-based value estimation.