**Abstract**. Autonomous AI agents are emerging as persistent, goal-driven systems capable of interacting with digital and physical environments without continuous human intervention. While advances in large language models (LLMS), small language models (SLMs), and multi-modal AI have accelerated their cognitive and decision-making abilities, a critical barrier remains: the lack of secure, scalable, and trustless infrastructure enabling these agents to transact, own assets, and execute agreements autonomously. This paper explores the concept of _operating rails_—the foundational infrastructure layer that provides identity, value transfer, and programmable coordination for AI agents. We examine blockchain as a candidate for these rails, with a focus on the SKALE Networkas a case study in delivering zero-gas and high-throughput in collaboration with the upcoming FAIR Blockchain for the underlying financial rails. Through technical analysis and scenario-based exploration, I outline how such infrastructure enables a new class of economically active AI agents, discuss the ethical and governance implications, and propose a framework for evaluating future operating rail architectures.
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## Introduction
The adoption of artificial intelligence, specifically usage of Large Language Models (LLMs), has been explosive in 2025.
[**CALL OUT:**
- Hook: Open with a scenario or statistic about AI agents in 2025.
- Define “AI agents” and “operating rails” in plain language.
- State the paper’s research question: *How can AI agents operate autonomously in real-world economies, and what infrastructure is required?*
- Briefly preview SKALE’s relevance.]
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## 2. Background and Literature Review
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[**CALL OUT:**
- Summarize prior research on autonomous agents and agentic AI ([cite sources]).
- Explain the current state of blockchain infrastructure for AI ([cite academic + industry sources]).
- Identify key gaps in existing systems — scalability, trust, cost.]
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## 3. Problem Statement
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[**CALL OUT:**
- Clearly define the “ownership and autonomy gap” for AI agents — why they can think but not *own* assets.
- Detail the limitations of traditional financial and identity systems.
- Explain risks (security, trust, governance) without proper operating rails.]
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## 4. Proposed Framework — Blockchain as Operating Rails
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[**CALL OUT:**
- Outline how blockchain infrastructure can address the problem.
- Highlight key properties needed: trustless execution, programmability, scalability, privacy.
- Briefly introduce SKALE as an example platform — but keep it general enough for research context.]
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## 5. SKALE as a Case Study
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[**CALL OUT:**
- Describe SKALE’s architecture (elastic chains, zero-gas fees, MEV resistance).
- Explain how these features map directly to AI agent needs.
- Use diagrams or tables to match SKALE capabilities to AI requirements.]
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## 6. Example Scenarios
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[**CALL OUT:**
- Provide 2–3 realistic AI agent use cases operating on SKALE:
1. Finance bot with automated micro-payments.
2. E-commerce agent managing supply chain.
3. Game NPC with its own economy.
- Show how the operating rails enable these scenarios to function at scale.]
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## 7. Discussion
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[**CALL OUT:**
- Evaluate the benefits and limitations of using SKALE for AI agents.
- Consider ethical, governance, and security implications.
- Compare with other blockchain solutions briefly to justify choice.]
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## 8. Conclusion and Future Work
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[**CALL OUT:**
- Restate the importance of operating rails for AI autonomy.
- Highlight SKALE’s role in enabling agentic AI economies.
- Suggest areas for future research — e.g., multi-agent coordination, hybrid AI-blockchain governance models.]
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## References
[**CALL OUT:** Insert academic papers, whitepapers, industry reports, and relevant web sources here — aim for at least 8–12 credible citations.]