Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries
- URL: http://arxiv.org/abs/2601.04583v1
- Date: Thu, 08 Jan 2026 04:29:26 GMT
- Title: Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries
- Authors: Saad Alqithami,
- Abstract summary: Agentic AI systems and public blockchains have evolved into a programmable substrate for value transfer, access control, and verifiable state transitions.<n>Their convergence introduces a high-stakes systems challenge: designing standard, interoperable, and secure interfaces that allow agents to observe on-chain state, formulate transaction intents, and authorize execution without exposing users, protocols, or organizations to unacceptable security, governance, or economic risks.<n>This survey systematizes the emerging landscape of agent-blockchain interoperability through a systematic literature review, identifying 317 relevant works from an initial pool of over 3000 records.
- Score: 2.28438857884398
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step workflows, while public blockchains have evolved into a programmable substrate for value transfer, access control, and verifiable state transitions. Their convergence introduces a high-stakes systems challenge: designing standard, interoperable, and secure interfaces that allow agents to observe on-chain state, formulate transaction intents, and authorize execution without exposing users, protocols, or organizations to unacceptable security, governance, or economic risks. This survey systematizes the emerging landscape of agent-blockchain interoperability through a systematic literature review, identifying 317 relevant works from an initial pool of over 3000 records. We contribute a five-part taxonomy of integration patterns spanning read-only analytics, simulation and intent generation, delegated execution, autonomous signing, and multi-agent workflows; a threat model tailored to agent-driven transaction pipelines that captures risks ranging from prompt injection and policy misuse to key compromise, adversarial execution dynamics, and multi-agent collusion; and a comparative capability matrix analyzing more than 20 representative systems across 13 dimensions, including custody models, permissioning, policy enforcement, observability, and recovery. Building on the gaps revealed by this analysis, we outline a research roadmap centered on two interface abstractions: a Transaction Intent Schema for portable and unambiguous goal specification, and a Policy Decision Record for auditable, verifiable policy enforcement across execution environments. We conclude by proposing a reproducible evaluation suite and benchmarks for assessing the safety, reliability, and economic robustness of agent-mediated on-chain execution.
Related papers
- From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture [0.0]
Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems.<n>This paper examines this transition by connecting intelligent agent theories, with contemporary LLM-centric approaches.<n>The study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms.
arXiv Detail & Related papers (2026-02-11T03:34:48Z) - The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
arXiv Detail & Related papers (2026-01-21T15:22:21Z) - Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
arXiv Detail & Related papers (2026-01-12T21:31:38Z) - A Blockchain-Monitored Agentic AI Architecture for Trusted Perception-Reasoning-Action Pipelines [0.0]
The application of agentic AI systems in autonomous decision-making is growing in the areas of healthcare, smart cities, digital forensics, and supply chain management.<n>The paper suggests a single architecture model comprising of LangChain-based multi-agent system with a permissioned blockchain to guarantee constant monitoring, policy enforcement, and immutable auditability of agentic action.
arXiv Detail & Related papers (2025-12-24T06:20:28Z) - OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
arXiv Detail & Related papers (2025-10-28T13:22:39Z) - Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach [21.498244821985562]
We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tracking.<n>Our implementation integrates GPT-4 agents with Solidity contracts and demonstrates, through 50-round simulations, strong task success rates, stable utility distribution, and emergent agent specialization.
arXiv Detail & Related papers (2025-09-20T16:00:24Z) - AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production [4.031479494871582]
We present Agent, the first evaluation framework designed specifically for post-deployment monitoring and reasoning of agentic pipeline.<n>Agent achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations.
arXiv Detail & Related papers (2025-09-18T05:59:04Z) - Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions [30.046299694187855]
Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration.<n>We propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer.<n>Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems.
arXiv Detail & Related papers (2025-09-11T07:46:00Z) - A Survey on Agent Workflow -- Status and Future [2.817843718857682]
This survey provides a comprehensive review of agent workflow systems.<n>We classify existing systems along two key dimensions: functional capabilities and architectural features.<n>We highlight common patterns, potential technical challenges, and emerging trends.
arXiv Detail & Related papers (2025-08-02T04:15:30Z) - SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents [58.21223208538351]
This work explores the security issues surrounding mobile multimodal agents.<n>It attempts to construct a risk discrimination mechanism by incorporating behavioral sequence information.<n>It also designs an automated assisted assessment scheme based on a large language model.
arXiv Detail & Related papers (2025-07-01T15:10:00Z) - LLM Agents Should Employ Security Principles [60.03651084139836]
This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
arXiv Detail & Related papers (2025-05-29T21:39:08Z) - Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [43.253676241213626]
We propose an architecture for blockchain-based PAISs to preserve confidentiality and transparency.<n>Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.<n>We assess the security of our solution through a systematic threat model analysis and evaluate its practical feasibility.
arXiv Detail & Related papers (2024-12-07T20:18:36Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents.
We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions.
Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
arXiv Detail & Related papers (2023-06-29T15:57:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.