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What Are Fully Onchain Agents?

  • Writer: John Pilongo
    John Pilongo
  • Apr 26
  • 5 min read

Updated: May 21

In our first article, we introduced the concept of AI agents. Today, we’re diving deeper, focusing on onchain agents and the real-world advantages they offer over traditional offchain systems. These aren’t just theoretical ideas. Onchain agents are already transforming how tasks, transactions, and trust are managed within decentralized ecosystems. In most cases, “onchain” refers to agents that take actions onchain, while their logic and execution remain offchain.


What we’re building is different. It’s fully onchain. We use that term intentionally because we believe the distinction matters. For us, the logic and execution of the agents are carried out onchain, bringing a level of transparency and decentralization to agents that hasn’t been seen before. As the space evolves, we expect more precise language to emerge, language that better reflects how agents operate and where they truly live.


ChainsAtlas Fully Onchain AI Agents

AI Agents in Blockchain Explained


In blockchain networks, AI agents function like autonomous problem solvers. They monitor their surroundings, make intelligent decisions, and conduct transactions across distributed ledgers without human input. For instance, agents on the Fetch.ai platform autonomously book parking spaces or optimize energy usage in smart cities by negotiating with other agents based on live conditions.


In decentralized finance, platforms like Ulam Labs have explored using AI agents to track arbitrage opportunities and execute trades across multiple decentralized exchanges in real time. Unlike basic automated scripts, these agents use machine learning to adapt their behavior to respond to changing network conditions. This ability to learn and adjust boosts system efficiency, improves resilience, and supports more advanced decision-making in decentralized environments.


Onchain AI Agent Use Cases

Cross-Chain Communication


Conventional blockchain bridges rely on manual initiation and often suffer from slow processing times. AI agents streamline this process by actively monitoring network conditions and executing transfers at optimal moments. For example, Wormhole and LayerZero are exploring intelligent automation to improve cross-chain messaging and asset transfers. Meanwhile, deBridge has introduced routing mechanisms that dynamically assess transaction speed and cost to enhance user experience. These AI-enhanced systems evaluate factors like gas fees, network congestion, and security risks across multiple blockchains. They automatically choose the most efficient and secure route for each transfer, removing the need for constant human input.


Smart Contract Optimization


AI agents refine and optimize smart contract interactions by analyzing historical transaction data to identify patterns in gas usage and execution reliability. For example, platforms like Autonolas are developing autonomous agents that monitor contract performance and fine-tune parameters on the fly. Similarly, Bittensor explores decentralized learning where agents adjust based on collective network behavior. Using insights from past activity, these agents dynamically modify inputs such as gas limits or transaction timing. This allows them to reduce costs, avoid failed executions, and ensure smoother interactions across blockchain applications.


DeFi Integration


In decentralized finance (DeFi), AI agents function as autonomous financial tools that simplify and optimize complex tasks. They reduce the need for manual oversight by using real-time data to manage portfolios, execute trades, and control risk. For instance, Gauntlet provides automated risk management for DeFi protocols, helping platforms adjust parameters based on live market simulations to maintain capital efficiency and protect users.


Key roles of AI agents in DeFi include:


  • Tracking liquidity pools across multiple protocols

  • Dynamically rebalancing portfolios based on market conditions

  • Identifying and executing arbitrage across decentralized exchanges

  • Managing risk parameters for lending and borrowing platforms

  • Powering AI-driven DAOs for automated governance and treasury functions

  • Optimizing investment strategies using real-time analysis

  • Enhancing multisig wallet security through automated approvals based on defined rules.


Privacy


AI-powered security is becoming a foundational layer in blockchain infrastructure, enabling systems to detect and respond to threats before they can cause harm. A standout example is COTI, a ChainsAtlas partner, which integrates AI not only for performance optimization but also for robust privacy and security.


COTI’s Trustchain protocol leverages machine learning to assign Trust Scores to users based on behavioral data, helping the network detect anomalies, prevent double-spending, and ensure faster, more reliable transaction validation. At the same time, COTI V2 introduces a major breakthrough in blockchain privacy: the use of garbled circuits. This cryptographic technique allows confidential execution of smart contracts and transactions, keeping sensitive data private while maintaining the integrity of decentralized systems. Combined with AI-driven monitoring and asset management tools, COTI demonstrates how intelligence and privacy can coexist to raise the standard for decentralized security and user control.


The Future of Onchain and Fully Onchain Agents


The future of AI agents is being shaped not just by where they act, but by how deeply they are woven into the fabric of decentralized systems. True autonomy will belong to agents that are not only blockchain-aware but blockchain-native, executing their logic directly onchain, as ChainsAtlas agents do. This level of integration creates transparency, decentralization, and operational integrity that surface-level onchain interactions simply can't match.


Looking ahead, the most capable agents will be those that learn and evolve in real time, trustlessly coordinate across networks, and fluidly interact across ecosystems. Onchain learning will allow agents to adjust to new protocols without external intervention. Peer-to-peer collaboration among agents will raise the bar for decision-making across sectors like DeFi, governance, and security. Formal verification will make financial logic provable, reducing risk at the contract level. With standardized interoperability, agents will move across blockchains as easily as data flows across the internet today, opening the door to a truly decentralized intelligence network.


Challenges and Limitations


Despite the fast progress of AI agents in blockchain, key challenges remain. Security is one of the most pressing, as shown in the aiXbt exploit, where an AI agent was manipulated into sending 55 ETH due to false inputs. This vulnerability often arises when agents operate offchain and depend on external data or execution layers, making them easier to exploit. Fully onchain agents, like those built with ChainsAtlas, reduce this risk by operating within verifiable environments where all data and decisions are transparent and traceable on the blockchain.



Computational scalability is another challenge. AI agents process large amounts of onchain data in real time, which requires significant resources. Ensuring these systems remain efficient as they scale is crucial. Solving security and performance challenges is essential to building AI agents that can operate safely and effectively in decentralized systems.


Conclusion


AI agents are rapidly becoming the engine behind a more efficient decentralized future. No longer limited to basic automation, they now play active roles in securing networks, optimizing financial strategies, and making intelligent decisions in real time. The rise of these agents marks a turning point in the evolution of blockchain technology.


Fully onchain agents, like those developed by ChainsAtlas, go a step further by ensuring that decisions, data, and execution are verifiable and transparent at every layer. This solves key issues that offchain or semi-automated systems often face.


As onchain learning, formal verification, and cross-chain interoperability develop, AI agents will become more adaptive, secure, and collaborative. Challenges around scalability and adversarial threats remain, but solutions are rapidly emerging. What lies ahead is a more resilient blockchain ecosystem where autonomous agents act as the foundation for a new era of decentralized coordination, governance, and intelligence.


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