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What are AI Agents?

  • Writer: John Pilongo
    John Pilongo
  • Apr 12
  • 4 min read

Updated: Apr 19



ChainsAtlas builds the core infrastructure that powers the future of autonomous onchain agents. Our mission is to help developers and organizations have intelligent AI agents natively interact with any blockchain while themselves running on-chain on a fully decentralized and trustless blockchain. By combining AI capabilities with decentralized networks, ChainsAtlas enables agents to autonomously interact with smart contracts, manage assets, and execute complex tasks safely and reliably. 

 

This article series will serve as the first part of an educational journey, giving readers a clear entry point into artificial intelligence. We will begin by exploring what AI agents are, followed by what onchain agents are and why they matter. This series will help readers understand how ChainsAtlas will provide the core tools and infrastructure for scalable, secure, and composable onchain agents.


Fundamental Building Blocks of AI Agents


AI agents are designed with a structured three-layer architecture:


  • Perception Layer: Gathers data from various sources, including real-time inputs, external APIs, and historical datasets, forming the foundation for decision-making.

  • Reasoning Layer: Utilizes machine learning algorithms to analyze data, detect patterns, and generate adaptive responses based on evolving conditions.

  • Execution Layer: Interfaces with digital systems, applications, or automated processes to implement decisions and carry out tasks efficiently.


AI agents use integration tools to help them work across different platforms, a concept ChainsAtlas brings to Web3 and Blockchain for the first time. They rely on virtualization software to turn instructions into formats each system can understand. This makes sure everything works smoothly together, no matter the environment.



Automated Workflows


AI agents go beyond regular automation by working through complex systems independently, adapting to real-time conditions, and executing tasks without manual intervention.


For example, if an e-commerce platform experiences server congestion, an AI agent can dynamically reroute data processing to a less burdened cloud service, ensuring seamless operations. Once traffic stabilizes, it reallocates resources to maintain optimal efficiency while minimizing costs. These intelligent workflows enable AI agents to optimize performance, enhance responsiveness, and streamline decision-making across diverse environments.


Security Mechanisms


AI Agents that operate independently across digital environments present significant security challenges. To mitigate risks, AI agents employ several protective strategies:


  • Isolated Execution Environments: Contain processes to prevent threats from spreading if an agent is compromised.

  • Verified Decision Pathways: Utilize formal verification to ensure logical accuracy before executing critical actions.

  • Multi-Factor Authentication: Require multiple verification steps before performing high-risk operations.

  • Anomaly Detection Systems: Continuously monitor for irregular or malicious behavior, enhancing security and resilience.


These mechanisms ensure AI agents operate safely, maintaining integrity and reliability in dynamic, high-stakes environments.


AI Agents in 2025


In 2025, AI agents will now process millions of tasks daily, driving the evolution of autonomous systems. AI agents go beyond traditional automation, operating intelligently across digital spaces without constant human input. For instance, in the financial sector, AI agents handle approximately 65% of daily trades, processing market data 1,000 times faster than humans. The continuous integration of AI into various industries pushes the boundaries of efficiency, scalability, and real-time decision-making, creating an innovative, more adaptive ecosystem that evolves independently.


Other Agent Use Cases


Infrastructure isn’t the only playground for AI agents. Most attention goes to tools that let you spin up autonomous workflows, coordinate dev tasks, or manage systems at scale, but agents are showing up everywhere.

Some agents are already deploying websites autonomously, such as DevGPT. Others write and iterate on long-form content through tools like Storywrite AI or SudoWrite. A few are experimenting with trading strategies, including AIXBT for onchain execution and AI Trader Agent for simulated stock trading. Then there are agents focused on marketing tasks, generating ad copy, scheduling posts, and even launching full campaigns, as seen with AlbertAI. Some are designed purely for companionship or entertainment, like Replika, which simulates human-like conversations and emotional support.


Now, if we zoom in on Web3, we begin to encounter a new kind of agent; agents are already engaged in executing arbitrage, managing liquidity, sniping mints, and bridging assets on various blockchains. These agents are pushing the boundaries of what’s possible in decentralized environments. Having already mentioned AIXBT, it’s worth touching on other Web3 agents that are reshaping the landscape, such as those seen in platforms like Autonolas, Sentient, VADER, and Sophia, where the evolution of agent-based systems is creating new opportunities for automation and coordination.


Our next article will delve deeper into the growing world of agents and the many use cases already emerging across Web3.


Conclusion


AI agents are redefining automation. They enable intelligent, autonomous decision-making across digital ecosystems. They enhance efficiency, scalability, and resilience in real-time operations through structured architectures, adaptive workflows, and robust security mechanisms. As they evolve, industries like finance, healthcare, and logistics will see increased innovation. However, maintaining their security, ethical governance, and seamless interoperability will be crucial to unlocking their full potential. With ongoing advancements, AI agents will become the foundation of next-generation autonomous systems, shaping a future driven by intelligent automation.


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