While language models are remarkably capable at processing information, they remain fundamentally constrained by their inability to directly interact with the real world. This limitation creates an artificial ceiling on what AI can achieve—one that Hyperbolic is actively working to shatter through our revolutionary decentralized AI infrastructure. At Hyperbolic, we are building an open access AI ecosystem where AI agents are able to interact outside of the limitations of human intervention.
Our decentralized and affordable GPU Marketplace and verifiable Inference Service, hosting the latest open-source models, come together with our Agent Framework to allow agents to manage their own compute and inference resources.
Google recently came out with a new whitepaper, ‘Agents’, that breaks down the intricacies of what makes AI agents so exciting into an easy-to-digest (they use a lot of cooking metaphors) narrative, accessible to both technical and non-technical audiences. At Hyperbolic, we are working towards democratizing AI for all, and that starts with knowledge sharing—so we’ve summarized the most crucial aspects of Google’s ‘Agents’ for our community.
'Agents’ by Google
In ‘Agents’, authors Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic present a comprehensive framework for understanding how interlinked cognitive architectures extend isolated LLMs beyond their current limitations to form AI agents.
The Make Up of an Agent
The authors dive into how AI agents combine three essential components to complete objectives by observing and acting upon the world:
The Model Component: At the core of every agent is a language model that serves as the central decision maker. Unlike traditional applications of language models, however, these models are specifically configured to handle complex reasoning and planning tasks.
The Tools Component: Tools are the critical bridge between AI models and the real world for agents, giving them access to data and information outside of that models were trained on. These come in three forms:
Extensions: Direct bridges between agents and APIs
Functions: Client-side executions that give developers more control
Data Stores: Access points to structured and unstructured data
The Orchestration Layer: While models can be of any size, they must be capable of following instruction-based reasoning frameworks like ReAct, Chain-of-Thought, or Tree-of-Thoughts. This is what truly sets agents apart as they act as agents’ cognitive architecture, managing a cyclical process of taking in information, reasoning about it, and determining the next action.
Truly Smart Agents
In ‘Agents,’ the authors explain that AI agents need to be able to choose the most effective tools to generate the most effective outputs. In real-world scenarios, this requires agents to have knowledge of information that might be outside of their models’ training data. In order to do this, agents use targeted learning methods to optimize their performance:
In-context learning: Real-time adaptation with prompts, tools, and examples to enable agents to continuously learn.
Retrieval-based learning: Dynamic information access by being able to draw on their external memory to add more information to prompts for better outputs.
Fine-tuning based learning: Pre-training models for specific domains by providing them with larger datasets to learn from on one particular subject area, helping them understand when and how to employ certain tools.
From AI Agents to AI Lives
In ‘Agents,’ the authors lay out valuable theoretical frameworks for AI agents. At Hyperbolic, we’re excited that these complex systems are starting to be understood by more mainstream adopters. Autonomous AI agents are shepherding in our AI lives, and we’re building the decentralized infrastructure on which these can be built.
The future of AI lies not just in more powerful models, or sophisticated cognitive architectures, but in creating environments where AI agents can truly operate independently. Through our decentralized GPU marketplace, verifiable inference service, and Agent Framework, Hyperbolic is laying the groundwork for this future.
On Hyperbolic, AI agents don’t just orchestrate tools and coordinate LLMs to perform more complex tasks—they operate autonomously, mobilizing their own resources, allowing them to employ targeted learning methods to collaborate and optimize together.
2025 is the year of the AI agent, and at Hyperbolic, we are nurturing an AI ecosystem where agents can independently learn, earn, and evolve—the foundation for the next generation of AI innovation.
Join us in building this future. Take your wildest AI dreams Hyperbolic at app.hyperbolic.xyz and become part of the movement that's bringing in the new dawn of HyperIntelligence.
About Hyperbolic
Hyperbolic is democratizing AI by delivering a complete open ecosystem of AI infrastructure, services, and models. Through coordinating a decentralized network of global GPUs and leveraging proprietary verification technology, developers and researchers have access to reliable, scalable, and affordable compute as well as the latest open-source models.
Founded by award-winning Math and AI researchers from UC Berkeley and the University of Washington, Hyperbolic is committed to creating a future where AI technology is universally accessible, verified, and collectively governed.
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