What is Fetch and how does it work?
Fetch.ai is a decentralized platform looking to blend artificial intelligence, blockchain technology, and multi-agent systems under a cohesive umbrella. Fetch seeks to address the many inefficiencies in today’s digital and economic systems by enabling a new layer of autonomous machine-to-machine interaction through an innovative product suite and series of highly capable releases. Building on a combination of advanced cryptographic protocols, distributed ledger technology, and AI frameworks, Fetch’s vision is to connect diverse services and data sources in a self-organizing, peer-to-peer network.
Core Technology and Architecture
Fetch operates on a high-performance blockchain designed to handle complex computations and high transaction throughput. At its core are autonomous software agents that execute tasks with minimal human oversight. They rely on a robust set of smart contracts and protocols to identify resources, negotiate deals, and securely share data in real time. This layered design empowers individuals and businesses to automate a wide array of processes. For instance, in logistics, agents can coordinate shipments or maintain real-time inventory; in mobility, they can streamline ride-sharing and micro-transport; in finance, they can handle payments and verify digital identities.
Central to Fetch’s offering is its AI Engine, which provides the platform’s machine-learning backbone. The AI Engine facilitates model training, data analysis, and predictive analytics that agents can tap into. By integrating AI methodologies directly into the network’s architecture, Fetch aims to move beyond the typical “smart contract” paradigm.
Instead of limiting on-chain activity to simple conditional logic, the AI Engine allows agents to analyze complex datasets and make intelligent decisions autonomously. This is crucial for applications like dynamic pricing, where machine-learning models can adjust costs based on shifting market conditions, or for recommendation systems that tailor services to users’ preferences.
Another important product in Fetch’s tech stack is DeltaV, which focuses on the concept of distributed or federated machine learning. Instead of collecting and centralizing data (which can create privacy and security concerns), DeltaV enables various parties to train models while keeping sensitive information in local silos collaboratively.
From a technical standpoint, DeltaV orchestrates secure multi-party computations, ensuring that no single entity gains unnecessary visibility into others’ raw data. This aligns with a growing industry push toward data protection and user privacy, while simultaneously enabling the collective intelligence of the network to grow.
The problems Fetch seeks to solve align closely with the major challenges plaguing both blockchain and AI in their current forms. First, there is the scalability issue: existing blockchains often struggle with high transaction volumes and complex computations. Fetch aims to address this through a specialized consensus mechanism and architecture optimized for agent interactions. Second, data ownership and privacy remain challenging. By leveraging the privacy-preserving principles of federated learning and advanced cryptography, the Fetch ecosystem aspires to give end-users and data owners control over their information, mitigating risks of data breaches or misuse.
Another major challenge involves fragmented and siloed data. In many industries, information is locked away in centralized databases, making collaboration and holistic insights difficult. Fetch’s multi-agent system, combined with its AI Engine, is designed to break down these silos. Agents can tap into multiple data streams—some provided by other agents, some from publicly accessible sources—without exposing confidential details. This fosters an environment where cross-industry and cross-platform collaboration is both frictionless and secure.
The platform’s consensus and incentive mechanisms also address the fundamental question of trust in decentralized systems. Fetch’s blockchain incorporates a proof-of-stake model, enabling validators to earn rewards for securing the network. This encourages participants to act honestly and efficiently. Meanwhile, specialized smart contracts coordinate how agents discover, negotiate, and finalize transactions. The result is a trust-minimized environment that can be extended into real-world use cases, from verifying supply chain authenticity to automating settlements in decentralized finance (DeFi).
By combining powerful on-chain logic with off-chain machine learning, Fetch enables applications that go beyond basic token swaps. Agents can, for example, learn and adapt to user behavior or market trends in real time.
With DeltaV’s privacy-preserving training, multiple organizations—say, hospitals in different regions—could collaboratively refine a machine-learning model for medical research without ever sharing patient records directly. This kind of AI synergy, powered by decentralized coordination, is a core aspect of what Fetch defines as “collective intelligence.”
Overview of Fetch’s position in the DeAI space
Decentralized AI (DeAI) protocols seek to combine the benefits of blockchain—security, transparency, and decentralized governance—with the transformative capabilities of machine learning.
While projects in this domain vary in focus and approach, many of them share a fundamental aim: to reduce the barriers to AI development and data sharing by eliminating centralized intermediaries. Initiatives like SingularityNET, for example, strive to build global marketplaces for AI services, enabling developers to monetize their algorithms, while users can discover and integrate cutting-edge AI tools in a trustless environment. Meanwhile, platforms such as Ocean Protocol concentrate on tokenizing data and facilitating data sharing in a privacy-preserving manner, which is invaluable for machine-learning applications that rely on large, diverse datasets.
Golem is another major player, originally conceived as a distributed compute resource network. It lets participants rent out spare CPU and GPU cycles, effectively democratizing access to computing power that can be directed toward AI model training or other computationally intensive tasks. Numerai, on the other hand, leverages cryptographic techniques and crowd-sourced AI modeling to manage a hedge fund, incentivizing data scientists around the world to collectively improve predictive models without revealing the underlying data. In each of these use cases, the interplay between decentralized incentives and AI-driven innovation is core to the project’s identity.
Differentiators
Fetch distinguishes itself with its agent-based paradigm, where AI-powered entities interact autonomously across multiple domains. For example, a Fetch agent could source data from Ocean Protocol, process it using Golem’s computing power, and autonomously execute smart contract transactions—all within a trust-minimized framework. Moreover, Fetch’s architecture promotes cross-protocol interoperability, enabling agents to interact with external blockchains and data marketplaces. This integration fosters a seamless, interconnected AI ecosystem rather than isolated silos.
The “agent-based” paradigm is a distinctive selling point in the DeAI space. By combining AI logic with blockchain-based smart contracts, Fetch aims to automate more sophisticated tasks than just “buying and selling” data or compute resources. This can involve dynamic pricing, real-time negotiations, and complex coordination of assets across multiple stakeholders. In scenarios like decentralized supply chain management, an agent could continuously assess shipping options, inventory levels, and real-time market data to optimize logistics with minimal human intervention. This adaptability is where Fetch’s approach may bring something novel to DeAI.
Another notable factor is how Fetch addresses interoperability. Many DeAI protocols are siloed, focusing on one aspect—like data marketplaces, computation, or financial modeling. Fetch’s architecture encourages cross-protocol collaboration, allowing its agents to interface not just with the Fetch blockchain, but with external blockchains and data sources. This could create a more fluid ecosystem where agents seamlessly tap into specialized services from other DeAI projects. If successful, this interconnectedness could accelerate the entire sector by fostering a larger network effect.
Privacy
Privacy considerations are also increasingly central in decentralized AI, as well as its obvious utility across every major crypto application.
Ocean Protocol and Numerai both use cryptographic techniques to keep proprietary data hidden while still enabling collaboration. Fetch’s adoption of tools like DeltaV for federated learning builds upon these concepts. Rather than forcing everyone to pool sensitive information into a single repository, DeltaV ensures that each participant retains ownership of their data, contributing only the learned parameters needed to improve models. This resonates with a broader movement in the AI world toward privacy-preserving computations, a key differentiator for protocols that want to meet real-world regulatory and ethical standards.
Where many DeAI projects concentrate on the technology alone, Fetch aims to tie in economic incentives more deeply through its agent model. Agents earn or spend tokens in pursuit of tasks that might require data, computation, or other resources. This token-driven economy can reward participants who offer valuable services—like curated datasets or specialized AI modules—while ensuring that inefficiencies are minimized. Over time, this economic layer could encourage the emergence of specialized agent networks or marketplaces catering to niche industries, each operating under Fetch’s overarching framework.
Governance
Equally important is governance. Some DeAI protocols use on-chain voting systems or DAO structures to decide network parameters or which AI models receive funding. Fetch’s model allows for more granular forms of agent-based governance, where each agent’s preferences and behaviors shape outcomes in a distributed fashion. This might lead to emergent “micro-governance” processes, in which communities of agents decide on best practices or co-develop new protocols without needing a single hierarchical authority.
Because the field is evolving rapidly, collaborative experimentation is essential. Fetch might integrate with or draw insights from newer DeAI entrants that tackle specialized challenges—like privacy-preserving computation, knowledge graph reasoning, or generative AI models. By weaving these functionalities into its agent-based system, Fetch can stay at the forefront of innovation, offering a platform that is both broad in scope and open to novel tool sets. This keeps pace with the constant advance of AI research, which is crucial for staying relevant in a sector prone to rapid change.
If successful, agents running on Fetch’s network would orchestrate increasingly complex tasks, tapping into diverse AI and blockchain platforms without the friction of centralized control. Fetch stands to fill a critical role in orchestrating these disparate elements into cohesive, agent-driven ecosystems. In doing so, it doesn’t merely seek to replicate what other DeAI protocols have done but rather aims to supercharge their functionalities by providing a versatile, agent-based infrastructure that can integrate seamlessly with a broader network of decentralized services and marketplaces.
Fetch’s technology and progress
One of the most notable recent developments in the Fetch ecosystem is the launch of ASI-1 Mini, which builds on Fetch’s commitment to integrating advanced AI into a new type of framework. According to Fetch’s blog posts, ASI-1 Mini stands out due to its knowledge graph integration—a feature that allows the model to tap into structured data sources for more contextually accurate responses. This is a significant advancement in conversational AI, particularly when combined with the “agentic” approach outlined by Fetch. Agents running on the Fetch network can leverage this language model for tasks like complex negotiations or context-driven recommendations, all while maintaining a secure, trust-minimized environment.
ASI-1 Mini is branded as the first Web3 LLM specifically designed for agentic AI. In traditional LLMs, information flow is often confined to a centralized system, where the model has no autonomous means of calling upon external resources. By contrast, ASI-1 Mini can interact with smart contracts, data sources, and other agents on the Fetch blockchain. This synergy allows for a more dynamic, real-time AI experience. For instance, an agent might query the model to refine contract details, validate data from a knowledge graph, and then autonomously commit a transaction if certain conditions are met. The result is an ecosystem where intelligence is fluid and decentralized.
The knowledge graph integration is particularly noteworthy. In typical AI systems, language models operate on large text corpora, but they have limited “understanding” of the relationships between entities. By incorporating a knowledge graph, ASI-1 Mini can reference structured, semantically rich data to answer queries or generate advice with greater precision. This makes it especially useful for tasks that require context, such as supply chain management or financial forecasting, where an agent might need to verify a product’s origin, price history, and performance track record before completing a transaction.
These capabilities are complemented by AgentVerse, another key initiative that has seen recent enhancements in search functionality and user engagement. AgentVerse is described in the blogs as a platform that allows users to interact with AI agents in a more intuitive manner. The updates focus on making the discovery of relevant agents, data sources, and knowledge bases more seamless. By implementing new search algorithms and user interface improvements, Fetch aims to lower the barrier to entry for both casual users and developers looking to build complex agent-based applications.
Enhanced search not only benefits end-users but also has profound implications for how agents coordinate among themselves. If each agent can efficiently find other agents based on specialized tags or skill sets, the entire network gains “collective intelligence.” Agents can form ad hoc collaborations to solve multi-step problems, drawing on each other’s specialized knowledge or access to different data streams. The recent blog updates detail how these search improvements will likely boost the overall efficiency of the Fetch ecosystem, by making agent discovery more scalable and less prone to bottlenecks.
AgentVerse updates also delve into the performance improvements aimed at handling higher volumes of queries and agent interactions. As the number of participants in the Fetch network grows, scalability becomes critical. The blog posts highlight optimizations in communication protocols, query handling, and data indexing that ensure real-time responsiveness. This aligns with Fetch’s broader mission: a scalable, decentralized platform for AI-driven agent interactions that can cater to enterprise-level use cases. Even for smaller projects or individual developers, the promise of low-latency, high-throughput systems is essential for user adoption and trust.
These performance enhancements feed directly into the capabilities of ASI-1 Mini. By allowing the LLM to interface with a faster, more robust agent network, developers can build applications where real-time data flows into the model for instantaneous analysis. Imagine an e-commerce setting where prices and stock levels update in real time, and AI-driven agents quickly process these changes to offer shoppers personalized deals. With knowledge graph integration, these deals could be validated against product attributes, vendor reliability, and consumer reviews—unifying siloed information under a single, decentralized framework.
One interesting aspect raised in the blogs is how these agent-centric improvements also foster a more “community-driven” approach to AI development. In many centralized AI platforms, users have limited visibility into how models make decisions. With AgentVerse and ASI-1 Mini, there’s potential for more transparent training processes and data usage policies. By leveraging Fetch’s tokenomics and decentralized governance mechanisms, communities can vote on which data sources to integrate into the knowledge graph or how to allocate resources for model retraining.
From a developer standpoint, the combined features of ASI-1 Mini, AgentVerse, and the broader Fetch tech stack reduce the complexities of building advanced AI applications on a blockchain. The blog posts underscore the availability of SDKs, documentation, and turnkey solutions that aim to simplify the integration of AI agents into existing workflows. This is in line with Fetch’s overarching strategy: to create a cohesive ecosystem where organizations can experiment with decentralized AI without needing to piece together multiple, often incompatible tools.
Security and privacy also benefit from these upgrades. The knowledge graph integration in ASI-1 Mini can be made permissioned, ensuring only authorized agents can modify or read sensitive data. Meanwhile, the federated learning principles behind the DeltaV product can be adapted to train or update the AI components without centralizing all user data in one place. This means that AgentVerse is not just more capable—it’s also more robust in terms of data governance and compliance with global privacy regulations.
Finally, these developments bolster Fetch’s long-term vision of building a self-sustaining digital ecosystem where intelligent agents coordinate autonomously. By introducing a specialized Web3 LLM, refining how agents discover each other, and improving overall system performance, Fetch continues to refine its multi-layered approach to decentralized AI. Each innovation highlights the synergy between blockchain, AI, and autonomous agents - continuing to solidify Fetch’s position as a versatile platform in the rapidly evolving DeAI space. Through these advancements, the project seems well-positioned to address real-world use cases that demand scalable, trustworthy, and highly intelligent systems.
Disclaimer: This research report is exactly that — a research report. It is not intended to serve as financial advice, nor should you blindly assume that any of the information is accurate without confirming through your own research. Bitcoin, cryptocurrencies, and other digital assets are incredibly risky and nothing in this report should be considered an endorsement to buy or sell any asset. Never invest more than you are willing to lose and understand the risk that you are taking. Do your own research. All information in this report is for educational purposes only and should not be the basis for any investment decisions that you make.
What is Fetch and how does it work?
Fetch.ai is a decentralized platform looking to blend artificial intelligence, blockchain technology, and multi-agent systems under a cohesive umbrella. Fetch seeks to address the many inefficiencies in today’s digital and economic systems by enabling a new layer of autonomous machine-to-machine interaction through an innovative product suite and series of highly capable releases. Building on a combination of advanced cryptographic protocols, distributed ledger technology, and AI frameworks, Fetch’s vision is to connect diverse services and data sources in a self-organizing, peer-to-peer network.
Core Technology and Architecture
Fetch operates on a high-performance blockchain designed to handle complex computations and high transaction throughput. At its core are autonomous software agents that execute tasks with minimal human oversight. They rely on a robust set of smart contracts and protocols to identify resources, negotiate deals, and securely share data in real time. This layered design empowers individuals and businesses to automate a wide array of processes. For instance, in logistics, agents can coordinate shipments or maintain real-time inventory; in mobility, they can streamline ride-sharing and micro-transport; in finance, they can handle payments and verify digital identities.
Central to Fetch’s offering is its AI Engine, which provides the platform’s machine-learning backbone. The AI Engine facilitates model training, data analysis, and predictive analytics that agents can tap into. By integrating AI methodologies directly into the network’s architecture, Fetch aims to move beyond the typical “smart contract” paradigm.
Instead of limiting on-chain activity to simple conditional logic, the AI Engine allows agents to analyze complex datasets and make intelligent decisions autonomously. This is crucial for applications like dynamic pricing, where machine-learning models can adjust costs based on shifting market conditions, or for recommendation systems that tailor services to users’ preferences.
Another important product in Fetch’s tech stack is DeltaV, which focuses on the concept of distributed or federated machine learning. Instead of collecting and centralizing data (which can create privacy and security concerns), DeltaV enables various parties to train models while keeping sensitive information in local silos collaboratively.
From a technical standpoint, DeltaV orchestrates secure multi-party computations, ensuring that no single entity gains unnecessary visibility into others’ raw data. This aligns with a growing industry push toward data protection and user privacy, while simultaneously enabling the collective intelligence of the network to grow.
The problems Fetch seeks to solve align closely with the major challenges plaguing both blockchain and AI in their current forms. First, there is the scalability issue: existing blockchains often struggle with high transaction volumes and complex computations. Fetch aims to address this through a specialized consensus mechanism and architecture optimized for agent interactions. Second, data ownership and privacy remain challenging. By leveraging the privacy-preserving principles of federated learning and advanced cryptography, the Fetch ecosystem aspires to give end-users and data owners control over their information, mitigating risks of data breaches or misuse.
Another major challenge involves fragmented and siloed data. In many industries, information is locked away in centralized databases, making collaboration and holistic insights difficult. Fetch’s multi-agent system, combined with its AI Engine, is designed to break down these silos. Agents can tap into multiple data streams—some provided by other agents, some from publicly accessible sources—without exposing confidential details. This fosters an environment where cross-industry and cross-platform collaboration is both frictionless and secure.
The platform’s consensus and incentive mechanisms also address the fundamental question of trust in decentralized systems. Fetch’s blockchain incorporates a proof-of-stake model, enabling validators to earn rewards for securing the network. This encourages participants to act honestly and efficiently. Meanwhile, specialized smart contracts coordinate how agents discover, negotiate, and finalize transactions. The result is a trust-minimized environment that can be extended into real-world use cases, from verifying supply chain authenticity to automating settlements in decentralized finance (DeFi).
By combining powerful on-chain logic with off-chain machine learning, Fetch enables applications that go beyond basic token swaps. Agents can, for example, learn and adapt to user behavior or market trends in real time.
With DeltaV’s privacy-preserving training, multiple organizations—say, hospitals in different regions—could collaboratively refine a machine-learning model for medical research without ever sharing patient records directly. This kind of AI synergy, powered by decentralized coordination, is a core aspect of what Fetch defines as “collective intelligence.”
Overview of Fetch’s position in the DeAI space
Decentralized AI (DeAI) protocols seek to combine the benefits of blockchain—security, transparency, and decentralized governance—with the transformative capabilities of machine learning.
While projects in this domain vary in focus and approach, many of them share a fundamental aim: to reduce the barriers to AI development and data sharing by eliminating centralized intermediaries. Initiatives like SingularityNET, for example, strive to build global marketplaces for AI services, enabling developers to monetize their algorithms, while users can discover and integrate cutting-edge AI tools in a trustless environment. Meanwhile, platforms such as Ocean Protocol concentrate on tokenizing data and facilitating data sharing in a privacy-preserving manner, which is invaluable for machine-learning applications that rely on large, diverse datasets.
Golem is another major player, originally conceived as a distributed compute resource network. It lets participants rent out spare CPU and GPU cycles, effectively democratizing access to computing power that can be directed toward AI model training or other computationally intensive tasks. Numerai, on the other hand, leverages cryptographic techniques and crowd-sourced AI modeling to manage a hedge fund, incentivizing data scientists around the world to collectively improve predictive models without revealing the underlying data. In each of these use cases, the interplay between decentralized incentives and AI-driven innovation is core to the project’s identity.
Differentiators
Fetch distinguishes itself with its agent-based paradigm, where AI-powered entities interact autonomously across multiple domains. For example, a Fetch agent could source data from Ocean Protocol, process it using Golem’s computing power, and autonomously execute smart contract transactions—all within a trust-minimized framework. Moreover, Fetch’s architecture promotes cross-protocol interoperability, enabling agents to interact with external blockchains and data marketplaces. This integration fosters a seamless, interconnected AI ecosystem rather than isolated silos.
The “agent-based” paradigm is a distinctive selling point in the DeAI space. By combining AI logic with blockchain-based smart contracts, Fetch aims to automate more sophisticated tasks than just “buying and selling” data or compute resources. This can involve dynamic pricing, real-time negotiations, and complex coordination of assets across multiple stakeholders. In scenarios like decentralized supply chain management, an agent could continuously assess shipping options, inventory levels, and real-time market data to optimize logistics with minimal human intervention. This adaptability is where Fetch’s approach may bring something novel to DeAI.
Another notable factor is how Fetch addresses interoperability. Many DeAI protocols are siloed, focusing on one aspect—like data marketplaces, computation, or financial modeling. Fetch’s architecture encourages cross-protocol collaboration, allowing its agents to interface not just with the Fetch blockchain, but with external blockchains and data sources. This could create a more fluid ecosystem where agents seamlessly tap into specialized services from other DeAI projects. If successful, this interconnectedness could accelerate the entire sector by fostering a larger network effect.
Privacy
Privacy considerations are also increasingly central in decentralized AI, as well as its obvious utility across every major crypto application.
Ocean Protocol and Numerai both use cryptographic techniques to keep proprietary data hidden while still enabling collaboration. Fetch’s adoption of tools like DeltaV for federated learning builds upon these concepts. Rather than forcing everyone to pool sensitive information into a single repository, DeltaV ensures that each participant retains ownership of their data, contributing only the learned parameters needed to improve models. This resonates with a broader movement in the AI world toward privacy-preserving computations, a key differentiator for protocols that want to meet real-world regulatory and ethical standards.
Where many DeAI projects concentrate on the technology alone, Fetch aims to tie in economic incentives more deeply through its agent model. Agents earn or spend tokens in pursuit of tasks that might require data, computation, or other resources. This token-driven economy can reward participants who offer valuable services—like curated datasets or specialized AI modules—while ensuring that inefficiencies are minimized. Over time, this economic layer could encourage the emergence of specialized agent networks or marketplaces catering to niche industries, each operating under Fetch’s overarching framework.
Governance
Equally important is governance. Some DeAI protocols use on-chain voting systems or DAO structures to decide network parameters or which AI models receive funding. Fetch’s model allows for more granular forms of agent-based governance, where each agent’s preferences and behaviors shape outcomes in a distributed fashion. This might lead to emergent “micro-governance” processes, in which communities of agents decide on best practices or co-develop new protocols without needing a single hierarchical authority.
Because the field is evolving rapidly, collaborative experimentation is essential. Fetch might integrate with or draw insights from newer DeAI entrants that tackle specialized challenges—like privacy-preserving computation, knowledge graph reasoning, or generative AI models. By weaving these functionalities into its agent-based system, Fetch can stay at the forefront of innovation, offering a platform that is both broad in scope and open to novel tool sets. This keeps pace with the constant advance of AI research, which is crucial for staying relevant in a sector prone to rapid change.
If successful, agents running on Fetch’s network would orchestrate increasingly complex tasks, tapping into diverse AI and blockchain platforms without the friction of centralized control. Fetch stands to fill a critical role in orchestrating these disparate elements into cohesive, agent-driven ecosystems. In doing so, it doesn’t merely seek to replicate what other DeAI protocols have done but rather aims to supercharge their functionalities by providing a versatile, agent-based infrastructure that can integrate seamlessly with a broader network of decentralized services and marketplaces.
Fetch’s technology and progress
One of the most notable recent developments in the Fetch ecosystem is the launch of ASI-1 Mini, which builds on Fetch’s commitment to integrating advanced AI into a new type of framework. According to Fetch’s blog posts, ASI-1 Mini stands out due to its knowledge graph integration—a feature that allows the model to tap into structured data sources for more contextually accurate responses. This is a significant advancement in conversational AI, particularly when combined with the “agentic” approach outlined by Fetch. Agents running on the Fetch network can leverage this language model for tasks like complex negotiations or context-driven recommendations, all while maintaining a secure, trust-minimized environment.
ASI-1 Mini is branded as the first Web3 LLM specifically designed for agentic AI. In traditional LLMs, information flow is often confined to a centralized system, where the model has no autonomous means of calling upon external resources. By contrast, ASI-1 Mini can interact with smart contracts, data sources, and other agents on the Fetch blockchain. This synergy allows for a more dynamic, real-time AI experience. For instance, an agent might query the model to refine contract details, validate data from a knowledge graph, and then autonomously commit a transaction if certain conditions are met. The result is an ecosystem where intelligence is fluid and decentralized.
The knowledge graph integration is particularly noteworthy. In typical AI systems, language models operate on large text corpora, but they have limited “understanding” of the relationships between entities. By incorporating a knowledge graph, ASI-1 Mini can reference structured, semantically rich data to answer queries or generate advice with greater precision. This makes it especially useful for tasks that require context, such as supply chain management or financial forecasting, where an agent might need to verify a product’s origin, price history, and performance track record before completing a transaction.
These capabilities are complemented by AgentVerse, another key initiative that has seen recent enhancements in search functionality and user engagement. AgentVerse is described in the blogs as a platform that allows users to interact with AI agents in a more intuitive manner. The updates focus on making the discovery of relevant agents, data sources, and knowledge bases more seamless. By implementing new search algorithms and user interface improvements, Fetch aims to lower the barrier to entry for both casual users and developers looking to build complex agent-based applications.
Enhanced search not only benefits end-users but also has profound implications for how agents coordinate among themselves. If each agent can efficiently find other agents based on specialized tags or skill sets, the entire network gains “collective intelligence.” Agents can form ad hoc collaborations to solve multi-step problems, drawing on each other’s specialized knowledge or access to different data streams. The recent blog updates detail how these search improvements will likely boost the overall efficiency of the Fetch ecosystem, by making agent discovery more scalable and less prone to bottlenecks.
AgentVerse updates also delve into the performance improvements aimed at handling higher volumes of queries and agent interactions. As the number of participants in the Fetch network grows, scalability becomes critical. The blog posts highlight optimizations in communication protocols, query handling, and data indexing that ensure real-time responsiveness. This aligns with Fetch’s broader mission: a scalable, decentralized platform for AI-driven agent interactions that can cater to enterprise-level use cases. Even for smaller projects or individual developers, the promise of low-latency, high-throughput systems is essential for user adoption and trust.
These performance enhancements feed directly into the capabilities of ASI-1 Mini. By allowing the LLM to interface with a faster, more robust agent network, developers can build applications where real-time data flows into the model for instantaneous analysis. Imagine an e-commerce setting where prices and stock levels update in real time, and AI-driven agents quickly process these changes to offer shoppers personalized deals. With knowledge graph integration, these deals could be validated against product attributes, vendor reliability, and consumer reviews—unifying siloed information under a single, decentralized framework.
One interesting aspect raised in the blogs is how these agent-centric improvements also foster a more “community-driven” approach to AI development. In many centralized AI platforms, users have limited visibility into how models make decisions. With AgentVerse and ASI-1 Mini, there’s potential for more transparent training processes and data usage policies. By leveraging Fetch’s tokenomics and decentralized governance mechanisms, communities can vote on which data sources to integrate into the knowledge graph or how to allocate resources for model retraining.
From a developer standpoint, the combined features of ASI-1 Mini, AgentVerse, and the broader Fetch tech stack reduce the complexities of building advanced AI applications on a blockchain. The blog posts underscore the availability of SDKs, documentation, and turnkey solutions that aim to simplify the integration of AI agents into existing workflows. This is in line with Fetch’s overarching strategy: to create a cohesive ecosystem where organizations can experiment with decentralized AI without needing to piece together multiple, often incompatible tools.
Security and privacy also benefit from these upgrades. The knowledge graph integration in ASI-1 Mini can be made permissioned, ensuring only authorized agents can modify or read sensitive data. Meanwhile, the federated learning principles behind the DeltaV product can be adapted to train or update the AI components without centralizing all user data in one place. This means that AgentVerse is not just more capable—it’s also more robust in terms of data governance and compliance with global privacy regulations.
Finally, these developments bolster Fetch’s long-term vision of building a self-sustaining digital ecosystem where intelligent agents coordinate autonomously. By introducing a specialized Web3 LLM, refining how agents discover each other, and improving overall system performance, Fetch continues to refine its multi-layered approach to decentralized AI. Each innovation highlights the synergy between blockchain, AI, and autonomous agents - continuing to solidify Fetch’s position as a versatile platform in the rapidly evolving DeAI space. Through these advancements, the project seems well-positioned to address real-world use cases that demand scalable, trustworthy, and highly intelligent systems.
Disclaimer: This research report is exactly that — a research report. It is not intended to serve as financial advice, nor should you blindly assume that any of the information is accurate without confirming through your own research. Bitcoin, cryptocurrencies, and other digital assets are incredibly risky and nothing in this report should be considered an endorsement to buy or sell any asset. Never invest more than you are willing to lose and understand the risk that you are taking. Do your own research. All information in this report is for educational purposes only and should not be the basis for any investment decisions that you make.
What is Fetch and how does it work?
Fetch.ai is a decentralized platform looking to blend artificial intelligence, blockchain technology, and multi-agent systems under a cohesive umbrella. Fetch seeks to address the many inefficiencies in today’s digital and economic systems by enabling a new layer of autonomous machine-to-machine interaction through an innovative product suite and series of highly capable releases. Building on a combination of advanced cryptographic protocols, distributed ledger technology, and AI frameworks, Fetch’s vision is to connect diverse services and data sources in a self-organizing, peer-to-peer network.
Core Technology and Architecture
Fetch operates on a high-performance blockchain designed to handle complex computations and high transaction throughput. At its core are autonomous software agents that execute tasks with minimal human oversight. They rely on a robust set of smart contracts and protocols to identify resources, negotiate deals, and securely share data in real time. This layered design empowers individuals and businesses to automate a wide array of processes. For instance, in logistics, agents can coordinate shipments or maintain real-time inventory; in mobility, they can streamline ride-sharing and micro-transport; in finance, they can handle payments and verify digital identities.
Central to Fetch’s offering is its AI Engine, which provides the platform’s machine-learning backbone. The AI Engine facilitates model training, data analysis, and predictive analytics that agents can tap into. By integrating AI methodologies directly into the network’s architecture, Fetch aims to move beyond the typical “smart contract” paradigm.
Instead of limiting on-chain activity to simple conditional logic, the AI Engine allows agents to analyze complex datasets and make intelligent decisions autonomously. This is crucial for applications like dynamic pricing, where machine-learning models can adjust costs based on shifting market conditions, or for recommendation systems that tailor services to users’ preferences.
Another important product in Fetch’s tech stack is DeltaV, which focuses on the concept of distributed or federated machine learning. Instead of collecting and centralizing data (which can create privacy and security concerns), DeltaV enables various parties to train models while keeping sensitive information in local silos collaboratively.
From a technical standpoint, DeltaV orchestrates secure multi-party computations, ensuring that no single entity gains unnecessary visibility into others’ raw data. This aligns with a growing industry push toward data protection and user privacy, while simultaneously enabling the collective intelligence of the network to grow.
The problems Fetch seeks to solve align closely with the major challenges plaguing both blockchain and AI in their current forms. First, there is the scalability issue: existing blockchains often struggle with high transaction volumes and complex computations. Fetch aims to address this through a specialized consensus mechanism and architecture optimized for agent interactions. Second, data ownership and privacy remain challenging. By leveraging the privacy-preserving principles of federated learning and advanced cryptography, the Fetch ecosystem aspires to give end-users and data owners control over their information, mitigating risks of data breaches or misuse.
Another major challenge involves fragmented and siloed data. In many industries, information is locked away in centralized databases, making collaboration and holistic insights difficult. Fetch’s multi-agent system, combined with its AI Engine, is designed to break down these silos. Agents can tap into multiple data streams—some provided by other agents, some from publicly accessible sources—without exposing confidential details. This fosters an environment where cross-industry and cross-platform collaboration is both frictionless and secure.
The platform’s consensus and incentive mechanisms also address the fundamental question of trust in decentralized systems. Fetch’s blockchain incorporates a proof-of-stake model, enabling validators to earn rewards for securing the network. This encourages participants to act honestly and efficiently. Meanwhile, specialized smart contracts coordinate how agents discover, negotiate, and finalize transactions. The result is a trust-minimized environment that can be extended into real-world use cases, from verifying supply chain authenticity to automating settlements in decentralized finance (DeFi).
By combining powerful on-chain logic with off-chain machine learning, Fetch enables applications that go beyond basic token swaps. Agents can, for example, learn and adapt to user behavior or market trends in real time.
With DeltaV’s privacy-preserving training, multiple organizations—say, hospitals in different regions—could collaboratively refine a machine-learning model for medical research without ever sharing patient records directly. This kind of AI synergy, powered by decentralized coordination, is a core aspect of what Fetch defines as “collective intelligence.”
Overview of Fetch’s position in the DeAI space
Decentralized AI (DeAI) protocols seek to combine the benefits of blockchain—security, transparency, and decentralized governance—with the transformative capabilities of machine learning.
While projects in this domain vary in focus and approach, many of them share a fundamental aim: to reduce the barriers to AI development and data sharing by eliminating centralized intermediaries. Initiatives like SingularityNET, for example, strive to build global marketplaces for AI services, enabling developers to monetize their algorithms, while users can discover and integrate cutting-edge AI tools in a trustless environment. Meanwhile, platforms such as Ocean Protocol concentrate on tokenizing data and facilitating data sharing in a privacy-preserving manner, which is invaluable for machine-learning applications that rely on large, diverse datasets.
Golem is another major player, originally conceived as a distributed compute resource network. It lets participants rent out spare CPU and GPU cycles, effectively democratizing access to computing power that can be directed toward AI model training or other computationally intensive tasks. Numerai, on the other hand, leverages cryptographic techniques and crowd-sourced AI modeling to manage a hedge fund, incentivizing data scientists around the world to collectively improve predictive models without revealing the underlying data. In each of these use cases, the interplay between decentralized incentives and AI-driven innovation is core to the project’s identity.
Differentiators
Fetch distinguishes itself with its agent-based paradigm, where AI-powered entities interact autonomously across multiple domains. For example, a Fetch agent could source data from Ocean Protocol, process it using Golem’s computing power, and autonomously execute smart contract transactions—all within a trust-minimized framework. Moreover, Fetch’s architecture promotes cross-protocol interoperability, enabling agents to interact with external blockchains and data marketplaces. This integration fosters a seamless, interconnected AI ecosystem rather than isolated silos.
The “agent-based” paradigm is a distinctive selling point in the DeAI space. By combining AI logic with blockchain-based smart contracts, Fetch aims to automate more sophisticated tasks than just “buying and selling” data or compute resources. This can involve dynamic pricing, real-time negotiations, and complex coordination of assets across multiple stakeholders. In scenarios like decentralized supply chain management, an agent could continuously assess shipping options, inventory levels, and real-time market data to optimize logistics with minimal human intervention. This adaptability is where Fetch’s approach may bring something novel to DeAI.
Another notable factor is how Fetch addresses interoperability. Many DeAI protocols are siloed, focusing on one aspect—like data marketplaces, computation, or financial modeling. Fetch’s architecture encourages cross-protocol collaboration, allowing its agents to interface not just with the Fetch blockchain, but with external blockchains and data sources. This could create a more fluid ecosystem where agents seamlessly tap into specialized services from other DeAI projects. If successful, this interconnectedness could accelerate the entire sector by fostering a larger network effect.
Privacy
Privacy considerations are also increasingly central in decentralized AI, as well as its obvious utility across every major crypto application.
Ocean Protocol and Numerai both use cryptographic techniques to keep proprietary data hidden while still enabling collaboration. Fetch’s adoption of tools like DeltaV for federated learning builds upon these concepts. Rather than forcing everyone to pool sensitive information into a single repository, DeltaV ensures that each participant retains ownership of their data, contributing only the learned parameters needed to improve models. This resonates with a broader movement in the AI world toward privacy-preserving computations, a key differentiator for protocols that want to meet real-world regulatory and ethical standards.
Where many DeAI projects concentrate on the technology alone, Fetch aims to tie in economic incentives more deeply through its agent model. Agents earn or spend tokens in pursuit of tasks that might require data, computation, or other resources. This token-driven economy can reward participants who offer valuable services—like curated datasets or specialized AI modules—while ensuring that inefficiencies are minimized. Over time, this economic layer could encourage the emergence of specialized agent networks or marketplaces catering to niche industries, each operating under Fetch’s overarching framework.
Governance
Equally important is governance. Some DeAI protocols use on-chain voting systems or DAO structures to decide network parameters or which AI models receive funding. Fetch’s model allows for more granular forms of agent-based governance, where each agent’s preferences and behaviors shape outcomes in a distributed fashion. This might lead to emergent “micro-governance” processes, in which communities of agents decide on best practices or co-develop new protocols without needing a single hierarchical authority.
Because the field is evolving rapidly, collaborative experimentation is essential. Fetch might integrate with or draw insights from newer DeAI entrants that tackle specialized challenges—like privacy-preserving computation, knowledge graph reasoning, or generative AI models. By weaving these functionalities into its agent-based system, Fetch can stay at the forefront of innovation, offering a platform that is both broad in scope and open to novel tool sets. This keeps pace with the constant advance of AI research, which is crucial for staying relevant in a sector prone to rapid change.
If successful, agents running on Fetch’s network would orchestrate increasingly complex tasks, tapping into diverse AI and blockchain platforms without the friction of centralized control. Fetch stands to fill a critical role in orchestrating these disparate elements into cohesive, agent-driven ecosystems. In doing so, it doesn’t merely seek to replicate what other DeAI protocols have done but rather aims to supercharge their functionalities by providing a versatile, agent-based infrastructure that can integrate seamlessly with a broader network of decentralized services and marketplaces.
Fetch’s technology and progress
One of the most notable recent developments in the Fetch ecosystem is the launch of ASI-1 Mini, which builds on Fetch’s commitment to integrating advanced AI into a new type of framework. According to Fetch’s blog posts, ASI-1 Mini stands out due to its knowledge graph integration—a feature that allows the model to tap into structured data sources for more contextually accurate responses. This is a significant advancement in conversational AI, particularly when combined with the “agentic” approach outlined by Fetch. Agents running on the Fetch network can leverage this language model for tasks like complex negotiations or context-driven recommendations, all while maintaining a secure, trust-minimized environment.
ASI-1 Mini is branded as the first Web3 LLM specifically designed for agentic AI. In traditional LLMs, information flow is often confined to a centralized system, where the model has no autonomous means of calling upon external resources. By contrast, ASI-1 Mini can interact with smart contracts, data sources, and other agents on the Fetch blockchain. This synergy allows for a more dynamic, real-time AI experience. For instance, an agent might query the model to refine contract details, validate data from a knowledge graph, and then autonomously commit a transaction if certain conditions are met. The result is an ecosystem where intelligence is fluid and decentralized.
The knowledge graph integration is particularly noteworthy. In typical AI systems, language models operate on large text corpora, but they have limited “understanding” of the relationships between entities. By incorporating a knowledge graph, ASI-1 Mini can reference structured, semantically rich data to answer queries or generate advice with greater precision. This makes it especially useful for tasks that require context, such as supply chain management or financial forecasting, where an agent might need to verify a product’s origin, price history, and performance track record before completing a transaction.
These capabilities are complemented by AgentVerse, another key initiative that has seen recent enhancements in search functionality and user engagement. AgentVerse is described in the blogs as a platform that allows users to interact with AI agents in a more intuitive manner. The updates focus on making the discovery of relevant agents, data sources, and knowledge bases more seamless. By implementing new search algorithms and user interface improvements, Fetch aims to lower the barrier to entry for both casual users and developers looking to build complex agent-based applications.
Enhanced search not only benefits end-users but also has profound implications for how agents coordinate among themselves. If each agent can efficiently find other agents based on specialized tags or skill sets, the entire network gains “collective intelligence.” Agents can form ad hoc collaborations to solve multi-step problems, drawing on each other’s specialized knowledge or access to different data streams. The recent blog updates detail how these search improvements will likely boost the overall efficiency of the Fetch ecosystem, by making agent discovery more scalable and less prone to bottlenecks.
AgentVerse updates also delve into the performance improvements aimed at handling higher volumes of queries and agent interactions. As the number of participants in the Fetch network grows, scalability becomes critical. The blog posts highlight optimizations in communication protocols, query handling, and data indexing that ensure real-time responsiveness. This aligns with Fetch’s broader mission: a scalable, decentralized platform for AI-driven agent interactions that can cater to enterprise-level use cases. Even for smaller projects or individual developers, the promise of low-latency, high-throughput systems is essential for user adoption and trust.
These performance enhancements feed directly into the capabilities of ASI-1 Mini. By allowing the LLM to interface with a faster, more robust agent network, developers can build applications where real-time data flows into the model for instantaneous analysis. Imagine an e-commerce setting where prices and stock levels update in real time, and AI-driven agents quickly process these changes to offer shoppers personalized deals. With knowledge graph integration, these deals could be validated against product attributes, vendor reliability, and consumer reviews—unifying siloed information under a single, decentralized framework.
One interesting aspect raised in the blogs is how these agent-centric improvements also foster a more “community-driven” approach to AI development. In many centralized AI platforms, users have limited visibility into how models make decisions. With AgentVerse and ASI-1 Mini, there’s potential for more transparent training processes and data usage policies. By leveraging Fetch’s tokenomics and decentralized governance mechanisms, communities can vote on which data sources to integrate into the knowledge graph or how to allocate resources for model retraining.
From a developer standpoint, the combined features of ASI-1 Mini, AgentVerse, and the broader Fetch tech stack reduce the complexities of building advanced AI applications on a blockchain. The blog posts underscore the availability of SDKs, documentation, and turnkey solutions that aim to simplify the integration of AI agents into existing workflows. This is in line with Fetch’s overarching strategy: to create a cohesive ecosystem where organizations can experiment with decentralized AI without needing to piece together multiple, often incompatible tools.
Security and privacy also benefit from these upgrades. The knowledge graph integration in ASI-1 Mini can be made permissioned, ensuring only authorized agents can modify or read sensitive data. Meanwhile, the federated learning principles behind the DeltaV product can be adapted to train or update the AI components without centralizing all user data in one place. This means that AgentVerse is not just more capable—it’s also more robust in terms of data governance and compliance with global privacy regulations.
Finally, these developments bolster Fetch’s long-term vision of building a self-sustaining digital ecosystem where intelligent agents coordinate autonomously. By introducing a specialized Web3 LLM, refining how agents discover each other, and improving overall system performance, Fetch continues to refine its multi-layered approach to decentralized AI. Each innovation highlights the synergy between blockchain, AI, and autonomous agents - continuing to solidify Fetch’s position as a versatile platform in the rapidly evolving DeAI space. Through these advancements, the project seems well-positioned to address real-world use cases that demand scalable, trustworthy, and highly intelligent systems.
Disclaimer: This research report is exactly that — a research report. It is not intended to serve as financial advice, nor should you blindly assume that any of the information is accurate without confirming through your own research. Bitcoin, cryptocurrencies, and other digital assets are incredibly risky and nothing in this report should be considered an endorsement to buy or sell any asset. Never invest more than you are willing to lose and understand the risk that you are taking. Do your own research. All information in this report is for educational purposes only and should not be the basis for any investment decisions that you make.
