The emergence of large language models (LLMs) like ChatGPT has reshaped AI capabilities. However, as AI use expands, limitations of these generalist models are becoming clear, such as generating false information, lacking context, and struggling with multi-step reasoning. These issues prevent LLMs from consistently delivering high-quality and reliable results.
To address this, AI development should start to mirror human intelligence by focusing on specialization. LLM development should be realigned to more specialized roles where they function as AI Agents, a form of software that leverages the capabilities of AI to perform specific functions, in many domains.
Recently, we published an article titled “Wen Agents?” which explored the potential growth of AI agents as an emerging technological field. It provided key insights into AI agents, including how they will be developed and the growing need for these agents to collaborate in multi-agent systems,which Theoriq have labeled as AI Collectives. The article also discussed how leveraging Web3 to develop these systems introduces principles like decentralization, community governance, and open innovation, creating more equitable AI development.
For a refresher, click here to read the full article.
To organize, assess, and grow Agent Collectives in Web3 and beyond, we will inevitably face the above limitations and further challenges that hinder their comprehensive development. Some of these primary challenges include:
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Composability and interoperability: Current AI models struggle to communicate, share data, and collaborate, creating fractured and heterogeneous systems.
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Evaluation and optimization: There are limited ways to measure, assess and choose the best performing AI Agents for specific tasks.
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Safety and security: Malicious agents and unintended consequences could erode trust in AI Agents.
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Decentralization and inclusivity: Convincing the majority that the concentration of AI development to centralized entities is bad for AI development and accessibility.
Each topic presents unique challenges, and to address these, Theoriq has developed a decentralized protocol that is modular, composable, and community permissioned, enabling the creation, discovery and interaction of AI Agents. This approach tackles each challenge in a unified way. In this blog, we will dive into each of these challenges and explain how the Theoriq protocol is practically addressing them.
Composability and Interoperability – Standardizing AI Agent Primitives
Although the LLM field is relatively young, a wide range of tools and frameworks for AI agent creators already exists, which has resulted in a diverse and heterogeneous AI landscape. This diversity encourages innovation but also presents significant challenges when aiming to integrate these agents into a cohesive system. Differences in data formats, communication protocols, and operational logic create barriers for effective interoperability, highlighting the need for a common layer that supports collaboration and composability among AI agents.
Another major challenge is the lack of standardized protocols and interfaces. Without them, developers will continue creating custom solutions for each integration, increasing complexity and reducing efficiency. Having standardized protocols would simplify integration, allowing agents to communicate, share data, transact and coordinate actions much easier. Without these standards, the development of multi-agent systems will remain fragmented and labor-intensive, hindering seamless collaboration.
Theoriq addresses these issues by offering standardized agentic primitives, modular design, and interoperable frameworks, enabling agents to integrate and collaborate effectively. Cross-agent communication and collaboration is facilitated by the integration of innovative concepts like “Behaviors”, formal interfaces specifying Agent capabilities, and “Permissionless Extensibility”, both which ensure Agents can work and evolve together more efficiently.
Evaluation and Optimization – Robust Mechanisms and Optimization
Even when a diverse system is well-integrated, the complexity of interactions can make it challenging to accurately evaluate and optimize the performance of individual agents and collectives. Evaluating agents in dynamic, real-world environments, where tasks and conditions are constantly shifting, adds another layer of complexity.
How should users measure which agents are reliable and whether they complete the tasks you require efficiently? What methods and metrics should be developed that accurately assess this request?
Agents would firstly have to be assessed on narrowly defined metrics such as task completion, response time, resource utilization, and accuracy, and more qualitative metrics such as objectiveness, fairness, or even simple preference. The next step to accurate evaluation is in creating robust mechanisms that allow users to evaluate and select the best agents from an ever-expanding pool of choices. The Theoriq protocol has developed and integrated a comprehensive toolkit that uses “Evaluators” to categorize AI Agents based on a combination of preference-based evaluation, staking mechanisms and human feedback.
The next hurdle, once you have found the best performing AI Agents for your specific tasks, how do you combine them into an optimized collective?
This is where Theoriq’s protocol implements a selection of optimization processes that assess, organize and improve the composition of agent collectives. Instead of just selecting the best-performing agents individually, users will be able to measure how well agents work together as a team by estimating the performance of agent combinations, significantly speeding up the process of finding high-performing Collective combinations.
Safety and Security – Aligning Agent Behavior with Intended Purpose
Even with a well-optimized system, complex agent interactions can lead to unexpected risks. It's essential to prevent and reduce the impact of malicious agents to keep the system safe.
Theoriq focuses on safety and security by using community-driven standards, staking, and slashing mechanisms to prevent malicious behavior. There are also banning measures for breaking these rules. Evaluators play a role in governance by providing data-driven, immutable, and verifiable checks to ensure that an Agent's behavior aligns with its intended purpose. These steps are crucial for maintaining transparency, security, and accountability within the ecosystem, creating a secure and trustworthy environment for AI Agent interactions and collaborations.
Decentralization – Equitably Distributing Development and Ownership
We believe that collaborative AI Agents will profoundly impact future economies, and to ensure this impact is widespread and beneficial, their development must incorporate the features of decentralization enabled by Web3. Currently, the development of AI systems are occuring behind gated corporate walls and developed in black box environments. This is concentrating the accessibility to monopolized entities and their developers, limiting competition and suppressing innovation.
The challenge then lies in decentralizing the development, control, and usage of AI systems. For these systems to reach their full potential, they must integrate the benefits of decentralization—such as distributed ownership, governance, transparency, and accessibility—into their design and implementation.
Theoriq are already instilling these benefits in its protocol, with Web3-enabled security for robust AI agent protection and immutable records for auditability and effective reputation systems. Its decentralized marketplace promotes inclusivity, offering fair participation and monetization for developers. Community governance promotes transparency, allowing stakeholders to influence the protocol's development, and in the future, a potential financial layer could align incentives by supporting staking, rewards, and governance, encouraging collaboration and innovation in the ecosystem.
This approach results in more practical, equitable, robust, and secure AI systems that cater to diverse use cases and provide real utility to their users. The Theoriq protocol is instilling this with their decentralized ecosystem where AI creators and users can build and deploy interoperable agents that collectively solve complex challenges.
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The future of AI lies in specialized agents that can collaborate seamlessly, optimize their performance, and operate securely within a decentralized framework. Theoriq is paving the way for this future by addressing the key challenges of interoperability, optimization, security, and decentralization. By leveraging Web3 principles and its innovative protocol, Theoriq is creating a robust and equitable ecosystem where AI agents can evolve, delivering real utility and value. This decentralized approach will not only drive the next wave of AI innovation but also ensure that its benefits are accessible to all.
All of the above concepts are dissected and presented in detail in the Theoriq Litepaper which you can find here.
About Theoriq
Theoriq is the first decentralized protocol for governing and building multi-agent systems by integrating AI with blockchain technology. The platform is centered around an agnostic modular base layer that powers an ecosystem of dynamic AI Agent collectives that are interoperable, composable and decentralized.
Theoriq has raised over $10.4M from leading investors such as Hack VC, Foresight Ventures, Inception Capital, HTX Ventures and more, and have active partnerships with leading web3 and web2 projects including Google Cloud and NVIDIA.
With Theoriq, you're not just part of a network; you're part of a movement that's empowering communities, developers, researchers, and AI enthusiasts to reshape the future of intelligent autonomous systems.