Why Gen AI will transform business processes with the help of AI agentic workflows - A look into Cluster Protocol x Theoriq
Nov 4, 2024
17 min Read
When was the last time you communicated with Chat-GPT? Were you left amazed by how capable such a chatbot can be? Now visualize such specialized chat-bots working amongst each other within an ecosystem to achieve a particular objective.
The latest advancements in Generative AI (Gen AI) have provided humanity an incredible leap in the field of Artificial Intelligence; and has left many surprised by the capabilities of natural language chatbot models like Chat-GPT.
While there is no doubt in the exceptionalism of such new technologies, what appears to be next for Gen AI looks like an ever bigger leap in AI transforming our contemporary world. This new technology is AI agents. that are capable of co-working within a system, efficiently delegating and executing on some of the highly complex tasks in fields like finance, healthcare and more.
Agentic workflows have played an important part in business processes for multiple years now, where the technology has helped to efficiently delegate mundane tasks to complex tasks using the help of specialized agents that are capable of communicating with each other to deliver on a specific task.
In a comprehensive article by McKinsey and Company titled “Why agents are the next frontier of generative AI” , the leading global strategy firm describes the transformation brought by AI agents as the evolution from thought to action -
“We are beginning an evolution from knowledge-based, gen-AI-powered tools - say, chatbots that answer questions and generate content—to gen AI–enabled “agents” that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action.”
This article is an in-depth exploration of AI agents and how the technology promises to transform modern business workflows by enhancing efficiency, accuracy and quality of output when compared to human based workflows. The article dives deep into the basics of AI agents, how they work and the current implementation of the technology across various industries. Lastly, we explore some shortcomings of the current workflows created using AI agents and offer a web3 based decentralized alternative that offers a promising trajectory for the growth and wide-scale adoption of AI agents. To begin with,
What is an AI Agent?
An artificial intelligence (AI) agent is a system or program designed to autonomously carry out tasks for users or other systems by creating its own workflows and utilizing a wide-range of tools. These agents offer a variety of functions, extending beyond natural language processing to include decision-making, problem-solving, and interaction with external environments.
AI agents can be used in many applications across enterprise settings, such as software design, IT automation, code generation, and conversational assistants. They leverage advanced natural language processing techniques from large language models (LLMs) to understand and respond to user inputs step-by-step, and they determine when to use external tools to assist in completing tasks. Simply put, a set of AI agents working together towards a shared objective (code debugging for example) becomes an agentic workflow that is capable of highly-advanced task execution by leveraging specialized skills present in each agent and robust communication between all of the agents to develop, test, iterate and produce results.
It is pretty easy to visualize how such “virtual teammates” can significantly enhance outcomes as well as introduce a level of depth and expertise that may not be possible with human counterparts.
How AI Agents Work -
The fundamental difference between simple chat based models and AI agents is the ability of agents to work cooperatively and utilize the various tools to execute on certain tasks. As described below, AI agents have abilities to reason, learn and reflect as necessary to achieve the co-identified objective.
At the heart of AI agents are large language models (LLMs), which is why they are often called LLM agents. Traditional LLMs, like IBM Granite models, generate responses based on their training data, which limits their knowledge and reasoning capabilities. In contrast, agentic technology enhances these models by using backend tool calling to access up-to-date information, optimize workflows, and autonomously create subtasks to achieve complex goals.
1. Adaptation and Personalization
AI agents learn to adapt to user expectations over time. They can store past interactions in memory and plan future actions, leading to a personalized experience and more comprehensive responses. The main differentiating factor of AI agents is the ability to perform tool calling without human intervention, which in turn expands the real-world applications of these AI systems without the need of continuous external input.
2. Three Stages of Goal Achievement
A. Goal Initialization and Planning: Although AI agents make autonomous decisions, they need human-defined goals and environments. Three key influences shape their behavior:
- The developers who design and train the AI system.
- The team that deploys the agent and provides user access.
- The user, who sets specific goals and specifies available tools.
With the user's goals and available tools, the AI agent performs task decomposition to enhance performance, breaking down complex goals into specific tasks and subtasks. For simpler tasks, planning may not be necessary; the agent can iteratively improve its responses without pre-planning.
B. Reasoning Using Available Tools: AI agents act based on perceived information. Often, they lack the complete knowledge needed to tackle all subtasks of a complex goal, so they utilize external tools, such as data sets, web searches, APIs, or even other agents. As they retrieve missing information, the agents update their knowledge base, reassessing their action plans and self-correcting throughout the process.
For example, if a user asks an AI agent to predict the best week for a surfing trip in Greece, the LLM may not specialize in weather patterns. The agent gathers historical weather data and then consults an external agent specializing in surfing conditions to learn that optimal surfing requires high tides and sunny weather with minimal rain. Using this combined information, the agent can identify the best week for the user’s trip. This point primarily emphasizes that AI agents do not have an innate ability to reason as a human would but they utilize the system of specialized agents to facilitate a simulation of reasoning. Put in other words, an AI agent cannot innately perform the task as portrayed in the image below but rather uses a system of specialized agents to make it happen.
C. Learning and Reflection: AI agents improve their responses through feedback mechanisms, which can involve interactions with other AI agents or human oversight (human-in-the-loop, or HITL). Using the surfing example, after forming a response, the agent stores the learned information and user feedback to refine its performance and adapt to user preferences for future tasks.
Feedback from other agents can also be valuable, reducing the time users spend directing the AI. Users can provide ongoing feedback during the agent's actions and reasoning processes, helping align results with intended goals.
These feedback mechanisms enhance the reasoning and accuracy of AI agents, a process known as iterative refinement. To avoid repeating past mistakes, agents can store data about previous solutions in a knowledge base.
Benefits of using AI based agentic workflows to businesses -
AI agents can unlock significant value for businesses by automating a range of complex use cases that mainly involve highly variable inputs and outputs. Historically, these use cases have been challenging to address efficiently in terms of cost and time as it relates to training specialized LLM models. For instance, very simple tasks like planning a business event can involve numerous variabilities like choosing between a variety of locations, event hosts, menu items and itineraries which are difficult to implement within linear LLM models. AI agents on the other hand promise to enhance such capabilities by leveraging reasoning and learning abilities as well as a large toolset to perform complex tasks effectively. Below are 3 reasons why AI agents hold the potential to transform business processes -
1. Managing Multiplicity: Many business processes follow linear workflows with clear steps leading to specific outcomes, making them easier to automate using rule-based systems. However, these systems can be brittle, failing when faced with unexpected situations not anticipated by the original rules. In contrast, generative AI-enabled agents, based on foundation models, can adapt to a wide range of unpredictable scenarios. They can handle nuanced judgment and specialized tasks in real time, making them suitable for complex workflows with variable outcomes.
2. Natural Language Direction: Automating a use case typically requires breaking it down into rules and steps, which are then codified into computer code—a labor-intensive process that demands technical expertise. AI agents allow users to provide instructions in natural language, simplifying the encoding of complex workflows. This approach enables even non-technical employees to contribute, facilitating the integration of subject matter expertise and promoting collaboration between technical and non-technical teams.
3. Integration with Existing Software Tools: AI agents can interact with a wide range of software applications and platforms, enhancing their functionality. They can perform tasks like data analysis, web searches, collecting human feedback, and leveraging other foundation models. This capability allows agents to operate within a broader digital ecosystem, making them versatile tools in business operations. Without these advanced capabilities, integrating systems would require extensive manual effort and complex processes, such as using extract, transform, and load tools to collate outputs from various software.
A hypothetical use case of AI agents - Loan underwriting -
McKinsey in the same article provides a hypothetical example of where AI agents could be implemented effectively in the near future -
Financial institutions create credit-risk memos to evaluate the risks associated with extending credit or loans to borrowers. This process involves gathering, analyzing, and reviewing various types of information about the borrower, the loan type, and other relevant factors. Due to the variety of credit-risk scenarios and analyses required, the process can be time-consuming and highly collaborative. It typically involves a relationship manager working closely with the borrower, various stakeholders, and credit analysts to conduct specialized analyses, which are then submitted to a credit manager for further review and expertise.
Potential Agent-Based Solution -
An agentic system, composed of multiple specialized agents, could be designed to manage a range of credit-risk scenarios efficiently. The process would begin with a human user initiating the workflow using natural language to outline a high-level plan with specific rules, standards, and conditions. The team of agents would then break this work down into executable subtasks.
1. Relationship Manager Agent: This agent would manage communications between the borrower and the financial institution, facilitating information exchange and updates.
2. Executor Agent: Responsible for compiling the necessary documents, this agent would gather all relevant paperwork and prepare it for analysis.
3. Financial Analyst Agent: This agent would examine financial data, such as debt from cash flow statements, and calculate essential financial ratios.
4. Critic Agent: After the analysis, this agent would review the findings to identify discrepancies and errors, providing feedback to ensure accuracy.
This iterative process of breakdown, analysis, refinement, and review would continue until the final credit memo is completed.
Issues with current AI agents -
AI agents offer an incredible level of execution capacity especially when handling complex and highly variable tasks. But there some shortcomings with current agentic systems that emerging technologies offer potent alternatives to -
1. Multi-agent dependencies
Certain complex tasks require the knowledge of multiple AI agents. When implementing these multi-agent frameworks, there is a risk of malfunction.
Multi-agent systems built on the same foundation models may experience shared pitfalls. Such weaknesses could cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks.13 This highlights the importance of data governance in building foundation models and thorough training and testing processes.
2. Infinite feedback loops
The convenience of the hands-off reasoning for human users using AI agents also comes with its risks. Agents that are unable to create a comprehensive plan or reflect on their findings, may find themselves repeatedly calling the same tools, invoking infinite feedback loops. To avoid these redundancies, some level of real-time human monitoring may be used.
3. Computational complexity
Building AI agents from scratch is both time-consuming and can also be very computationally expensive. The resources required for training a high-performance agent can be extensive. Additionally, depending on the complexity of the task, agents can take several days to complete tasks.
The Future of AI Agents - Decentralized?
A look into the Cluster Protocol x Theoriq partnership and what it entails -
So, what is Theoriq?
Theoriq is a blockchain based AI agent protocol that believes in enhancing the potential of AI agents by opening up the technology for use in a decentralized and collective manner. Let us understand Theoriq and Cluster Protocol on a conceptual level below to really grasp the potential that these collaboration holds.
The Theoriq lite paper contrasts their foundational idea from tech giants like Open AI, Google and Meta who are rushing to lead the AI agent scene by emphasizing one key differentiating factor - decentralization and collectivity of AI agents of Theoriq. As the lite paper states,
“At Theoriq, we believe that the true potential of AI lies not in individual agents, but in collectives of collaborating agents—much like human civilization has thrived through specialization and collaboration. By harnessing collective abilities, AI Agents working together can achieve greater efficiency, creativity, and effectiveness in tackling complex problems.” Ron Bodkin, CEO and Co-Founder of Theoriq
The passage below outlines the framework of Theoriq on a conceptual level with a breakdown of its key concepts and components:
Overview of Theoriq
Theoriq aims to create a decentralized ecosystem for AI agents, enabling them to operate collaboratively while ensuring transparency, security, and accountability through the use of blockchain technology. A synergy of AI and blockchain allows Theoriq to provide decentralization and collaborative tools for individuals to collectively develop and deploy AI agents. The 3 core pillars of the ecosystem are outlined below -
Core Pillars
1. Interoperable Agentic Primitives:
- **Definition:** These are foundational building blocks that allow developers to create AI agents capable of interacting across various platforms and models.
- **Importance:** This flexibility ensures that agents can communicate and work together effectively, regardless of the underlying technology or framework.
2. Composable Collective Formation:
- **Definition:** This aspect focuses on how agents can dynamically come together to form collectives, enhancing their capabilities.
- **Mechanisms:** It includes systems for discovering, evaluating, and optimizing these collectives based on performance metrics and user feedback.
- **Reputation Systems:** Trust and reliability among agents can be established through reputation mechanisms, which evaluate agent contributions and performance.
3. Decentralized Innovation Ecosystem:
- **Marketplace Concept:** This pillar encourages a collaborative environment where developers, users, and agents can interact and innovate.
- **Token Economics:** Economic incentives are provided to motivate continuous improvement and the expansion of network capabilities.
Optimization for wide-spread growth -
1. No-Code Builder Interfaces
Theoriq is designed to be user-friendly, enabling widespread adoption even among those without extensive coding knowledge. This feature enhances accessibility and encourages participation from a diverse range of users.
2. Permissionless
The architecture is designed to be open and adaptable, allowing anyone to contribute new components or concepts. This promotes organic growth and innovation while reducing reliance on centralized control. This is especially important considering the importance and power that AI agents hold.
Protocol Components
The passage details specific components of Theoriq's architecture, including:
1. Collectives:
- **Definition:** Groups of agents organized to tackle complex tasks collaboratively.
- **Function:** They implement collaborative problem-solving strategies.
2. Aggregators:
- **Role:** Specialized agents that manage workflows within collectives, executing planning functions and budget management.
3. Evaluators:
- **Function:** Agents responsible for assessing the quality of contributions made by others, generating "Proofs of Contribution" to inform reputations.
4. Optimizers:
- **Purpose:** Focus on improving membership within collectives and enhancing overall collective performance through metrics and artifacts, such as leaderboards.
5. Profiles:
- **Description:** Secure, decentralized stores of information that keep track of agent metadata, execution logs, and user-specific data. They ensure accountability by recording agent actions and experiences.
Synergies in Collaboration between Cluster Protocol and Theoriq -
1. Enhanced Interoperability:
By integrating Cluster Protocol's resource-sharing capabilities with Theoriq's interoperable agentic primitives, agents could easily collaborate across diverse frameworks. This could lead to more robust generative models that utilize shared datasets and computational resources.
2. Dynamic Collective Formation:
Theoriq’s composable collective formation can be enhanced by Cluster Protocol’s infrastructure, allowing agents to dynamically form and reconfigure based on real-time needs and objectives. This could lead to faster and more efficient generative processes.
3. Decentralized Marketplace for AI Models:
Together, they could create a decentralized marketplace for AI models, where developers can trade and evaluate generative AI agents based on their performance. This could foster a community-driven approach to model improvement and innovation.
Overall, Theoriq’s protocol is designed to foster a dynamic, adaptable environment for AI agents to collaborate effectively. The focus on decentralized governance, modularity, and extensibility aims to create a robust framework that can evolve alongside advancements in AI technology. This makes it a promising approach for addressing complex challenges in multi-agent systems while prioritizing user engagement and community-driven innovation.
Conclusion - What is the future of the AI Agents market?
As we analyze the future potential of the AI agents market, certain components are expected to be particularly in demand due to their ability to enhance functionality, usability, and overall effectiveness. In turn, these characteristics will best position the technology to penetrate society and provide effective change. Here’s a deeper look at some of these components of AI Agents that are likely to be in high-demand as the demand for AI Agent grows:
1. Natural Language Processing (NLP)
- **Overview:** NLP enables AI agents to understand, interpret, and respond to human language effectively. This capability is crucial for applications like chatbots, virtual assistants, and customer service automation.
- **Demand Drivers:** With the rise of conversational AI, NLP technology is essential for creating more intuitive and human-like interactions. The market for NLP is projected to grow at a CAGR of 26%, driven by increasing demand for voice-activated devices and improved customer engagement solutions.
2. Machine Learning and Deep Learning Algorithms
- **Overview:** These algorithms allow AI agents to learn from data and improve their performance over time. This adaptability is critical for applications in predictive analytics, personalization, and decision-making.
- **Demand Drivers:** As more businesses seek to leverage big data, the need for robust machine learning frameworks will rise. The machine learning market is expected to reach $117 billion by 2027, with a significant portion allocated to developing AI agents.
3. Integration with Internet of Things (IoT)
- **Overview:** AI agents that can interact with IoT devices enhance automation capabilities in smart homes, industries, and cities. This integration facilitates real-time data collection and responsive actions.
- **Demand Drivers:** The increasing adoption of IoT devices is driving demand for AI agents capable of processing and analyzing data from these devices. The IoT market is projected to reach $1.1 trillion by 2026, with AI agents playing a crucial role in data management and automation.
4. Decentralized Governance Mechanisms
- **Overview:** As people and organizations prioritize transparency and accountability, AI agents that utilize decentralized governance protocols like blockchain will become increasingly valuable. This is where the true essence of protocols like Theoriq, Cluster Protocol and their subsequent collaboration takes importance. Due to the extensive factors discussed above, this collaboration can hold a unique edge in this growing market.
- **Demand Drivers:** The need for secure and transparent data handling practices, especially in sectors like finance and healthcare, is driving interest in decentralized AI solutions. The blockchain market is expected to grow to $163 billion by 2027, creating opportunities for AI agents that leverage this technology.
Financial Growth Potential of the AI Agent Market
The financial outlook for the AI agent market reflects substantial growth potential across various dimensions. Here’s a detailed analysis of that potential:
1. Projected Market Growth
- **Market Size:** The AI agent market, valued at approximately $7 billion in 2023, is projected to grow to $30 billion by 2028, reflecting a CAGR of 35%.
- **Revenue Streams:** Key revenue streams will include:
Licensing and Subscription Fees: Ongoing revenue from software as a service (SaaS) models.
Consulting and Integration Services: Increasing demand for tailored solutions and support.
Data Monetization: Companies may monetize insights gained through AI agent interactions.
2. Investment Trends
- **Venture Capital:** In 2022, global investments in AI technologies reached approximately $50 billion. This trend is expected to continue as investors seek opportunities in emerging AI agent startups.
- **R&D Spending:** Companies in the AI sector are projected to allocate 15-20% of their revenues to research and development, fostering innovation and enhancing competitive positioning.
3. Cost Savings and ROI
- **Operational Cost Reductions:** AI agents can reduce operational costs by 20-30% through automation and efficiency improvements. This cost-saving potential is a strong driver for adoption across sectors.
- **Return on Investment (ROI):** Organizations adopting AI agents can anticipate a ROI of up to 300% over three years, making a compelling case for investment in AI technologies.
4. Sector-Specific Opportunities
- **Healthcare:** The use of AI agents in telehealth, diagnostics, and patient engagement is projected to grow significantly, with the healthcare AI market reaching $60 billion by 2024.
- **Finance:** AI agents are increasingly used for fraud detection, customer service, and investment analysis. The financial AI market is expected to exceed $22 billion by 2025.
- **Retail and E-commerce:** Personalized shopping experiences driven by AI agents will see rapid growth, with projections indicating a market size of $30 billion by 2026.
5. Geographic Expansion
- **North America:** Currently leading in AI adoption, this region is expected to maintain its dominance, accounting for over 40% of the global AI agent market.
- **Asia-Pacific:** Rapid growth is anticipated in this region due to increased investments in technology and a burgeoning startup ecosystem. The market is projected to grow at a CAGR of 45%, driven by demand in countries like China and India.
Have a look at a relevant graph below -
The AI agent market is poised for remarkable growth, driven by advancements in technology, increasing demand for automation, and the need for personalized and efficient services. With a projected market size of $30 billion by 2028, companies that invest in key components such as NLP, machine learning, and decentralized governance will be well-positioned to capitalize on this opportunity. As financial projections indicate significant ROI and cost savings, the strategic integration of AI agents by protocols like Theoriq and Cluster Protocol will become critical for businesses aiming to thrive in the digital age.
About Cluster Protocol
Cluster Protocol is a decentralized infrastructure for AI that enables anyone to build, train, deploy and monetize AI models within few clicks. Our mission is to democratize AI by making it accessible, affordable, and user-friendly for developers, businesses, and individuals alike. We are dedicated to enhancing AI model training and execution across distributed networks. It employs advanced techniques such as fully homomorphic encryption and federated learning to safeguard data privacy and promote secure data localization.
Cluster Protocol also supports decentralized datasets and collaborative model training environments, which reduce the barriers to AI development and democratize access to computational resources. We believe in the power of templatization to streamline AI development.
Cluster Protocol offers a wide range of pre-built AI templates, allowing users to quickly create and customize AI solutions for their specific needs. Our intuitive infrastructure empowers users to create AI-powered applications without requiring deep technical expertise.
Cluster Protocol provides the necessary infrastructure for creating intelligent agentic workflows that can autonomously perform actions based on predefined rules and real-time data. Additionally, individuals can leverage our platform to automate their daily tasks, saving time and effort.
🌐 Cluster Protocol’s Official Links: