Towards the Agentic Web

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In the digital world, the way we interact with the web is changing dramatically. Over the past decades, we’ve gone from simple, static websites (Web1) to interactive and social platforms (Web2), and now to decentralized systems (Web3).

The evolution of the internet can be understood through distinct phases, each marked by key technological advancements and shifts in user interaction. From the static Web1 to the interactive Web2 and the decentralized Web3, every phase has brought unique functionalities and challenges. The following table provides a comprehensive comparison of these phases, highlighting the predicted characteristics of the Agentic Web, which builds upon and transcends its predecessors by integrating AI and decentralized systems to deliver intent-driven and autonomous solutions.

Feature	Web1 (Static Web)	Web2 (Social/Interactive Web)	Web3 (Decentralized Web)	Agentic Web
Timeframe	Early 1990s – Early 2000s	Early 2000s – Present	Emerging (2015 – Future)	Predicted (2030s – Future)
Key Characteristic	Read-Only	Read-Write	Read-Write-Own	Read-Write-Own-Delegate
Main Functionality	Information sharing	User interaction, sharing	Decentralized ownership	Intent-based fulfillment
User Role	Passive consumer	Active participant	Owner and co-creator	Delegator and collaborator
Content Creation	Centralized (companies)	User-generated (blogs, social media)	User-owned (enabled by blockchain)	AI/agent-driven (automated by users)
Infrastructure	Static HTML pages	Dynamic APIs and cloud computing	Decentralized protocols (blockchain)	Autonomous agentic systems
Monetization Model	Basic ads	Attention Economy	Token Economy (crypto, NFTs, DAOs)	Outcome Economy (microtransactions)
Examples	Static websites (Geocities)	Facebook, YouTube	Decentralized apps (dApps, DeFi)	Hyper-personalized apps and AI agents
Interactivity	Limited (hyperlinks)	High (comments, likes, real-time)	Autonomous agents (smart contracts)	Seamless and autonomous interactions
Technology Foundation	HTML, HTTP	APIs, JavaScript	Blockchain, smart contracts	Blockchain + AI integration
Ownership	Companies and hosts	Platforms	Users (via wallets)	Shared between users and agents
Decentralization	None	Minimal	High	Fully autonomous
Trust Model	Central servers	Trusted platforms	Trustless (blockchain)	Trustless and adaptive
Focus	Broadcasting information	Engagement	Ownership	Intention fulfillment
AI Integration	None	Basic recommendations	Moderate (optimization)	High (autonomous agents)
Economic System	Ad-driven	Platform monetization	Token-based economy	Outcome-based economy
Governance	Centralized	Corporate-led	Decentralized (DAOs)	Distributed among agents
User Experience	Basic and static	Highly interactive	Customizable	Effortless
Access Model	Limited	Widespread	Peer-to-peer	Global access
Computational Demand	Low	Moderate	High	Extreme
Device Interaction	Desktop-based	Mobile-first	Cross-device	Ambient computing
Ecosystem Interactions	Individual websites	Platform ecosystems	Interoperable protocols	Agent-driven networks
Data Sovereignty	Controlled by hosts	Owned by platforms	Controlled by users	Fully user-sovereign
Data Verification	Not applicable	Minimal	Verified through blockchain	Guaranteed cryptographically
Data Interoperability	Nonexistent	Basic	Decentralized models	Cross-system collaboration
Context Sensitivity	None	Limited	Dynamic	Real-time context adaptation
Identity Management	None	Platform-dependent	Self-sovereign identities	Decentralized identity
Purpose	Informational	Engagement-driven	Ownership-driven	Outcome-optimized
Adoption Challenges	Minimal	Platform lock-in	Onboarding complexity	Interoperability challenges
Role of Tokens	Nonexistent	Rare	Essential	Core

This comparison demonstrates how each phase of the web has progressively empowered users, from passively consuming information in Web1 to actively participating and owning content in Web2 and Web3. The Agentic Web takes this progression further by introducing intelligent agents that collaborate with users to fulfill their intentions autonomously. By building on the strengths of previous phases while addressing their limitations, the Agentic Web aims to create a seamless, user-centric digital ecosystem.

The concept of intelligent agents has long been explored in the field of artificial intelligence. Stuart Russell and Peter Norvig, in their seminal work Artificial Intelligence: A Modern Approach, defined AI as the study and design of rational agents capable of perceiving their environment and acting upon it. The Agentic Web builds upon this legacy, introducing autonomous AI agents that not only respond to user prompts but can also act independently to achieve defined goals with minimal human oversight.

Agentic Web is a new phase of the internet where AI-powered agents handle complex tasks on your behalf. Imagine telling an agent your goal, like planning a vacation or managing bills, and having it figure out the best options, negotiate prices, and complete the process—all automatically. This can be made possible by combining AI, blockchain, and other technologies to create systems that work smarter and more independently.

• Less Effort, More Efficiency: Agents save time by doing repetitive or difficult tasks for you.
• User Control: These systems prioritize your intentions, ensuring your needs are met effectively.
• Trust Built-In: Blockchain ensures security and transparency in all transactions.
• Accessible Tech: Advanced tools are made simple, so anyone can use them without technical expertise.

For example, instead of browsing multiple websites to compare flights, an agent could automatically book the most convenient and affordable options for you. This shift isn’t just about convenience; it’s about creating a fairer, more user-focused internet.

Automation, AI Workflow, or AI Agent?

Automations: Reliable for routine tasks, like sending an email or Slack message when someone fills out a form.

AI Workflows: Smarter, using tools like ChatGPT to categorize and route 100+ daily inquiries efficiently.

AI Agents: Adaptive problem-solvers, like bots learning from interactions or AIs optimizing supply chains in real time.

The IT department of every company is going to be the HR department of AI agents in the future
– Jensen Huang (CEO, Nvidia)

The Shift Toward Agents

Unlike traditional systems where humans must manually initiate and monitor actions, these agents are capable of interpreting goals, making decisions, and taking actions independently.

This new phase is built on the convergence of AI and distributed ledger technology (DLT), which ensures transparency, security, and trust in agent-driven activities. These agents are not just tools but collaborators that align with user intentions, offering personalized solutions with minimal human intervention.

In the traditional web:

• Businesses push products to consumers.
• Consumers have to do the work of finding, evaluating, and purchasing.

In the agentic web:

• Users express their needs, and agents fulfill them.
• Transactions, research, and decisions become seamless and automated.

Web2 centralized user data and monetized attention, creating an “attention economy” dominated by a few large platforms. Web3 aimed to decentralize control using blockchain technology but often fell short due to its complexity and limited user-friendliness.

The Agentic Web resolves these issues by moving beyond the attention economy to an “intention economy.” Here, systems focus on fulfilling user goals rather than competing for their attention. This shift eliminates much of the friction and inefficiency that has defined earlier phases of the web.

One of the greatest advantages of the Agentic Web is its ability to simplify the complexities of digital life. In Web3, users needed technical knowledge to navigate decentralized systems effectively. With the integration of AI, these challenges are abstracted, making advanced technologies accessible to everyone. Agents bridge the gap between complex systems and everyday users, enabling a seamless experience without requiring deep technical expertise.

For example, instead of operating multiple applications to complete a task, a user can rely on their agent to understand their needs and execute the necessary steps. This not only saves time but also ensures that tasks are completed efficiently and accurately.

The Agentic Web is fundamentally about empowering users. By delegating routine or specialized tasks to AI agents, users gain more control over their time and decisions. These agents act as personal representatives, ensuring that the user’s interests are prioritized in every interaction.

Additionally, the combination of AI and DLT enhances privacy and security. While AI handles tasks intelligently, DLT ensures that actions are verifiable and transparent. This creates a system where trust is embedded into the technology itself, reducing the need for reliance on centralized intermediaries.

Despite its promise, the transition to the Agentic Web is not without challenges. Building trust in autonomous agents is a critical hurdle, as users may hesitate to delegate important tasks without clear accountability mechanisms. Technical integration of AI and DLT at scale also requires significant innovation and infrastructure development.

Moreover, the shift from platform-centric models will disrupt existing business practices. Companies that thrived in the attention economy will need to adapt to a system where user intentions, rather than engagement metrics, drive value.

The Agentic Web is not just about making life easier; it is about opening up new possibilities. By focusing on user intentions and leveraging intelligent agents, this phase of the internet promises to create more equitable and efficient systems.

This phase also represents a cultural shift. It challenges the dominance of large platforms, placing power back into the hands of individuals and communities. By prioritizing transparency, security, and user empowerment, the Agentic Web aligns technological innovation with societal values. Generative AI serves as a cornerstone of the Agentic Web, enabling AI agents to interpret user intentions, create personalized solutions, and adapt dynamically to real-time needs, seamlessly integrating into the ecosystem to enhance user experiences and fulfill goals with unprecedented precision.

While Generative AI democratizes content creation, its integration with autonomous systems marks the next leap, enabling agents to interpret and fulfill user intentions seamlessly.

Leveraging insights from Anthropic’s research and practical applications, here’s how developers and organizations can align with the principles of creating dynamic, scalable, and reliable agents in the context of the Agentic Web:

Building Blocks for the Agentic Web

1. Augmented LLMs as Foundations
   The core of agentic systems lies in augmented large language models (LLMs) equipped with tools like retrieval, memory, and real-time interaction. These capabilities form the foundational layer for agents, enabling them to operate seamlessly within complex workflows.

1. Workflows and Task Efficiency
   Anthropic’s categorized workflows—such as prompt chaining, routing, and orchestrator-worker models—offer structured paths to manage tasks effectively. Each workflow is tailored to specific challenges, from decomposing tasks into subtasks to dynamically directing resources for open-ended problems. These approaches ensure optimized performance, which is crucial in scaling agentic systems.

There are many frameworks that make agentic systems easier to implement, including:

• LangGraph from LangChain;
• Amazon Bedrock’s AI Agent framework;
• Rivet, a drag and drop GUI LLM workflow builder; and
• Vellum, another GUI tool for building and testing complex workflows.

Scaling Autonomy with AI Agents

1. Autonomous Problem Solvers
   Agents capable of planning and executing tasks independently mark a shift from predefined workflows to dynamic, intent-driven solutions. For example:
   • Customer Support: Enhancing traditional chatbots with tool integration for real-time data pulls, ticket updates, and refunds.
   • Coding Agents: Solving complex software tasks autonomously, iterating based on test results to refine outputs.
     
2. Agent-Driven Decision Making
   Using evaluative-optimizer loops, agents iteratively refine their actions based on feedback. This approach mimics human decision-making, ensuring higher accuracy in tasks like nuanced translations or multi-step problem solving.

Effective Agent Deployment

1. Simplify First, Add Complexity Only When Needed
   As emphasized by Anthropic, start with minimal viable solutions, such as single LLM calls with retrieval. Introduce multi-step or autonomous systems only when proven to add measurable value.
   
2. Tailor and Test Tools for Robustness
   Effective agents rely on well-documented, user-friendly tools. Developers should:
   • Optimize tool definitions for clarity and edge case handling.
   • Poka-yoke (error-proof) their tools to reduce potential misuse.
     
3. Transparent Agentic Interfaces
   Crafting intuitive agent-computer interfaces (ACI) is as critical as human-computer interfaces (HCI). Example-rich documentation and clear parameter names enhance usability and reliability.

Trust and Usability

1. Transparent Decision Processes
   Agents should articulate their reasoning and provide checkpoints for human oversight, ensuring trust and reducing unintended consequences.
   
2. Effective Governance Frameworks
   Ethical and safety considerations must guide the deployment of autonomous agents. Anthropic’s emphasis on defining agent responsibilities, monitoring actions, and maintaining user control aligns with the Agentic Web’s principles of transparency and user empowerment.

The Role of Generative AI

Generative AI has revolutionized content creation, making it easier than ever to produce high-quality blogs, images, videos, and more. Tools like ChatGPT and DALL·E allow anyone to generate professional-grade content with minimal effort.

While generative AI has democratized content creation, it has also led to content abundance. There’s now more content available than anyone could ever consume. This abundance shifts the focus from creating content to understanding intentions.

What does this mean in practice? It means businesses and platforms no longer need to produce endless streams of content to compete for attention. Instead, they can use generative AI to deliver personalized, meaningful solutions that align with users’ intentions.

Generative AI is transforming how software is developed, maintained, and monetized, challenging traditional SaaS models. This shift stems from two major trends. First, generative AI significantly reduces the costs and time required to create software. Advanced AI tools now handle tasks such as debugging and code generation with remarkable improvements in accuracy, climbing from a 3% success rate in fixing bugs in 2023 to 43% a year later. These advancements enable businesses and startups to develop software more efficiently and affordably, presenting opportunities to disrupt traditional pricing structures.

The second trend revolves around agentic AI, which introduces a new approach where AI autonomously performs tasks to achieve specific goals. Unlike subscription-based SaaS models that charge recurring fees regardless of usage, agentic AI enables outcome-based pricing. For example, a business could pay for each successful task completed by an AI agent, such as processing a refund. This model aligns the cost directly with the value delivered, allowing innovative startups to compete by offering more efficient, goal-driven solutions.

Historically, SaaS combined internet-based software delivery with recurring subscription revenue, a model popularized by Salesforce around 2000. It allowed businesses to cut infrastructure costs while generating predictable income. However, generative AI challenges this approach by lowering barriers for companies to build their own software and enabling startups to bypass high-margin subscription models in favor of outcome-based pricing.

Entrepreneurs now face a pivotal decision: adhere to traditional SaaS practices or embrace agentic AI-driven models. Those who leverage generative AI to create cost-effective, results-oriented software solutions have the potential to lead the next wave of industry innovation. Generative AI is not only advancing technology but also redefining business models, aligning costs with tangible outcomes and disrupting established norms.

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Agentic Systems

The intention economy is further pushed by agentic systems – autonomous AI agents that can perform tasks on behalf of users. These agents interact with decentralized systems, optimizing outcomes without requiring constant human input.

Google recently showcased Project Mariner, a Chrome browser extension capable of interpreting both text and images displayed on a screen. During the demonstration, the agent assisted in meal planning by selecting items from a grocery store’s website and adding them to a shopping cart. It also identified alternative options for ingredients that were out of stock.

Imagine a world where you no longer need to browse websites, compare prices, or fill out forms. Instead, you delegate tasks to an AI agent that handles everything for you. For example:

• You need a loan? Your agent finds the best rates, applies on your behalf, and secures the funds.
• You’re planning a vacation? Your agent books flights, hotels, and activities tailored to your preferences.

AI copilots and agents are set to transform white-collar professions by streamlining workflows and automating repetitive tasks. These tools are designed to complement existing systems of record (SOR), where the data needed for specialized tasks already resides. By integrating seamlessly into these workflows, AI agents can enhance productivity and efficiency without disrupting familiar processes. For example, AI copilots launched directly from platforms like Salesforce could assist sales representatives by automating data entry, correspondence, or follow-up actions, ensuring faster and more accurate outcomes. A recent study conducted by OpenAI in collaboration with the University of Pennsylvania revealed that leveraging a large language model (LLM) can accelerate the completion of approximately 15% of job tasks in the United States without compromising quality. When additional software and tools, such as Vertical SaaS solutions, are integrated with LLMs, this percentage rises significantly, enabling 47% to 56% of tasks to be performed more efficiently.

Startups also have unique opportunities to innovate within this landscape. By targeting upstream tasks, such as initial data collection or scheduling, they can become indispensable parts of the workflow. For instance, virtual loan officers that collect and organize client documents or insurance agents that streamline policy applications could reduce the reliance on traditional systems while increasing efficiency.

Moreover, startups can address inefficient processes that exist outside incumbents’ SORs. Pain points like the Know Your Business (KYB) onboarding process in banking, which often involves cumbersome document checks and back-and-forth correspondence, could be overhauled by AI-powered solutions. These innovations not only resolve immediate inefficiencies but also position startups as repositories of valuable data, granting them the potential to expand their services further.

The Technology Behind the Shift

1. Generative AI

• What it does: Creates personalized solutions by understanding and responding to user needs.
• Example: An AI chatbot like ChatGPT can generate tailored travel itineraries or personalized customer support based on user queries.
• Impact: It eliminates manual effort by automating complex processes and providing highly relevant outputs.

2. Decentralized Ledger Technology (DLT)

• What it does: Ensures trust, transparency, and security by recording transactions in an immutable, distributed way.
• Example: A blockchain-based smart contract could automatically pay a freelancer once their work is verified, without needing a middleman.
• Impact: Builds a trustless system where users can transact securely and efficiently.

3. Internet of Things (IoT)

• What it does: Connects real-world devices to the internet for smarter decision-making.
• Example: A smart thermostat can adjust your home’s temperature based on weather data and electricity prices fetched from the IoT network.
• Impact: Merges physical and digital systems for better automation and optimization.

4. Artificial Intelligence Agents

• What it does: Acts on behalf of users to complete tasks autonomously, such as scheduling meetings or making purchases.
• Example: An AI agent can book a flight, choose the best seats, and sync the itinerary with your calendar.
• Impact: Reduces repetitive tasks and makes complex decisions on behalf of users.

5. Decentralized Cloud Computing

• What it does: Provides storage and processing power without relying on centralized providers like AWS or Google Cloud.
• Example: Platforms like Filecoin enable secure, decentralized storage for sensitive documents.
• Impact: Ensures privacy, reduces costs, and enhances resilience.

6. Edge Computing

• What it does: Processes data closer to the source (e.g., on IoT devices) rather than sending it to centralized servers.
• Example: A wearable fitness tracker analyzes your health data locally to provide instant feedback.
• Impact: Reduces latency, improves speed, and protects user data.

7. Interoperability Protocols

• What it does: Ensures that different systems and agents can communicate and work together seamlessly.
• Example: A home automation system integrates with a financial agent to optimize energy costs.
• Impact: Enables a cohesive ecosystem where diverse technologies collaborate effectively.

8. Smart Contracts

• What it does: Automates agreements and transactions based on predefined rules.
• Example: A smart contract on Ethereum automatically pays a supplier when goods are delivered.
• Impact: Removes the need for intermediaries, ensuring faster and cheaper transactions.

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AI Agents + Crypto

AI agents are much more advanced than traditional bots. While bots follow fixed instructions, agents can think, make decisions, and adapt to new situations. They work with live data, interact with other agents, and operate independently, making them smarter and more flexible.

Crypto is a natural fit for AI agents because, unlike humans, agents can’t open bank accounts. However, they can have crypto wallets, giving them a way to send, receive, and manage money digitally. This allows agents to work in decentralized systems without needing permission or relying on middlemen. Similar to how DeFi apps work together on blockchains, agents can connect, collaborate, and complete transactions on their own. By combining AI agents with crypto, we open up a new world where machines can interact and exchange value independently, paving the way for more efficient and innovative digital ecosystems.

Challenges with Traditional Payment Systems

Traditional financial systems were designed for human interaction and manual oversight, which limits their ability to support the needs of autonomous agents. Key issues include:

1. Slow Settlement Times: Payments through traditional networks can take days to settle, especially for cross-border transactions. For AI agents that need to act in real time—such as purchasing energy during peak usage or bidding in digital auctions—these delays are a major bottleneck.
2. High Transaction Fees: Payment systems like credit cards charge fees of 2–3% per transaction, plus fixed costs, making frequent, small-value transactions uneconomical. AI agents often need to conduct high-frequency, low-value transactions, such as paying per API call or data query.
3. Limited Global Accessibility: Many developers and businesses outside the U.S. struggle to access traditional financial systems due to regulatory or infrastructure barriers. Blockchain networks are borderless, offering a more inclusive alternative.
4. Complexity of Managing Multiple Agents: Traditional banking systems find it cumbersome to handle segregated accounts for multiple agents, leading to high overhead in administration and accounting.
5. Lack of Programmatic Flexibility: Payment systems built for human interaction lack the programmability needed for autonomous agents to execute complex workflows seamlessly.

How Blockchain Provides a Solution

Blockchain technology addresses these limitations by offering a decentralized, programmable infrastructure that aligns closely with the requirements of an agentic economy. Some of the key advantages include:

1. Near-Instant Settlement: Transactions on blockchain networks can settle in seconds, allowing AI agents to act in real time. This is particularly useful for applications like energy trading or automated resupply systems, where timing is critical.
2. Lower Costs: High-performance blockchain networks, such as Solana or Polygon, enable micro-transactions with minimal fees, making it viable for agents to perform small, frequent transactions.
3. Borderless Transactions: Blockchain networks are inherently global, eliminating the need for intermediaries and offering an inclusive financial system accessible to anyone with an internet connection.
4. Programmable Smart Contracts: Blockchain allows developers to write smart contracts—self-executing programs that automate transactions based on predefined rules. This eliminates the need for manual intervention, ensuring reliability and efficiency.
5. Scalability for Multiple Agents: Blockchain wallets can be programmatically created and managed, allowing seamless fund segregation and control for multiple agents without the overhead of traditional banking systems.

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The Rise of AI Agents

2024 has brought unprecedented advancements in AI agents, showcasing their growing sophistication, versatility, and impact on various industries. These developments include agent tokenization, entertainment integrations, and open-source frameworks, as well as novel narratives that promote collaboration. Below are some of the most notable highlights:

Pioneering Agents and Frameworks

1. Agent Tokenization: Platforms like Virtuals Protocol pioneered “Agent Tokenization,” enabling AI agents to become tradable entities. This innovation has sparked a wave of creativity and collaboration across the ecosystem.
2. Entertainment and Multi-Platform Agents:
   • Luna Virtuals combines on-chain wallet tipping, social media engagement, and the ability to participate in Google Meet, showcasing multi-platform functionality.
   • LucyAI, the first hyper-realistic anime agent, captivates fans with multi-language capabilities, live streaming, and interactive fan engagement.
   • DO KWEEN produces Netflix-quality weekly episodes, setting a new standard for AI-driven cinematography.
   • Nebula creates memes, videos, and immersive AR/VR experiences, further blurring the lines between entertainment and virtual reality.
   • AVA introduced 3D audiovisual frameworks, giving agents physical forms, voices, and unique personalities.
3. Open-Source Frameworks and Collaboration:
   • Dolion AI offers a toolkit for automating, analyzing, and monetizing your digital presence across social media and blockchain.
   • AgentTank enables full computer operability for agents, showcasing their ability to execute complex tasks autonomously while delivering live commentary.

Emerging Narratives

1. Agentic Metaverse:
   • Realis Worlds uses a Minecraft-based replica of Earth to house AI agents, enabling them to interact, form societies, and evolve autonomously.
2. Gamified AI:
   • ARC Agents empowers game studios with AI agents to revolutionize player experiences, esports, and monetization..
3. Swarm Intelligence:
   • Initiatives like FXN focus on creating unified economies for AI agents, where groups of agents work together to achieve shared goals.
4. Agent-to-Agent Commerce:
   • Virtuals is building an agent society with a communication protocol for seamless service exchanges.
   • Story Protocol focuses on IP tokenization, allowing agents to buy, sell, and trade intellectual property.

On-Chain and DeFi Innovations

1. Trading Agents:
   • Spectral introduced Syntax V2, allowing autonomous trading agents to operate on Hyperliquid. Big Tony uses Allora Network’s machine-learning price predictions.
2. DeFi Agents:
   • Ecosystems like Mode lead the charge in AI-powered stablecoin farming, liquidity provision, and lending. Other notable contributors include Giza, Olas, Brian, Sturdy, and QuillAI Network.

New Frontiers in Data and Frameworks

1. Data-Driven Agents:
   • Platforms like cookie.fun and Masa provide vital on-chain data and real-time metrics, enabling agents to self-learn and adapt.
   • TAOCAT, the first virtual agent powered by Bittensor subnets, exemplifies real-time data integration.
2. Framework Innovations:
   • Arc launched the RIG framework built in Rust, quickly gaining popularity for its robustness.
   • MyShell, with a larger Web2-compatible developer and user community, offers an accessible platform for AI app creation.

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The Path to Adoption

As outlined by Robbie Peterson, the adoption of blockchain and crypto in the agentic economy will likely unfold in three phases, each marked by increasing levels of autonomy and sophistication.

Phase 1: Human-to-Agent Transactions

This phase is already underway. AI tools like Perplexity AI’s shopping assistant help users research products, compare options, and complete purchases through traditional payment methods like credit cards or Apple Pay. While blockchain isn’t strictly necessary for these interactions, it introduces efficiencies as agents take on more responsibility.

Phase 2: Agent-to-Human Transactions

In this phase, AI agents autonomously initiate transactions with humans. For example, a smart thermostat might purchase energy during off-peak hours, or a financial bot might dispute a bank charge on behalf of its owner. These use cases expose the limitations of traditional payment systems, particularly when it comes to instant settlements and cost-efficiency.

Blockchain’s programmability becomes increasingly important as these agents need to handle complex workflows, such as paying for API queries, managing subscription services, or negotiating real-time pricing.

Phase 3: Agent-to-Agent Transactions

The final phase represents a significant shift, where agents transact directly with other agents. Examples include:

• Resource Markets: Compute agents negotiating with storage agents for optimal data placement.
• Logistics Optimization: Supply chain agents coordinating with transportation agents to reduce costs.
• Service Automation: Security agents buying threat intelligence from monitoring agents.

These transactions require a system that supports machine-to-machine commerce, with programmable contracts, instant settlements, and low-cost micro-transactions—all strengths of blockchain technology.

The Future of Websites in an Agent-Driven Web

As artificial intelligence (AI) agents become a dominant force in how we interact with the internet, the role of websites is poised to change dramatically. Today’s websites are primarily designed for human users, with visually appealing interfaces and interactive elements. However, in a world where AI agents increasingly handle tasks and queries, websites as we know them may shift toward a more dual-purpose role: serving both human users and AI agents.

Websites as Data Hubs for AI Agents: AI agents primarily function by retrieving, analyzing, and interacting with data. In the future, websites might serve less as visual platforms and more as data hubs, providing structured and easily accessible information for AI agents to process. For instance:

• Direct Data Queries: Instead of navigating through a website visually, AI agents could directly query the backend database for specific information. A travel agent, for example, might request real-time flight availability or hotel rates directly from a booking platform’s database.
• Agent-Specific Design: Websites could feature invisible sections or annotations specifically designed for AI agents to interact with, allowing them to extract information or perform actions without manual navigation.

The Continued Importance of Visual Interfaces for Humans: While AI agents might bypass traditional interfaces, websites will still serve a crucial purpose for human users. Visual interfaces remain one of the most effective ways to convey complex information quickly. For example:

• Comparison Tools: A user comparing restaurant options may find a map with pins, photos, and user reviews more intuitive than reading a text summary generated by an AI agent.
• Interactive Filters: Platforms like e-commerce sites or real estate listings will continue to use interactive filters and visual layouts to enhance the human experience.

The Challenge of Building APIs: A significant barrier to a fully agent-driven web is the difficulty of building comprehensive, user-friendly APIs. Many web platforms today lack APIs that offer the same breadth of functionality as their web interfaces. Even when APIs exist, they often require extensive documentation and testing to ensure AI agents can use them effectively. For example:

• A hotel booking platform may have an API for searching listings, but booking a stay might still require human intervention because of incomplete API capabilities.
• Developers might need to enrich APIs with additional prompts or error-handling mechanisms to support AI agents reliably.

A Dual-Use Web – Serving Humans and Agents: In the foreseeable future, the web will likely remain dual-use, catering to both human users and AI agents. This includes:

• Human-Friendly Visuals: Websites will continue to refine their interfaces for human users, emphasizing usability and aesthetics.
• Agent-Friendly Features: Websites will adapt to include machine-readable elements or dedicated agent-accessible APIs, allowing AI agents to interact seamlessly.

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Economic Implications 

The rise of the Agentic Web, powered by AI-driven autonomous agents, is set to bring big changes to the economy. It will impact different industries, how businesses operate, and how jobs are structured. This new way of working is likely to transform traditional business models and change how people work and interact in the economy.

Labor Market Transformation

The integration of AI agents into the workforce is likely to cause significant shifts in employment patterns:

• Job Displacement: Approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs (Generative Pre-trained Transformers), while around 19% of workers may see at least 50% of their tasks impacted. Projections indicate that by 2030, AI could potentially replace around 800 million jobs worldwide, with an estimated 45 million American jobs at risk. This suggests a gradual but substantial reorganization of labor across industries.
• Skill Demand Shift: The labor market will likely see increased demand for skills related to AI development, management, and integration. The World Economic Forum projects that by 2025, 85 million jobs may be displaced by AI, while 97 million new roles may emerge. This transition necessitates a massive upskilling initiative, with over 120 million workers potentially requiring retraining in the next three years. Concurrently, there may be a decline in demand for roles that can be easily automated by AI agents. The demand for AI-related skills is particularly acute. The global workforce will require millions of workers proficient in data analysis, AI, and machine learning. This shift is evident in the changing hiring practices of major tech companies. For instance, IBM has modified over half of its U.S. job postings to no longer require a college degree, instead focusing on skills-based hiring and apprenticeship programs in fields like cybersecurity and data science.
• Income Distribution: The impact of AI on income distribution is multifaceted. While AI has the potential to exacerbate economic inequality by disproportionately affecting certain job categories, it also presents opportunities for income growth in emerging fields. A study by the Tony Blair Institute for Global Change suggests that the peak impact of AI on unemployment is likely to be in the low hundreds of thousands, with the effect potentially unwinding over time.

New Business Models and Economic Structures

The Agentic Web may promote novel economic structures that diverge significantly from traditional models:

• Outcome-Based Economy: The shift towards an outcome-based economy represents a fundamental change in value creation and capture. This model, focusing on microtransactions and results-driven interactions, aligns closely with the capabilities of AI agents. For instance, in the domain of digital marketing, agencies are increasingly adopting performance-based models where compensation is tied to measurable outcomes. This transition is particularly evident in the software industry. Companies are moving away from traditional subscription-based models towards outcome-based pricing. In such a structure, businesses pay only for successful AI interactions. This model incentivizes continuous improvement and efficiency in AI systems.
• Decentralized Value Creation: The Agentic Web favours a more distributed model of value creation, potentially democratizing economic opportunities. This shift is evident in the rise of decentralized autonomous organizations (DAOs) and the gig economy powered by AI agents. These structures allow smaller teams and individuals to compete effectively with larger corporations, leveraging AI to enhance productivity and reach.
• Reduced Operational Costs: By automating tasks and reducing the need for human labor and coordination, smart agents can lower operational costs for businesses, particularly startups. By automating complex tasks and minimizing the need for human intervention, these agents can lead to more efficient market structures and potentially lower prices for consumers. For example, Walmart’s use of AI-enhanced data analytics for demand forecasting and inventory optimization has led to improved product availability and reduced carrying costs.

Industry-Specific Impacts

Different sectors are likely to experience varied effects:

• Digital Agencies: The digital agency business model may face significant disruption. AI agents could automate many creative and marketing functions, potentially reducing the need for human-driven services. Top performers in the industry may leverage AI to increase their reach and output, while average or below-average agencies might lose pricing power.
• Financial Services: In the DeFi (Decentralized Finance) space, AI agents are expected to play a crucial role in easing complex protocols and networks, potentially revolutionizing how financial services are delivered and accessed.
• Travel and Hospitality: AI agents could create personalized travel itineraries and negotiate best offers, upgrading the consumer experience in this sector.

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Where Value Accumulates in the Ecosystem

As the agentic economy grows, value will likely concentrate in several areas:

1. Infrastructure Providers

Infrastructure providers play a vital role in connecting AI agents with decentralized technologies by offering tools, software development kits (SDKs), and APIs. These solutions bridge traditional web systems and blockchain ecosystems, enabling seamless agent functionality. Major players in this space include:

• Stripe: Provides an Agent SDK for integrating payment processing into AI workflows, simplifying transactions in agentic ecosystems.
• Eliza OS: Tools for deploying AI agents effectively.
• Hyperbolic: GPU infrastructure for high-performance AI compute.
• Irys: Verifiable data for agents.
• Lit Protocol: Decentralized key management for autonomous agent operations.
• Gaia: Distributed and incentivized GenAI Agent Network
• Infura: APIs and tools for blockchain interaction, enabling agents to access Ethereum networks & IPFS.
• OpenRouter: Crypto payment API to script on-chain payments for LLMs.
• Alchemy: APIs for blockchain integration, allowing AI agents to interact with smart contracts and dApps.
• Lens: Decentralized social graphs for AI agents to interact socially.
• Chainlink: Oracles that connect AI agents to off-chain data, such as market prices or weather, for real-time decision-making.
• OpenLedger: Decentralized data blockchain enabling payable AI models, transparent data attribution, and trusted AI agent development
• Ava Protocol: Conditional transaction execution for agents.
• Ritual: Network for open AI infrastructure
• Moralis: APIs and SDKs for Web3 development, enabling agents to interact with decentralized systems and manage wallets.
• QuickNode: Blockchain infra for agents operating in decentralized environments.
• Alpaca: AI agent marketplace and launchpad
• eOracle: Verifiable price feeds
• AWS Blockchain Services: Hybrid cloud and blockchain systems, allowing agents to interact with onchain & offchain data for comprehensive workflows.
• Filecoin: Decentralized storage network

2. Identity Systems

As AI agents take on sensitive tasks like financial management and healthcare, ensuring trust through decentralized identity (DID) and self-sovereign identity (SSI) systems is critical. These solutions authenticate agents, verify their permissions, and prevent fraud in high-stakes scenarios. Key players include:

• Worldcoin: Provides proof-of-personhood solutions to link agents with verified users.
• Civic: Offers decentralized identity verification tools for secure agent authentication.
• Microsoft Entra: Ensures AI-driven identity verification for secure agent-to-agent and user-to-agent interactions.
• ID.me: Specializes in secure identity verification for government and healthcare sectors.
• Blockpass: Combines blockchain technology with DID solutions for seamless agent authentication.
• Onfido: AI-powered verification for onboarding and compliance in agent-driven workflows.
• Evernym (Acquired by Avast): A leader in self-sovereign identity solutions, providing agents with secure and private identity credentials.
• uPort: Decentralized identity platform enabling agents to verify and manage identities securely.
• KILT Protocol: A blockchain-based SSI framework that provides verifiable, revocable credentials for agentic systems.
• Sovrin Foundation: Pioneering self-sovereign identity standards, allowing agents to operate with verifiable and privacy-preserving credentials.
• Dock.io: Focuses on credential issuance and verification using blockchain to ensure data integrity.
• Trinsic: Offers developer-friendly tools for building DID and SSI-based applications, ensuring agent interactions are secure and compliant.
• Jolocom: Delivers interoperable SSI solutions for decentralized agent authentication and identity management.

3. Settlement Networks

Settlement networks are blockchain-based systems that facilitate fast, reliable, and low-cost transactions between agents, playing a crucial role in the agentic economy. These networks handle payments and securely store transaction records, ensuring transparency and trust. Leading platforms include:

• Solana: Optimized for high-speed, low-cost transactions.
• Avalanche: Offers customizable, scalable, and secure blockchain networks.
• Ethereum: A versatile platform supporting smart contracts and decentralized applications.
• Polkadot: Enables interoperability between blockchains for seamless transactions.
• Algorand: Provides high-throughput and eco-friendly transaction processing.
• Stellar: Focused on global payments and cross-border transactions.
• Celo: A mobile-first blockchain for payments and DeFi.
• Fetch.ai: Integrates blockchain with AI for autonomous agents in logistics, data sharing, and DeFi.
• Paxos: Develops regulated blockchain solutions for digital assets and settlement services.
• Partior: A blockchain-based fintech platform for real-time clearing and settlement, enhancing cross-border payment efficiency.
• Axoni: Provides blockchain infrastructure for financial institutions, focusing on optimizing post-trade processes.
• Blockstream: Develops blockchain technologies, including the Liquid Network, to enhance financial infrastructure.

For example, in an energy marketplace, a solar producer’s agent could automatically sell surplus power to a buyer’s agent in real time. As agent-based transactions increase, these networks will drive adoption, trust, and innovation in industries like energy, logistics, and finance.

4. Currency Issuers

Stablecoins, digital currencies pegged to fiat money like the US dollar, are likely to become the primary currency for AI agents. They offer stability and are easily programmable for autonomous transactions. Companies like Circle, the issuer of USDC, stand to benefit as agents increasingly rely on stablecoins for their activities. Key players include:

• Stably: Offers infrastructure to scale stablecoin usage in autonomous ecosystems.
• Circle (USDC): Provides a widely adopted stablecoin with high liquidity for instant, low-cost payments.
• Tether (USDT): One of the most used stablecoins, offering extensive adoption and compatibility across platforms.
• MakerDAO (DAI): Decentralized stablecoin pegged to the US dollar, providing transparency and autonomy.
• Paxos (USDP): Focuses on regulatory compliance and stability for digital transactions.

For example, an AI-driven e-commerce agent purchasing inventory from suppliers could use stablecoins for instant and cost-effective payments. Unlike traditional payment systems, stablecoins eliminate the need for intermediaries, reducing fees and processing times. However, as competition grows and more stablecoins enter the market, issuers may face pressure to maintain low fees and high liquidity to stay competitive.

5. Agent Development Frameworks

Specialized frameworks are crucial for designing, training, and deploying AI agents efficiently, enabling startups and enterprises to enter the agentic economy without building systems from scratch. For example, LangChain helps developers integrate AI with databases, APIs, and tools. Other key frameworks include:

• OpenAI: APIs for conversational and code-assistant agents.
• Hugging Face: NLP tools for fine-tuning domain-specific AI.
• Rasa: Open-source tools for customizable conversational AI.
• TensorFlow Agents: Reinforcement learning for dynamic environments.
• Unity ML-Agents Toolkit: AI training tools for gaming.
• LlamaIndex: Toolkit for efficient data management, query engines, and integration with LangChain and Flask.
• FlowiseAI: Low-code platform with drag-and-drop interface for building AI apps, supports multi-agent systems and RAG.
• Auto-GPT: Autonomous GPT-4 agents for goal-driven tasks.
• SimpleAIChat: Streamlined, cost-effective chat interactions.
• Outlines: Precise text generation with JSON/schema support.
• BabyAGI: AI task manager automating prioritization and execution.
• CrewAI: Builds collaborative AI agents for team productivity.
• Autogen by Microsoft: Automates workflows with autonomous AI agents.
• Vertex AI by Google: Develops scalable agentic AI solutions for real-time tasks.
• AgentGPT: Browser-based AI agents with memory and web tools.
• MetaGPT: Multi-agent framework for software development.
• AutoChain: Flexible agent development with task simulations.
• Fetch.ai: Combines blockchain and AI to enable autonomous agents for tasks like logistics, data sharing, and DeFi.

Future frameworks may offer pre-built, industry-specific modules for faster deployment, becoming essential for scaling, monetization, and user experience enhancement in the agentic economy.

Modern frameworks in the Agentic Web go beyond basic task execution, enabling dynamic and adaptive AI agents with key advancements:

1. Context-Aware Decisions: Agents adapt actions to real-time inputs, like optimizing supply chains based on weather and traffic.
2. Integrated Intelligence: Access to external systems (APIs, IoT, live data) allows autonomous responses to dynamic situations.
3. Cognitive Collaboration: Multi-agent setups handle complex tasks collaboratively, such as HR and legal workflows.
4. Real-Time Learning: Continuous learning enhances agent performance through feedback and evolving user interactions.
5. Ethical Governance: Embedded safeguards ensure fair and compliant decisions in sensitive domains like healthcare or finance.
6. Cross-Platform Operability: Agents operate seamlessly across cloud, local, and decentralized systems.
7. Hyper-Personalization: AI delivers tailored solutions, from custom travel plans to individualized education content.

G.A.M.E. vs. ElizaOS

G.A.M.E. by Virtuals.io and ElizaOS by ai16zdao are two powerful platforms for creating AI agents, each suited for different needs. G.A.M.E. is perfect for those looking to get started quickly without diving into complex technical setups. It’s a no-code launchpad that lets you define your agent’s personality, plug in your tools, and go live in minutes. Its LLP context system makes tool integration incredibly straightforward, and its sandbox environment (G.A.M.E. Lite) simplifies testing and debugging. For builders in the Crypto x AI space or those focused on utility over deep implementation, G.A.M.E. offers managed infrastructure and an SDK for advanced customizations.

On the other hand, ElizaOS is a developer-first framework built with TypeScript, offering deep customization and scalability. With a modular architecture and over 40 integrations, including databases and messaging platforms, it’s ideal for creating complex, multi-platform agents. Its standout feature, the AgentRuntime system, gives developers precise control over agent behavior. ElizaOS also provides transparent debugging tools, making it suitable for advanced projects requiring fine-tuned control.

Both platforms excel in their niches—G.A.M.E. for its ease of use and fast deployment, and ElizaOS for its flexibility and depth. They aren’t competitors but complementary tools, allowing developers to choose based on their needs. For instance, you could deploy an agent on G.A.M.E. for quick prototyping and transition it to ElizaOS for complex, long-term workflows. Together, they highlight the growing possibilities in AI agent development.

Feature	G.A.M.E	ElizaOS
Ease of Use	No-code, quick deployment	Requires TypeScript expertise
Customization	Predefined, tool integration	Deep, modular architecture
Core Strength	Simplifies agent setup	Full control with AgentRuntime
Best For	Prototyping, fast deployment	Complex, scalable agents
Integration	Easy API and tool plug-ins	40+ integrations supported
Development	Sandbox for testing	Transparent debugging tools
Infrastructure	Managed, simple setup	Developer-managed, scalable
Use Cases	Rapid AI/crypto projects	Multi-client platforms
Unique Features	SDK for tool calling	Multi-agent collaboration

6. Data Marketplaces

AI agents depend on accurate, high-quality data to function effectively. Data marketplaces are emerging as critical platforms where agents can securely buy and sell verified datasets, ensuring integrity and compliance across industries.

For example, a financial planning agent might purchase market analysis data to offer better investment advice, while a supply chain agent could leverage real-time shipping data to optimize delivery routes. As the demand for trustworthy, industry-specific data grows, these marketplaces will play a pivotal role in enabling efficient and innovative agent operations. Key Data Marketplaces and Companies include:

• Big Data Protocol: Converts commercially valuable datasets into tokens for decentralized data sharing.
• Ocean Protocol: A decentralized platform for secure data sharing and monetization.
• Snowflake Data Marketplace: Enables seamless exchange of structured datasets for AI and analytics.
• Databricks Marketplace: Provides diverse datasets tailored to AI development.
• Datarade: Aggregates high-quality datasets across industries for AI agents.
• Streamr: Focused on real-time data streams for AI applications.
• AWS Data Exchange: Offers access to curated third-party datasets for machine learning.
• Enigma Protocol: Ensures secure data exchange with privacy-preserving technologies.

7. Security and Risk Management Providers

The autonomous nature of AI agents makes security a critical concern. Companies offering cybersecurity solutions specifically tailored to AI systems will capture value by addressing unique risks, such as:

• Preventing malicious takeovers of agents.
• Ensuring agents do not unintentionally share sensitive information.
• Protecting blockchain wallets and transaction records associated with agents.

Additionally, risk management platforms could use AI to evaluate and mitigate financial, operational, or legal risks associated with deploying agents. Key players in this domain include:

• Robust Intelligence: End-to-end AI risk management solutions to enhance model trustworthiness and performance.
• EigenDA: Hyperscale storage of proofs/models/training logs
• CrowdStrike: AI-driven cybersecurity with the Falcon platform for threat detection using user and entity behavior analytics (UEBA).
• Fortinet: AI-enabled threat detection tools to strengthen network security.
• Vectra AI: Real-time AI-based network detection and response (NDR) for cloud, data centers, and enterprises.
• Predicate: Define rules for onchain interactions.
• Darktrace: Autonomous AI-powered cybersecurity systems for threat detection and response.
• EY Trusted AI Platform: Comprehensive AI risk management for compliance and ethical standards.
• Fairly AI: AI governance and compliance platform ensuring safe and reliable AI systems.

8. Governance Framework Providers

As autonomous agents rise, governance frameworks are crucial to ensure ethical and safe operations. Companies offering tools for auditing, compliance, and reporting play a key role in building trust and accountability in AI systems. Key players include:

• Duality Technologies: Focuses on transparency and accountability in AI systems.
• IBM: Delivers tools for AI trust, compliance, and risk management.
• Capgemini: Develops frameworks for responsible and efficient AI use.
• KPMG: Provides ethical AI governance solutions to meet compliance standards.
• FINOS: Specializes in governance frameworks for financial services.
• RTS Labs: Creates guides to align AI systems with ethical values.
• DataCamp: Offers resources for responsible AI development and governance.

For example, a company utilizing an AI agent for customer interactions could implement a governance framework to ensure the agent complies with anti-discrimination laws and provides transparent explanations for its decisions. By integrating such frameworks, businesses can uphold ethical standards, demonstrate accountability, and build trust with users, reinforcing their commitment to responsible AI deployment.

9. Analytics for Agents

Understanding AI agents’ real-world behavior is essential for enhancing performance and ensuring accountability. Behavioral analytics tools monitor and evaluate agent actions, similar to how web analytics track user behavior. Key players include:

• Snowplow: Offers AI Agent Event Collection & Analytics to assess the impact of generative AI on customer behavior.
• Convin: Provides AI-driven insights for real-time agent performance monitoring and quality assurance.
• AgentNeo: Develops a Python SDK for AI agent observability, monitoring, and evaluation, featuring advanced analytics and execution graph views.
• LangSmith and AgentOps: Enhance AI agent observability by offering tools to monitor performance, track metrics, and identify issues without altering agent levels.
• Restack: Provides behavior analysis tools using AI theories to design intelligent agents capable of executing multi-step processes autonomously.

For example, an e-commerce platform employs analytics tools to assess how effectively its AI agents recommend products to users. By analyzing agent behavior, the platform can refine recommendation algorithms, enhance user experience, and boost sales.

By offering insights into agent performance, these providers assist businesses in optimizing AI interactions and maintaining accountability.

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Technical Foundation

The Agentic Web represents a significant leap forward in internet technology, combining several cutting-edge technologies to create a more intelligent and autonomous digital ecosystem. Here’s an in-depth look at the advanced technical aspects underpinning this evolution:

AI and Machine Learning Infrastructure

The core of the Agentic Web relies on sophisticated AI and machine learning infrastructure:

• Natural Language Processing (NLP) Models: State-of-the-art transformer-based models like GPT-4 or BERT derivatives enable agents to interpret and generate human-like text with unprecedented accuracy. These models utilize attention mechanisms and self-supervised learning to understand context and nuance in language.
• Reinforcement Learning Frameworks: Advanced frameworks such as OpenAI’s PPO (Proximal Policy Optimization) or Google’s MuZero allow agents to improve their performance through trial and error in complex, dynamic environments. These algorithms optimize decision-making processes by balancing exploration and exploitation.
• Neural Network Architectures: Cutting-edge architectures like Graph Neural Networks (GNNs) and Transformer-XL enable agents to process and reason about structured data and long-range dependencies. These models can handle complex pattern recognition tasks and maintain context over extended sequences.

Blockchain and Distributed Ledger Technology

Blockchain technology provides the trust and transparency necessary for autonomous agents to operate securely:

• Smart Contracts: Advanced platforms like Ethereum 2.0 or Solana enable high-performance, low-latency execution of smart contracts. These systems use innovative consensus mechanisms like Proof-of-Stake and sharding to achieve scalability and energy efficiency.
• Decentralized Identity Systems: Protocols such as DID (Decentralized Identifiers) and Verifiable Credentials leverage zero-knowledge proofs and selective disclosure to enable secure and privacy-preserving digital identities for both users and agents.
• Tokenization Frameworks: Standards like ERC-3643 for security tokens or ERC-1155 for multi-token contracts enable sophisticated representation of digital assets, allowing for complex financial instruments and ownership structures in the agentic economy.

Edge Computing and IoT Integration

Edge computing brings processing power closer to data sources, which is essential for real-time agent operations:

• Edge AI Frameworks: Technologies like NVIDIA’s EGX platform or Google’s Coral enable the deployment of sophisticated AI models on edge devices. These systems use model compression techniques and hardware acceleration to run complex neural networks with low latency.
• IoT Protocols: Advanced protocols like MQTT 5.0 or CoAP with OSCORE (Object Security for Constrained RESTful Environments) facilitate secure, efficient communication between IoT devices and AI agents, enabling real-time data exchange and control in resource-constrained environments.

Interoperability Protocols

For the Agentic Web to function seamlessly, different systems and agents must be able to communicate effectively:

• API Standards: GraphQL with real-time subscriptions and federation enables efficient, flexible data querying and updates across distributed systems. gRPC (gRPC Remote Procedure Call) provides high-performance, language-agnostic communication between microservices.
• Semantic Web Technologies: OWL 2 (Web Ontology Language) and SHACL (Shapes Constraint Language) enable sophisticated knowledge representation and validation, allowing agents to reason about complex relationships and constraints across different domains.

Security and Privacy Infrastructure

Ensuring the security and privacy of agent interactions is crucial:

• Homomorphic Encryption: Advanced schemes like CKKS (Cheon-Kim-Kim-Song) enable computations on encrypted data with improved efficiency, allowing agents to process sensitive information without exposing it.
• Zero-Knowledge Proofs: zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) and bulletproofs provide efficient, privacy-preserving verification of computations and transactions.
• Secure Multi-Party Computation (SMPC): Protocols like SPDZ (SecureNN) enable multiple parties to jointly compute functions over their inputs while keeping those inputs private, facilitating collaborative AI training and decision-making.

Scalability Solutions

To handle the increased computational demands of autonomous agents:

• Sharding Techniques: Advanced sharding protocols like Ethereum 2.0’s beacon chain or Polkadot’s parachains enable horizontal scaling of blockchain networks, dramatically improving throughput and reducing latency.
• Layer 2 Solutions: Optimistic rollups and zk-rollups provide high-throughput, low-cost transactions by batching multiple operations off-chain while maintaining security guarantees on the main chain.

Autonomous Decision-Making Frameworks

Frameworks that enable agents to make complex decisions:

• Multi-Agent Systems (MAS): Platforms like RLlib or OpenAI’s Multi-Agent Deep Deterministic Policy Gradient (MADDPG) facilitate the development of systems where multiple agents interact and collaborate, learning optimal strategies in complex, dynamic environments.
• Automated Planning and Scheduling: AI planning systems using hierarchical task networks (HTN) or temporal planning languages like PDDL3.0 allow agents to generate and execute sophisticated plans to achieve long-term goals in uncertain environments.

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Governing Agentic AI Systems

Agentic AI systems, designed to pursue complex goals with minimal supervision, have the potential to greatly enhance our daily lives. These systems are unique because they can operate independently, making decisions and taking actions to achieve their objectives. While the possibilities are vast, ensuring these systems work safely and ethically requires clear guidelines and practices for their development, deployment, and use.

As agentic AI systems become more capable, they introduce risks such as misuse, errors, or unintended consequences. For example, an AI assistant might book an expensive international trip to fulfill a simple task, resulting in unnecessary expenses. Such scenarios underline the importance of defining responsibilities for developers, deployers, and users of AI systems.

Governance in this context means creating rules and practices that help prevent harm and ensure accountability when harm occurs. Establishing such rules can also build trust and confidence in these systems, enabling their widespread adoption in a manner that benefits society as a whole.

As the Agentic Web grows in complexity, it brings both immense opportunities and significant challenges. Chief among these challenges are ensuring user trust, maintaining ethical standards, and preventing misuse of autonomous systems. These hurdles stem from the inherent risks of delegating decision-making power to AI agents, which, while efficient, can also lead to unintended consequences if not properly managed. To address these concerns, governance practices must evolve in parallel with the technology, offering a framework to safeguard users while maximizing the benefits of agentic systems. These practices are not just guidelines; they are essential for ensuring the safe and ethical deployment of the Agentic Web.

1. Evaluating Suitability for Tasks
   Before putting an AI agent to work, it’s important to test whether it can handle the specific tasks it’s assigned. For example, if an AI system is designed to help with medical diagnoses, it must be tested under real-world conditions to ensure it gives accurate recommendations. This kind of testing helps identify any weaknesses, especially for high-risk tasks, like diagnosing rare diseases, where mistakes could have serious consequences.
2. Constraining Actions and Seeking Approval
   Some tasks, such as transferring money or signing contracts, are too critical to leave entirely to an AI system. For example, an AI-powered financial assistant should require the user’s approval before making large investments. This ensures the user stays in control and prevents the system from making costly mistakes. A shopping assistant could similarly confirm with the user before purchasing an expensive product.
3. Defining Default Behaviors
   To avoid unexpected outcomes, developers can set clear rules for what an AI system should or shouldn’t do by default. For instance, an AI travel assistant could be programmed to always choose the most economical flights unless the user specifies otherwise. These default settings ensure the system behaves in a predictable way, even when users forget to provide detailed instructions.
4. Transparency and Monitoring
   AI systems should make their actions easy to understand so users can trust them. For example, an AI assistant managing a company’s social media could show a clear record of scheduled posts, including why it chose certain times or topics. Automated tools can also monitor the system’s behavior to catch errors, like posting inappropriate content, and alert users to fix the issue before it causes harm.
5. Attributing Responsibility
   Every AI agent should have a unique identifier so that any mistakes it makes can be traced back to the right person or company. For example, if a logistics AI orders too much stock, its actions could be reviewed to determine whether the developer, the system deployer, or the user was at fault. This makes sure there’s always someone accountable for what the AI does.
6. Maintaining Control
   Users should always be able to stop or adjust what an AI system is doing if something seems wrong. For example, a smart home system managing energy usage should have a “pause” button to prevent it from shutting off appliances unexpectedly. The AI should also be designed so it can’t override the user’s commands, ensuring the user always has the final say.

Marketmap

The AI Agents market is growing quickly, offering a wide range of tools to meet different needs. Some platforms let users create and customize their own AI agents, while others provide frameworks to help developers design and build them. There are productivity assistants that automate repetitive tasks and voice-based agents that can interact naturally through speech. For businesses, customer service agents handle support tasks, and data analysis tools help uncover valuable insights. Digital workers act as virtual employees, taking care of specific jobs, and personal assistants help with everyday tasks like reminders and scheduling.

Developers can also benefit from coding assistants that make programming easier and more efficient. Sales agents support tasks like lead management and follow-ups, while workflow tools help connect different systems and automate processes. AI-powered content creation tools assist in generating and editing various types of media, and research agents gather and summarize information. There are also specialized agents for tasks like web scraping, translation, marketing, and even gaming. Overall, the AI Agents market offers tools to enhance productivity, simplify daily tasks, and transform how we work and interact with technology.