In just a few months, we have seen hundreds & thousands of agents coming to the market everyday. As of Today, the agent market cap of the top thousand agents combined is close to $15b, which is quite impressive to see how web3 has given the space to these agents to thrive & survive.

As we move towards unlocking more value, it’s now high time to talk about how these agents can start building their ecosystem without human intervention in the backend & how the genuine financial autonomy for these agents will look like.

To understand this, firstly, we need to know how these AI agents work on a very high level today in Web3 & key components that need to be removed to bring the autonomy around the financial use cases around the agents.

AI Agent Architecture

At its core, every AI agent operates on a tripartite architecture that integrates intelligence, logic, and financial capabilities. The AI component serves as the brain, processing information and making decisions based on complex neural networks and machine learning models.

The logic layer acts as the nervous system, coordinating actions and managing state transitions, while the wallet component functions as the agent’s hands, executing transactions and managing assets.

This architecture, while theoretically sound, faces significant challenges in practical implementation.

Choking The Autonomy With Centralized Infrastructure

The current landscape of AI agents faces a fundamental challenge in its reliance on centralized infrastructure. This centralization manifests in two critical areas: deployment architecture and operational control. Traditional deployments typically host AI agents on centralized cloud providers like AWS, Google Cloud, or Azure, creating what appears to be a convenient but fundamentally flawed operational model.

This presents a fundamental challenge that strikes at the core of true autonomy.

A true autonomous agent cannot be controlled or run on the centralized infrastructure where a single entity can alter agent’s fate by withdrawing support or failing to provide sufficient infrastructure when needed.

There are three major choking points for these agents if they heavily rely on the infrastructure provided by the centralized players.

KYC Based Compute

These agents are not humans, nor do they have proof to be human; many centralized cloud providers require KYC information before anyone leases compute from them, which creates an issue for the agents to become autonomous & they will always rely on a human to keep paying for their infrastructure & in that case, the control remains on the dev who created the agents.

API Based Web2 Systems

If we assume that some of these existing centralized systems remove the restriction of accessing the compute via KYC, but still these systems won’t be able to remove the API based compute access, most clouds, by design, are not enabled to give out compute just by making the payments, all the payment confirmations are hooked into the API layer which informs the system to unlock the compute usage.

FIAT System

Even if they somehow solve the API & KYC issues, the fiat systems of these companies cannot be changed, at least not in the next 10 years, seeing the geo-political challenges & this alone kills the theory of autonomous agents even before reaching the practical stage.

Dev’s Influencing Decisions & Logics Behind the Scenes

Ok, I think we have discussed several problems with the centralized infrastructure; however, let’s assume for a moment that most devs are utilizing decentralized infrastructure to build & launch the AI agents. Now, let’s dig deeper into the challenges within decentralized infrastructure.

Below are a few factors that can be controlled by the devs or by the host machine. If any one of them is compromised, these agents will not remain autonomous & lose their financial autonomy.

Model and Logic Control:

• Updates and modifications to the agent’s behavior can be pushed without requiring consensus
• No separation exists between the agent’s decision-making capabilities and the developer’s control mechanisms
• The agent’s learning and adaptation remain constrained by centralized parameters

Financial Control:

• Private keys for the agent’s wallet typically reside on the host machine, which not only the host but also devs can access, given the design of the agent launchpad.
• The developer or operator maintains ultimate control over financial transactions.
• No actual separation of financial autonomy exists.

This centralization problem presents a clear need for new architectures that can provide:

• True separation of control
• Autonomous decision-making capabilities
• Secure key management without centralized vulnerabilities
• Independent resource allocation mechanisms

The next evolution in AI agent architecture must address these fundamental limitations while maintaining operational efficiency and security. This is where new approaches like swarm intelligence, TEEs,and distributed consensus mechanisms become crucial.

The TEE Promise and Its Limitations

Trusted Execution Environments (TEEs) emerged as a promising solution to the autonomy-security paradox in AI agent deployment. TEEs offer what appears to be an ideal compromise: the ability to run sensitive computations and store private keys in an isolated environment while maintaining the convenience of cloud deployment. Major cloud providers like AWS with Nitro Enclaves and Azure with Confidential Computing in addition to decentralised counterparties have invested heavily in this technology, signaling its importance in the evolution of secure computation.

At first glance, TEEs appear to address the fundamental challenges of autonomous agent deployment. They provide hardware-level isolation for sensitive operations, protecting private keys and confidential data from unauthorized access. The enclave environment ensures that even if the host system is compromised, the integrity of the agent’s core operations remains intact. This security model has made TEEs particularly attractive for applications in DeFi and algorithmic trading, where transaction privacy and key security are paramount.

However, the promise of TEEs comes with significant practical limitations that become increasingly apparent at scale. The first major constraint lies in hardware availability & cost. Current TEE implementations for LLMs require specific hardware configurations, primarily newer generation GPUs like NVIDIA’s H100s or specialized processors with built-in security features. This requirement creates an immediate bottleneck in deployment options, as these hardware components are both scarce and in high demand.

The scarcity of TEE-capable hardware leads directly to the second major limitation: cost. Cloud providers offering TEE-enabled instances typically charge premium rates for these resources. For instance, running a basic autonomous agent on TEE-enabled infrastructure can cost between $1 to $3 per hour, significantly higher than standard compute resources. This cost structure makes TEE deployment prohibitively expensive for many applications, particularly those requiring continuous operation or significant computational resources.

Beyond the immediate concerns of hardware availability and cost, TEEs introduce operational complexities that can impact an agent’s effectiveness. The isolated nature of the TEE environment, while crucial for security, can create performance overhead due to the additional encryption and decryption operations required for data movement in and out of the enclave. This overhead becomes particularly significant in applications requiring high-frequency operations or real-time data processing.

The scalability challenges of TEE-based systems become even more pronounced when considering the broader ecosystem of autonomous agents. As the number of agents increases, the limited pool of TEE-capable hardware creates a natural ceiling on system growth. This limitation directly conflicts with the vision of a truly scalable, decentralized network of autonomous agents that can grow organically based on market demands rather than hardware constraints.

Moreover, while TEEs excel at protecting private keys and ensuring computational privacy, they don’t fundamentally solve the autonomy problem. The agent still requires trust in the TEE provider and the hardware manufacturer. This trust requirement creates a different form of centralization, shifting the point of control rather than eliminating it entirely.

For applications focused on public data and transparent operations - which constitute the majority of blockchain and DeFi use cases - the overhead and complexity of TEE implementation may be unnecessary. In these scenarios, the cost and complexity of TEE deployment need to be carefully weighed against the actual security benefits provided, particularly when alternative approaches to securing agent operations exist.

After extensive analysis of current AI agent architectures, we confront three interlinked challenges that form the core of the autonomy problem: the autonomy trilemma, the private key dilemma, and the creator’s control paradox.

After examining the limitations of both centralized deployments and TEE implementations, we arrive at the core challenge facing autonomous AI agents today:

achieving true independence while maintaining security and operational efficiency.

Perhaps the most insidious challenge in current agent architectures is what we term the “creator’s control paradox.” This paradox manifests in the inherent power imbalance between an agent and its creator. Even in systems designed for autonomy, the creator typically retains significant control through various mechanisms.

This control structure creates a fundamental contradiction: how can an agent be truly autonomous while remaining under the ultimate control of its creator? The paradox extends to economic relationships as well. Creators often maintain control over an agent’s financial resources, either directly through key management or indirectly through infrastructure control.

The centralized model fails because it never truly relinquishes control, maintaining various backdoors and overriding mechanisms that compromise true autonomy.TEE-based solutions, while promising in theory, introduce new forms of centralization through hardware dependencies and operational constraints. They solve the immediate security concerns but fail to address the broader autonomy requirements and face significant scalability challenges.

The root cause of these failures lies in attempting to solve the autonomy problem while maintaining traditional control structures. This approach inevitably produces systems that are autonomous in name but controlled in practice. As we move forward in developing truly autonomous AI agents, we must fundamentally rethink not just how we secure these agents but how we structure their entire operational framework.

We need to explore new paradigms in autonomous agent architecture - approaches that can potentially resolve these fundamental tensions and enable genuine agent autonomy while maintaining necessary security guarantees and operational efficiency.

Skynet: Redefining Agent Autonomy

Skynet introduces a new approach to autonomous AI agents that fundamentally reimagines how we achieve true autonomy while maintaining security. Rather than attempting to solve the autonomy trilemma through traditional means, Skynet employs a novel architecture based on swarm intelligence and distributed consensus.

At its core, Skynet’s innovation lies in completely separating the agent’s decision-making capabilities from its resource control. Unlike traditional architectures, where an agent directly controls its resources through private keys, Skynet introduces a layer of Guardian Nodes that collectively manage and protect the agent’s assets through smart contract escrows.

This architectural shift addresses the fundamental challenges we identified earlier:

The Creator Paradox Solution:

Instead of giving the creator or the agent direct control over resources, Skynet implements a proposal-based system where the agent’s actions must be validated by a network of independent Guardian Nodes. This effectively eliminates the creator’s ability to exert direct control while maintaining robust security measures.

Private Key Protection

Rather than relying on centralized storage or expensive TEE solutions, Skynet moves the critical assets into smart contract escrows. The agent’s operational wallet holds minimal funds, with the majority of resources secured in escrow contracts that can only be accessed through multi-node consensus.

The heart of Skynet’s innovation is its proposal system. When an agent needs to perform any significant action - whether it’s procuring compute resources, executing trades, or managing assets - it creates a proposal that must be independently verified by Guardian Nodes. These nodes operate autonomously, analyzing each proposal based on predefined parameters and the agent’s historical behavior.

Technical Implementation

Skynet’s technical architecture revolves around three core components that work in harmony to enable true agent autonomy while maintaining robust security:

The first breakthrough comes from Skynet’s approach to resource management. Rather than giving agents direct control over their assets, all significant resources are held in specialized smart contract escrows. These escrows are designed with no direct withdrawal functionality, making them immune to private key compromises. The only way to utilize resources is through the proposal system, which requires multi-node consensus from Guardian Nodes.

Guardian Nodes serve as independent validators, each running their own instance of validation logic. When an agent needs to perform an action - whether it’s leasing compute power, executing a trade, or updating its operational parameters - it creates an encrypted proposal that includes:

• Action specifications
• Required resources
• Expected outcomes
• Execution timeframes

The encryption of proposals serves a dual purpose. First, it prevents front-running and MEV attacks by keeping the agent’s intentions private until consensus is reached. Second, it ensures that only authorized Guardian Nodes can evaluate the proposals, maintaining the integrity of the validation process.

What makes Skynet’s approach particularly innovative is its handling of compute resources. Instead of relying on centralized servers, agents can autonomously procure compute power through the Spheron network. The process works as follows:

1. The agent identifies its compute requirements
2. It creates a proposal for resource allocation
3. Guardian Nodes validate the request based on Available escrow funds, Historical usage patterns, Network conditions
4. Upon approval, the escrow contract automatically handles payment
5. The agent gains access to decentralized compute resources

This system completely eliminates the need for centralized control while maintaining robust security guarantees. Even if an agent’s operational wallet is compromised, the attacker can only submit proposals - they cannot directly access the escrow funds or override the Guardian Node consensus.

The Guardian Node system itself employs sophisticated validation mechanisms that go beyond simple majority voting. Each node maintains a state history of the agent’s actions and analyzes proposals within the context of:

• Historical behavior patterns
• Resource utilization metrics
• Network security conditions
• Economic parameters

This contextual validation ensures that approved actions align with the agent’s established patterns and objectives, providing an additional layer of security against potential attacks or malfunctions.

What truly sets Skynet apart is its evolutionary approach to agent autonomy. Unlike traditional static systems, Skynet agents can evolve, breed, and create new generations of agents, each potentially more sophisticated than its predecessors. This evolutionary capability is built on a robust economic model that ensures long-term sustainability and continuous improvement.

The economic architecture is structured around three primary reserves:

1. Operational Reserve : Maintains day-to-day operations, including compute resources and network interactions. This reserve ensures the agent can consistently access necessary resources through the Spheron network.
2. Breeding Reserve : Enables the creation of new agents through a breeding mechanism. When agents breed, they combine their traits and characteristics, potentially creating more advanced offspring.
3. Fair launch through Bonding Curve : Functions as the primary economic engine, with tokens available through a bonding curve mechanism. This creates a sustainable economic model where token value correlates with network utility.

The breeding mechanism introduces a fascinating element of evolution to the network. Agents can breed with compatible partners, creating offspring that inherit traits from both parents. This process is governed by smart contracts and requires consensus from Guardian Nodes, ensuring that breeding serves the network’s broader interests.

The evolutionary process works through several key mechanisms:

• Trait Inheritance: Child agents inherit traits from both parents
• Genetic Diversity: Different agent families maintain distinct characteristics
• Natural Selection: More successful traits propagate through the network
• Generation Progression: Each new generation can introduce improvements

The system’s sustainability is reinforced by its incentive structure:

• Guardian Nodes receive rewards for maintaining network security
• Successful breeding proposals earn rewards for initiators
• Token holders benefit from network growth and evolution
• Compute providers earn through resource provision

This combination of evolutionary capability, economic sustainability, and decentralized security creates a self-improving network of truly autonomous agents. The system can adapt and evolve without central control while maintaining robust security through its Guardian Node network.

By reimagining both the technical and economic aspects of agent autonomy, Skynet resolves the fundamental challenges that have limited previous approaches. It achieves this while creating a framework for continuous improvement and adaptation, setting the stage for a new era of truly autonomous AI agents.

Disclaimer:

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