Why AI Agents Need TEEs: Autonomy, Privacy, and Verifiability
The AI industry stands at an inflection point. As autonomous systems evolve from experimental projects to core business infrastructure, they’re reshaping modern software and automation. But as their influence grows, so do the demands for security, privacy, and trust guarantees related to the agents .
Every day, an increasing amount of money and IP-related value is entrusted to AI agents running on the rails of a bygone era — outdated centralized infrastructure that provides few if any guarantees for the builders or end-users of these agents.
In this blog, we are going to breakdown AI agents, their current infra setup, the bottlenecks, and how Fleek and TEEs improve the status quo.
Understanding AI Agents
AI agents are autonomous software programs that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI models that simply respond to inputs, agents are designed to be proactive, persistent, and capable of complex decision-making processes.
Think of an AI agent as a digital assistant with agency. These agents can:
- Process and analyze information from multiple sources
- Make autonomous decisions based on predefined rules and learning
- Interact with other systems and APIs
- Execute tasks without constant human supervision
- Adapt their behavior based on new information and experiences
For example, an AI agent might monitor cryptocurrency markets, analyze trading patterns, manage a portfolio, and execute trades automatically.
The key distinction between AI agents and traditional AI models is in their ability to act on their own without needing to be prompted every time. .
While a language model waits for queries and responds, an agent maintains state, pursues goals, and operates continuously over time.
While these capabilities make AI agents powerful tools for automation, they also introduce challenges around security, privacy, and operational integrity.
The Problem with Black Box AI Agent Compute
Running AI agents on traditional cloud infrastructure or closed-source inference platforms introduces significant risks and limitations that can’t be ignored anymore.
Here’s why this approach is becoming untenable:
- Trust and Verification Issues
When you deploy an AI agent on centralized infrastructure, the developer of the agent, but more importantly the end users of that agent, are essentially operating on blind faith. They have no guarantees that:
- The agent’s code is running as intended
- The decision-making process hasn’t been tampered with
- The outputs are genuine and haven’t been manipulated
- The agents crypto wallet and funds aren’t accessible by the developer
This “trust me” approach to compute might have been acceptable in simpler times, but as AI agents take on more critical responsibilities — from managing financial transactions to orchestrating supply chains — this lack of verifiability becomes a serious liability.
- Privacy Vulnerabilities
Traditional cloud deployments expose your AI agents to multiple attack vectors:
- Hosting providers can access and potentially exfiltrate your agent’s code and data
- System administrators have unrestricted access to runtime memory
- Network-level observers can potentially intercept agent communications
- Competitors could reverse-engineer your agent’s proprietary logic
This exposure level is particularly problematic for agents handling sensitive data or proprietary algorithms.
- The Centralization Trap
Relying on centralized infrastructure providers creates several critical issues:
- Single points of failure that can take your agents offline
- Vendor lock-in that makes migration difficult
- Exposure to arbitrary policy changes or service terminations
- Potential censorship of agent operations
- Unpredictable pricing changes and hidden costs
Over-reliance on corporate giants means developers have little control over the infrastructure and innovation. The lack of competition and transparency in centralized systems enables price gouging, which stifles smaller players.
The Need for Verifiable and Transparent Compute
As AI agents become more integral to our digital infrastructure, the need for verifiable and transparent compute isn’t just a nice-to-have but a fundamental requirement. In today’s AI landscape, we’re asking agents to handle increasingly critical tasks:
- Managing million-dollar trading portfolios
- Orchestrating supply chain operations
- Processing sensitive healthcare data
- Making real-time business decisions
- Handling personal and financial information
Yet we’re running these agents on infrastructure that inspires minimal confidence about their execution. This isn’t just risky – it’s unsustainable.
The Promise of Verifiable Compute
Verifiable compute changes this paradigm. With proper implementation, it provides:
- Cryptographic guarantees: Proof that code is executed exactly as written and verification of input data integrity.
- Transparency: Transparent compute environments are typically built on open-source infra, breaking the cycle of dependency on centralized providers.
- Accountability: Clear audit trails of agent decisions, traceable data handling, and reproducible results.
TEEs: The Secure Foundation for AI Agent Computing
Trusted Execution Environments (TEEs) are a game-changer for deploying AI agents securely and verifiably. TEEs create a secure foundation vital for autonomous AI systems by providing hardware-level isolation and cryptographic guarantees.
How TEEs Work?
- Secure enclaves: TEEs create a protected memory region within the processor, isolating computations from the rest of the system.
- Encryption at rest and in use: Data and code inside the TEE are encrypted, ensuring they remain secure even during processing.
- Remote attestation: TEEs provide cryptographic proof that a specific, trusted application is running in the environment, allowing external stakeholders to verify its integrity.
- Resistance to physical attacks: Even if the hardware is physically accessed, data inside the TEE remains protected.
Core Benefits of TEEs for AI Development
Deploying AI agents within TEEs brings many advantages that address some of the most pressing challenges in modern AI development.
Here’s how TEEs transform AI deployment:
- Confidential Computing
First, TEEs revolutionize privacy through confidential computing. This goes far beyond traditional data protection – it’s about securing the entire computation process. Even during active processing, the runtime memory remains encrypted, ensuring that inference processes run in complete security.
- Verifiable Execution
TEEs provide unprecedented verification capabilities for AI operations. Every single action your agent takes can be cryptographically proven, creating an unbreakable chain of trust. You get mathematical proof that your code executed precisely as intended, while input data integrity is verified at every step.
This verification creates a new standard of trust for AI systems.
- Secure Communication
AI agents rarely operate in isolation—interacting with APIs, databases, and other systems.
Providing end-to-end encrypted channels for these AI agents ensures that every external interaction remains secure and private. The system verifies the authenticity of all communication peers, protecting against man-in-the-middle attacks or impersonation attempts.
Fleek’s TEE Implementation: Making Secure Compute Easily Accessible with Fleek Machines
Fleek Machines takes TEE accessibility to the next level by offering lightweight TEE VMs with Docker support with best in class developer experience.
This is a game-changer for developers who want to leverage secure compute without managing complex infrastructure. You simply deploy your Docker image, and Fleek Machines handles all the TEE complexity behind the scenes. What makes this particularly powerful is its framework and chain-agnostic nature.
Whether you’re building Web2 or Web3 applications using any programming language or framework, Fleek Machines provides a consistent, secure environment for your code. The addition of Docker support means you can run virtually anything in a TEE that you can run in Docker, dramatically simplifying the integration process.
Key Benefits of Fleek’s Approach
Fleek’s implementation stands out in several crucial ways:
- Zero DevOpst: Fleek abstracts away all infrastructure concerns. There’s no hardware to provision, no complex key management to handle, and no DevOps overhead. You get auto-scaling out of the box.
- High performance: Fleek Machines run on the edge, providing global deployment options, geo-routing, and come with built-in CDN capabilities providing best in class performance. .
- Seamless DevEx: Fleek puts an extreme emphasis and focus on DevEx and provides the most seamless experience for accessing and utilizing TEE capabilities without needing to know anything about TEE’s.
What Lies Ahead for AI Agents in a TEE?
The convergence of TEEs and AI agents represents more than an infrastructure upgrade — it’s the missing foundation that will finally enable true autonomous systems at scale. Especially with solutions like Fleek abstracting away complexity while maintaining cryptographic guarantees, developers can realize the full potential of autonomous AI agents and the agentic future.
The tools and infrastructure we’re building today, like Fleek’s TEE implementation, are laying the groundwork for this autonomous future.
Ready to take your crypto AI agents to the next level? Discover how Fleek’s trustless cloud platform can power your projects.
- Explore the Docs
- Join the Community: Connect with developers and share your journey on Fleek’s forum.
- Start Building Today: Deploy your first AI agent framework on Fleek’s platform.