Most AI agents today are fundamentally broken in one important way. In other words, you forget everything. At the end of each session, context, learned behaviors, and user-specific adjustments are lost, forcing you to start over from scratch every time. This statelessness has become a quiet bottleneck in the race to build autonomous and useful on-chain assistants. According to WuBlockchain’s original report, DWF Ventures is now focused on the answer, focusing on Nous Research’s open-source Herme framework, which directly attacks the memory problem.
DWF’s memo argues that Hermes stands out because it’s more than just a one-shot automation tool. This framework introduces persistent memory that retains user interactions, sessions, and learned settings over time. This is combined with an automated skill system that organically expands the agent’s capabilities and a user profile that locks memories into a consistent identity. Self-improvement loops continually improve what an agent knows, increasing its usefulness rather than resetting it every cycle. For an industry that has flooded the market with chatbot wrappers and thin API agents, its design represents a tectonic shift toward durable composite intelligence.
Why stateless agents became the norm
Stateless architecture is cheap and easy. These are scaled by design to avoid storing sensitive user data. This makes sense for early crypto trading bots and simple Discord assistants, where raising an alert or processing a single command was enough. Lack of memory becomes an issue as AI agents start managing more complex tasks, such as interpreting DeFi positions, processing multi-step cross-chain operations, and learning from on-chain data feeds. Repetition undermines efficiency, and lack of personalization undermines trust. DWF’s framework suggests that they ignore the hype and aim for an infrastructure that not only demos well, but maintains sustained user engagement.
This push toward stateful, memory-aware agents is consistent with a broader move toward decentralized AI infrastructure. The project is starting to stitch together compute, storage, and training layers that allow AI agents to run without relying on a centralized cloud. For example, distributed computing partnerships like UXLINK and the Origins Network’s work on scalable AI-powered Web3 applications show how plumbing is being laid for agents that require persistent computing. Hermes is tackling this by relying on Nous’ decentralized Psyche training network, a layer that distributes the heavy lifting of model improvement.
Security, sealed keys, and psychedelic networks
Memory is not the only internal mechanism. Hermes separates and bakes credentials so that access tokens and private keys are not mixed with the agent’s core inference layer. Secret edits and automatic key rotation provide a security posture that is more similar to a managed system than a typical experimental bot. This architecture is important because stateful agents that hold user credentials are high-value targets. Integrating these capabilities with Psyche, a distributed training network, means that the models themselves are refined through a distributed node structure rather than a single server, which reduces central points of failure.
The storage demand for such persistent learning agents shows a remarkable trend. As models accumulate knowledge and user history, the need for cheap and verifiable storage increases. With the growing interest in AI data layers, projects like Filecoin are already joining the conversation for decentralized storage solutions tailored to AI workloads. Although Hermes cannot directly perform on-chain storage, the self-improvement loops it relies on will inevitably pull from and push to distributed environments as it scales for Web3 use cases.
When superiority is not guaranteed
DWF specifically compares Hermes to Claude Code and OpenAI Codex, arguing that the ability to generate code in the moment does not translate into increased performance over weeks of use. A stateless agent can create a perfect smart contract audit one day and forget the entire context of the project the next. What differentiates Hermès is the ability to accumulate experience. This is a genuine moat if the execution is clean, but it requires users to commit to a single long-running agent environment, something the market has been slow to tackle outside of niche financial operations.
The open source nature of Hermes works both ways. This could prompt widespread auditing and community adaptation, accelerating the adoption of DeFi tools, DAO operations, and NFT analytics. At the same time, remaining open source while maintaining a security advantage over well-funded closed source competitors is a tightrope walk. It remains unclear whether Hermes has gained enough developer mindshare to become the default anchor for stateful Web3 agents. Memory alone does not guarantee practicality if the quality of the underlying inference lags or the integration with existing wallets and dApps remains clunky. DWF’s attention is a sign that venture money is focused on architecture, not just user numbers. For teams building in the AI agent space, the Hermes Blueprint has become a reference to what’s to come after the chatbot era.
