Ollama Ecosystem Now Supports Hermes AI Agents for Continuous Self-Iteration of Local AI Agents
The Ollama ecosystem has begun supporting Hermes AI Agents, which can operate on local devices and possess the ability to continuously execute tasks and self-improve. The goal is to achieve a "long-term online" automated agent system without reliance on the cloud. Core features include local model invocation, task loop execution, and behavior optimization based on historical feedback.
This development continues the trend in the open-source community promoting a "local AI stack." Ollama has previously become an important tool for running large local models, while Hermes adds the "agent layer" on top, enabling AI to not only respond to commands but also proactively plan and execute complex tasks. Relevant developers and tech blogs point out that local agents combined with open-source models (such as the Llama series) are lowering the barriers for businesses and individuals to build automated systems.
Meanwhile, discussions in the English tech community are focused on two directions: one is the potential impact of the "free + local" model on cloud API business models; the other is the challenges of long-running agents in terms of security and resource management, including permission control and error accumulation issues.
Source: Public Information
ABAB AI Insight
This information's key point is not "another AI tool," but rather that AI agents are beginning to move away from cloud platforms towards "personal computing sovereignty." In the past, AI capabilities were highly centralized among cloud vendors due to the high costs of model training and inference; however, the combination of Ollama and Hermes essentially separates the "execution layer AI" from centralized infrastructure using open-source models and local inference.
This will change the value distribution in the AI industry. Cloud vendors' advantages lie in models and computing power, while local agents' advantages lie in data control and long-term operation. Once agents need continuous access to local files, accounts, and workflows, local deployment becomes more efficient and privacy-friendly. This means that some AI use cases (especially personal productivity and automation for small and medium enterprises) may not fully rely on API billing systems.
From a technical perspective, "self-improvement" does not mean the model itself is being trained, but rather that the agent is optimizing at the strategy level: recording history, adjusting prompts, and improving task breakdowns. This essentially overlays a "lightweight decision system" on top of the model, representing engineering capability rather than a breakthrough in the foundational model. This also explains why the open-source community can advance quickly—the barrier is not in training but in orchestration.
On a deeper level, this represents a shift of AI from a "request-response tool" to a "perpetually existing digital workforce." When agents can operate 24/7 and gradually optimize their behavior, they begin to possess attributes similar to a "position" rather than just "software functionality." The impact on future labor structures lies not in single efficiency improvements but in whether they can long-term replace repetitive knowledge work and gradually take over process control.