From: aidotengineer
AI agents, particularly personal ones, hold significant potential for augmenting daily life by taking actions on a user’s behalf and leveraging extensive life context [00:02:40]. However, there are notable challenges in their development, especially when aiming for local and private operation.
What is an AI Agent?
An AI agent is defined as something that can take action in the world [00:03:05]. Unlike an aggregator, which only gathers context and processes information, an agent possesses “agency” and can perform actions [00:03:05]. For instance, a news aggregator like Swix’s AI news is not an agent because it cannot act [00:03:01].
A highly intelligent agent becomes largely useless without the appropriate context [00:03:21]. If an agent lacks access to relevant information sources (e.g., iMessage for prescription renewals [00:03:32], Venmo for financial transactions [00:04:08]), its responses can be unreliable and irritating, making it difficult for users to trust its utility [00:04:17].
Why Local and Private Agents?
There are several compelling reasons to advocate for local and private personal AI agents over cloud-based services:
- Control and Trust: Traditional digital services like cloud email operate on simple, predictable mental models (e.g., “email in, reply out”) [00:08:01], fostering trust. However, AI agents have a powerful and often unpredictable action space [00:08:55]. Users may become uncomfortable if an agent can take actions on their behalf without full control, such as auto-replying to a boss inappropriately [00:08:31]. Furthermore, online services may prioritize monetization, potentially leading agents to make biased decisions (e.g., recommending products that offer kickbacks) [00:09:09]. Given the intimate nature of personal data, maintaining control is crucial [00:09:28].
- Decentralization: Centralized ecosystems often create “walled gardens” that limit interoperability [00:09:55]. Relying on one ecosystem for a personal agent that performs diverse actions in daily life could lead to undesirable dependencies and restrictions [00:10:04].
- Privacy (“Thought Crimes”): A personal agent intimately augments a user, potentially handling queries or thoughts that would never be vocalized publicly [00:11:02]. Cloud providers, even with enterprise-grade contracts, might be legally mandated to perform logging and safety checks [00:11:36]. To avoid any risk of being “punished for thought crimes,” keeping personal agents local is a powerful argument [00:11:50].
Technical Challenges in AI Agent Development
Despite the strong arguments for local agents, building effective AI agents presents several technical hurdles:
- Local Model Inference Speed and Limitations: While open-source projects like LM Studio and MLC LLM (built on PyTorch) are available for running local models [00:12:35], local model inference is currently slow and limited compared to cloud services [00:13:13]. Although 20-billion parameter or distilled models can run quickly [00:13:33], the latest, full, unquantized models remain very slow [00:13:41]. This area is expected to improve rapidly, but users may not always be able to run the absolute latest and greatest models locally [00:13:49].
- Multimodal Model Capabilities:
- Computer Use: Open multimodal models are not yet great for computer use, and even closed models (APIs) frequently break [00:14:20].
- Taste Identification: Models struggle with specific and fine-grained user tastes, especially in areas like shopping [00:15:00]. They often provide generic recommendations and are not effective at visually identifying specific requests, relying more on text matching [00:15:26].
- Catastrophic Action Classifiers: A significant challenge is the lack of robust catastrophic action classifiers [00:15:37]. While many agent actions are reversible or harmless (e.g., navigating to the wrong Wikipedia link) [00:15:51], some can be catastrophic (e.g., purchasing a Tesla instead of Tide Pods) [00:16:10]. More research is needed to enable agents to reliably identify and notify users before taking such irreversible actions [00:16:28].
- Open-Source Voice Mode: The state of open-source voice mode for local agents is currently rudimentary [00:16:52]. For a truly personal agent, voice interaction is essential, but the technology is not yet mature enough [00:16:56].
Optimism for the Future
Despite these challenges, there is optimism regarding the future of personal local agents, primarily due to the rapid advancement of open models [00:17:07]. Open models are demonstrating faster intelligence compounding compared to closed models because development efforts are coordinated across a broader community [00:17:11]. This collaborative approach, seen in projects like Linux, PyTorch, and models such as Llama, Mistral, and Grok, often leads to unprecedented wins once a critical mass of coordination is achieved [00:18:09]. It is anticipated that open models will eventually surpass closed models in performance per dollar of investment [00:18:22].
Projects like Galactica and PyTorch are actively working on plugging the reasoning gap between open and closed models [00:19:14] and enabling local agents by addressing the technical challenges [00:19:30].