From: aidotengineer
Effective AI agents, particularly personal ones, necessitate comprehensive “life context” to be truly valuable [02:46]. Without the appropriate context, a highly intelligent agent is considered useless, akin to a “bag of rocks” [03:21].
Defining an Agent and the Role of Context
An agent is characterized as something with “agency” that possesses the capability to “act in the world” [03:05]. Any system that can only gather information without the ability to take action is not classified as an agent [03:12].
“A highly intelligent agent without the right context is as good as a bag of rocks, it’s like really useless.” [03:21]
The Impact of Insufficient Context
When a personal agent operates with incomplete context, it risks providing inaccurate information, leading to user frustration and a breakdown of trust [04:17].
Examples of Contextual Failure:
- Prescription Renewal: An agent might falsely report that a prescription has not been renewed because its access is limited to Gmail, WhatsApp, and calendar, while the actual confirmation was received via iMessage from CVS, a source it cannot access [03:30].
- Financial Information: An agent reporting on a bank account balance could be incorrect if funds arrived via Venmo and the agent only has access to a traditional bank account [04:08].
These scenarios underscore that even an agent performing its best with limited information remains unhelpful if it cannot reliably provide accurate details due to missing context [03:55]. An agent becomes genuinely useful only when it reaches a certain level of reliability and predictability [04:33].
Strategies for Providing Context to AI Agents
To ensure an AI agent has the necessary context, one might ideally envision it seeing everything a user sees and hearing everything they hear [05:14]. However, this is currently impractical due to limitations like battery life in wearable devices [05:31].
Running an agent on a phone to monitor screen activity and background processes seems plausible, as much of a user’s life context resides on their phone [05:43]. However, mobile ecosystems, such as Apple, impose significant restrictions on asynchronous background processes, limiting this approach [06:01].
A more feasible current solution involves utilizing a Mac Mini placed in the user’s home [06:23]. This device can run agents asynchronously without battery concerns, allow logging into all personal services, and access Android ecosystems, which are more open [06:36]. This approach enables the comprehensive context gathering crucial for effective AI agents.
Why Local and Private Agents are Preferred for Personal Context
For personal agents that deeply integrate into a user’s life, keeping them local and private is crucial due to several factors:
- Trust and Predictability: Unlike simple digital services like email, whose behavior is predictable (email in, reply out), AI agents have a much broader and more unpredictable action space [08:01]. Users are uncomfortable with services that can take powerful actions on their behalf without full understanding or control [08:51].
- Monetization Concerns: Cloud-based agents could potentially be monetized in ways that conflict with user interests, such as prioritizing purchases from partners who offer kickbacks [09:09]. Local agents ensure the user maintains control over such decisions [09:33].
- Decentralization and Ecosystem Lock-in: Relying on a single ecosystem for a personal agent could lead to “walled gardens” and interoperability issues, similar to existing digital services like maps and email [09:53]. A decentralized approach through local agents avoids this lock-in for critical personal functions [10:17].
- Privacy and “Thought Crimes”: Users might ask personal agents questions or explore thoughts they would never voice aloud [11:07]. Cloud providers, even with enterprise-grade contracts, are subject to legally mandated logging and safety checks, posing a risk of “persecution for thought crimes” [11:36]. Local agents mitigate this risk by keeping sensitive interactions private [11:47].
Current Challenges in Providing Context to AI Agents
While the technical and infrastructural challenges in AI agent development for local agents are rapidly improving, specific issues persist:
- Local Model Inference Speed: Running local models, a key component of agents, is currently slower and more limited compared to cloud services, even on powerful machines [13:13]. Although this is changing with smaller, distilled models, the latest unquantized models remain very slow [13:38].
- Multimodal Model Quality: Open multimodal models are not yet great, particularly for computer use, often breaking [14:20]. They also struggle with understanding specific user tastes in shopping queries, relying more on text matching than visual identification [14:58].
- Catastrophic Action Classifiers: A significant gap exists in the ability of agents to identify “catastrophic actions” before taking them [15:37]. While many actions are reversible or harmless, some, like an unintended purchase, are not [15:48]. More research is needed to improve agent reliability in notifying users before executing such critical actions [16:25]. This directly impacts challenges in creating effective AI agents.
- Voice Mode: Open-source voice mode for local agents is still underdeveloped, yet crucial for intuitive interaction with personal agents [16:52].
Despite these challenges in AI agent development, optimism remains high for improving AI agent task execution. Open models are rapidly compounding intelligence faster than closed models due to coordinated community effort [17:11]. This parallels the success seen in other open-source projects like Linux [18:17]. The PyTorch project, for instance, is actively working on enabling local agents, addressing many of the technical hurdles [19:27].