We built an AI agent that runs in the cloud, on a schedule, and hands you the finished file
We built an AI agent that runs in the cloud, on a schedule, and hands you the finished file A few months ago we sat down and asked a question that turned out to be more awkward than it looked: what does an AI product look like if you assume the user is not sitting in front of it ? Almost everything in this category is built around a conversation. You open a tab, you type, it answers, you close…
We developed an AI agent that operates in the cloud, on a predetermined schedule, and delivers the completed file directly to the user, regardless of whether they were present to witness the process. A few months ago, our team pondered the concept of an AI product when the user is not actively engaged. Most products in this category revolve around a conversational interface, where the user types a query and receives an immediate response.
However, this design has a hidden limitation: the AI's knowledge is ephemeral and disappears once the user closes the session. To address this, we constructed the agent in a reverse manner, allowing it to operate autonomously on our servers, produce the output, and deliver it to the user via email, irrespective of their presence.
This write-up details the architectural decisions required for this "always-on" agent, emphasizing the inherent challenges of maintaining an always-active AI system. The initial idea was to build a chat application with a scheduled cron job. However, this approach quickly proved ineffective because chat sessions are ephemeral, limited to the duration of user interaction, and lack persistence beyond the current session.
The team recognized that the AI's work must survive the user's absence, last for extended periods, and provide a tangible output that the user can directly access and utilize. Consequently, the design shifted from a conversational product to a task executor that accepts natural language instructions and generates a file as its primary output.
By treating the output as a file instead of a chat response, the system gained a clear end state and the ability to measure progress. Scheduling the AI's activities became a fundamental aspect of the product, with time zones and user-specific schedules becoming crucial considerations. The system was designed to be robust, with a focus on idempotency and reliable execution, ensuring that recurring tasks run exactly as specified, without ambiguity or unexpected delays.
Moreover, the AI agent was developed with a strong emphasis on personalization, learning and retaining individual user preferences over time. This approach moves the focus away from generic model improvements and towards creating a unique cognitive framework tailored to each user, enhancing the AI's utility and relevance in their daily workflow.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
