"We've been impressed by how lightweight and flexible DBOS is, the speed at which their team ships, and the level of support offered. We are excited to scale with DBOS." - Abhishek Das, Co-founder and Co-CEO, Yutori
Yutori.ai is building an AI-powered "chief of staff" to handle everyday tasks on the web for people. Their first product is "Scouts," which monitors the web based on user interests.
Yutori.ai Scouts are always-on, autonomous AI agents that monitor the web for anything you care about. Simply tell Scouts what you're looking to track (e.g., flights to Tokyo under $900 in August), and a team of agents will get deployed to monitor and notify you when there’s a relevant update on the web.
Yutori has built their stack to be agent-first, from training their own models to generative product interfaces. For durable execution, hosting, and auto-scaling of their agentic AI system, they chose to build and run on DBOS.
Yutori wanted to build an AI agent for scale. Making scouts reliable at scale is challenging for a number of reasons. One is that workflows are dynamically generated by the agent based on results from LLM calls, which are not known ahead of time and can change with each execution, since LLMs aren’t deterministic. Second, those LLM calls are also unreliable, and can sometimes return nonsensical results, or no results at all. Also, each Scout runs complex workflows with many subtasks scheduled across multiple processes. And once all of that was done, the user has to be notified exactly once – failing to notify them at all would be a product failure, and notifying them more than once would annoy the user and look unprofessional.
Additionally, Scouts conduct their work on a scheduled basis, requiring large compute resources every hour, and much less in between. Sudden auto-scaling from very few to very many VMs, and then back down was important, along with the requirement to execute Scout tasks reliably, without interruption. Their vision is to “have a very flexible agent framework that can pause and resume at any time”.
DBOS addresses all of these challenges, as you will see below.
Yutori initially considered using a separate, dedicated workflow orchestration service (e.g., Temporal, or AWS Step Functions) to execute Scout workflows, but that approach increased cost and architectural complexity, especially at scale. They sought a solution that introduced less overhead into the system and worked more seamlessly with their backend code.
With DBOS they could just install the library and annotate their agents’ workflows and steps, no rewriting/rearchitecting existing code required, and no defining their workflows ahead of time, which is impossible for them since their workflows change based on LLM responses.
DBOS only has a single dependency – Postgres. They were already using Postgres and are familiar with operating it, so this was an easy choice for Yutori. It also means that no external server is required to manage workflows, a key factor in their decision, because their reliability is not tied to another server or service.
As a startup launching an end-user AI application to the public, Yutori sought a way to get to market quickly, and also scale up usage immediately. They also required durable execution in order to reliably automate tasks on behalf of their users. As a result, Yutori’s backend required:
Using DBOS workflows was a powerful abstraction for Yutori. With just standard Python functions DBOS provides dynamic control logic and observability into what scouts are doing and their execution paths. It allows them to walk through its checkpointed history to see why something failed and if it properly recovered. Durable workflows can also automatically recover from failures using checkpointed state, making it easier to handle flaky APIs, slow LLMs, outages, or anything else that may stop or slow down an agentic workflow.
Searching for simpler alternatives led Yutori to DBOS. The unique library-based approach of open source DBOS Transact gave them the durability, queuing, and observability they required, without having to host and run a separate orchestration server, and without having to implement workflow logic separate from the rest of their backend code.
In addition to that, hosting and running their backend code on DBOS Cloud met their performance requirements. DBOS Cloud’s serverless auto-scaling provided a simpler alternative to deploying on AWS Lambda, and with much better price-performance, since DBOS cloud only charges you when you’re doing work, not when your agent is idle (or waiting for an LLM).
The switch to DBOS was very successful for Yutori. The switch simplified their architecture and put them on a fast path to launch, which occurred within weeks of the switch.
If you’d like to launch your own Yutori Scouts, visit Yutori.ai.
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