Introduction
Run an AI research agent automatically every morning — easy to say, but "where and how do you launch it" turns out to be a surprisingly tricky question. Running it in the cloud feels modern, yet I deliberately chose to launch Claude Code headlessly via the local PC's Task Scheduler.
This article explains why a local PC, what headless execution means, and the tricks for running it reliably even when no one is around.
Why a Local PC Instead of the Cloud
The Real Story on Cost and Authentication
"Automation equals cloud" is a strong assumption, but once you actually build it, a local PC is often the more sensible choice.
You pay for an always-on server and must entrust each data source's credentials to the cloud. Token management and refresh flows for GA4 and Shopify are all on you to build.
Log in once on your everyday PC and each service's authentication stays live and usable. Zero extra server cost. Since it runs just a few minutes a day, the PC only needs to be powered on.
The agent runs only a few minutes each morning. Renting an always-on cloud server for that was excessive, both in cost and in operational overhead.
Access to At-Hand Data Was the Deciding Factor
The other deciding factor was direct access to data that lives at hand.
Store sales CSVs are downloaded and managed by staff in a local folder. To read those from a cloud agent, you'd have to build a separate path to upload them somewhere. Run it on the local PC and the agent reads that folder directly. Authenticated browser sessions and local config files can be used as-is, too. "Using what's already at hand, as-is" turned out to be a bigger advantage than expected.
Accepting the Trade-offs of Local Launch
Local launch has weaknesses, of course. It won't run if the PC is off, and it depends on the staff member's environment.
Divide by role
Heavy judgment and always-on processing go to the cloud business hub; the "investigate a little each morning" agent runs locally. Rather than forcing everything to one side, dividing by role was the realistic answer.
This agent is auxiliary work that "isn't fatal if it doesn't run," so accepting the trade-offs of local launch struck just the right balance.
How Headless Execution Works
What Headless Mode Means
Headless mode is a way of running where a human doesn't operate a dialogue screen; a single command drives the whole thing from start to finish. Launch Claude Code with a prompt handed to it in advance, and the agent proceeds through the instructions and exits automatically when done.
In interactive mode it waits for human input — "what next?" In headless mode it doesn't wait. That's exactly why the agent can complete its investigation autonomously in the early morning while people are still asleep.
Scheduled Launch with Task Scheduler
For the launch trigger, I use Windows Task Scheduler. Register the setting "run this command at this time every morning" once, and the OS launches it automatically from then on.
Create a script that starts Claude Code with the prompt and options specified
Set the daily run time and the script path
Save results and errors to a file so you can trace them later
From then on, the agent launches and completes unattended every morning
No special infrastructure is needed. Unattended scheduled execution is achievable with only the standard features of the PC you already use.
Logs and Preparing for Failure
Since it runs unattended, being able to confirm afterward that "it actually worked" matters.
The agent's execution results are logged whether they succeed or fail. A data source's temporary hiccup can halt an investigation midway, so it's important to accept that some days will have zero proposals. Quietly skipping and retrying the next morning is safer than forcing a retry and emitting a mistaken proposal.
Coordinating with the Cloud
Roles Divided Between Local and Cloud
This system has the local agent and the cloud business hub coordinate through an API.
Task Scheduler + Claude Code (investigation and proposal generation)
Storing proposals, admin screen, approval flow, Slack notifications
The agent handles "investigate and submit proposals," while the storing, display, approval, and notification beyond that are the cloud business hub's job. The local side focuses on investigation, and the parts needing constant access live in the cloud. This division takes the best of both.
Handling Credentials
When registering proposals to the API, the agent authenticates with a dedicated key issued to it. Each data source's credentials stay inside the local PC and are never sent to the cloud. Process at-hand data at hand, and pass only the results to the cloud — I like how simple this data flow is, from a security standpoint too.
Summary
We covered the mechanism for launching an AI agent on a schedule every morning.
- Why we chose local PC launch — sensible for cost, authentication, and access to at-hand data
- Headless execution — unattended scheduled runs with Task Scheduler plus Claude Code
- Division of roles with the cloud — local investigates; the cloud stores, approves, and notifies
What the agent actually investigates and how is covered in "Designing Research Prompts Across Multiple Data Sources," and how humans handle the proposals in "Human-in-the-Loop: Proposal → Approval → Execution." For the big picture, see the hub article "A Daily AI Research Agent."