Free Your Time: Schedule AI Agents with Cron & Serverless Functions

AI agents are immensely powerful across various domains and use cases but how do we get more out of them?

You can create scheduled AI agent workflows to automate tasks such as:

• Automated Research Tasks — Set up AI agents to run recurring workflows, such as daily financial analysis or scanning platforms like Google Scholar and arXiv for the latest papers. These agents can then generate summaries or structured reports based on the findings.
• Website Maintenance — Schedule AI agents to regularly monitor site performance, including keyword rankings and SEO metrics, then process and transform that data into actionable insights.
• Sentiment Analysis Over Time — Deploy agents to continuously track and analyze social media activity across chosen topics or trends, generating periodic summaries that reflect shifts in public sentiment.

In this article, we take you through the process of creating AI agents and scheduling them to work daily, weekly or even monthly depending on your use case. We will also offer some tips on how to best optimize the agents, key considerations when deploying them, what platform to deploy them on and so much more.

Step 1: Creating the AI Agent

First decide, what you want your AI agent to do. The list we provided above are just some general ideas and so feel free to explore your current workflows and see how they could be possibly enhanced by using AI agents.

Our example use case of automating and scheduling an AI agent workflow involves using bots to scrape Twitter trending hash tags and then turning them into artwork ideas for the TEV2, our AI image generator.

A key consideration is having somewhere to store the output of the agent so the work is not effectively wasted compute. This can be in vector database in which the text generated by the agent workflow is immediately vectorized and ingested into a vector database like Pincone, ChromaDB or FAISS.

In our case, we store the artwork ideas in a Neo4J graph database to allow us to map out semantic and thematic relationships between the artwork ideas.

Step 2: Choose Your Deployment Type

Once your AI agent is tested and works exactly how you expect to, you can look ahead to figuring how where and how you will deploy it.

You have option to deploy locally and schedule its workflow on your computer. On Windows, you can use Task Scheduler to run your AI agent at specified intervals.

Alternatively, you can deploy it via a cloud service such as Azure, AWS, or even serverless deployment platforms like Vercel or Koyeb.

How you deploy depends very much on what you intend to use the agent for, your budget and even your time flexibility. With locally scheduled AI agent workflows, you are responsible for ensuring your computer remains on during runtime. You also have to ensure that your computer has the resources to run the AI agent workflow. Assuming testing went well, this would be a forgone understanding.

With cloud and serverless, you can leverage third-party compute to have your AI agent work more flexibly and with less downtime. You do have to pay and the type of payment will depend on the type of deployment you go for. If your AI agent requires a GPU as part of its workflow, then you will have to pay for this which can be per hour or inference-based.

In our case, we choose to go for a serverless deployment via Koyeb which offers great pricing as well as free deployment in various regions.

Step 3: Deploy your AI Agent

What is “cron”?

“cron" is a time-based job scheduler in unix-like operating systems. You will have to configure the schedule including the days, frequency and specific times. Depending on your case, you might want to schedule a run at 9AM which might be the time at which your agent needs access to refreshed data from a stock pricing index or from a social media platform.

Configuring your Deployment with CRON

Koyeb allows you to define a worker service with a CRON schedule.

Once it’s deployed without any errors, you simply await your runs and that is it.

This is just an example of how you might do it with Koyeb or Vercel but if your cloud savvy, the same approach might be suitable on Azure or AWS. To conclude, here is a list of planning and scheduling tips:

🧠 AI Agent Workflow Planning

• Define a modular use case with clear inputs and outputs (e.g. trend → insight → prompt).
• Map logic visually including triggers, fallback paths, and output destinations (like graph DBs).
• Craft outputs semantically if feeding into downstream prompts or creative tasks.
• Add lightweight logging for traceability and drift detection.

⏱️ Scheduling AI Workflows

• Choose deployment wisely:
• Local = free but tied to uptime.
• Cloud/serverless (e.g. Koyeb, Vercel) = scalable, but metered.
• Use cron expressions to schedule intelligently (e.g. align with data freshness).
• Store outputs smartly for reuse (e.g. vector DBs or Neo4J for semantic querying).
• Balance cost vs. compute needs, especially with GPU-hungry tasks.

If you found this article useful, check out our website for other insightful pieces on generative AI, art and content creation.