I've been exploring ways to automate the collection and summarization of industry news for internal data teams, moving beyond manual RSS feeds or paid aggregators. My usual stack involves scheduled queries and dbt models, but the "fetch and summarize" step was always a glue-code headache.
The You.com Agent API caught my eye for this specific task. Here's the step-by-step workflow I built and have been running for a few weeks:
* **Agent Creation:** I set up a dedicated agent via the API, instructing it to focus on data engineering, analytics engineering, and data platform news from reputable sources. The key was crafting a precise system prompt to avoid generic tech news.
* **Orchestration:** A lightweight Python script, invoked by a scheduled Airflow DAG, calls the agent daily. It passes a prompt requesting a digest of the last 24 hours.
* **Data Pipeline Integration:** The script writes the agent's output (structured as a JSON) to a cloud storage bucket.
* **Loading & Transformation:** A separate process loads this JSON into a BigQuery raw table. From there, a dbt model parses the response into a fact table (topics, summaries, sources) and a date dimension for easy tracking.
* **Delivery:** A Looker studio dashboard picks up the transformed data, giving the team a scrollable digest.
The results are promising for a firehose of information. The quality is highly dependent on the initial agent prompt—it took a few iterations to nail down. I'm also experimenting with feeding the summaries into a vector database for a semantic search layer over time.
Has anyone else used this API for similar automated research? I'm particularly curious about:
* Strategies for improving source consistency.
* Handling rate limits in bulk operations.
* Whether you've found success in piping the output directly into other apps (like Slack) versus a warehouse-centric approach like mine.