Automatically monitor multiple RSS feeds, filter for genuinely new articles with AI, store seen items in Baserow, and deliver real-time Slack alerts.
This AI agent continuously monitors configured RSS feeds, parses article data, and keeps a centralized history in Baserow. It uses AI to distinguish truly new content from duplicates, ensuring only relevant updates are surfaced. It then stores the new articles as seen and sends structured Slack alerts with links and summaries.
Executes the end-to-end feed workflow and returns clean results.
Ingests feed URLs from Baserow to determine which sources to monitor.
Fetches the latest RSS data via HTTP requests and parses XML into JSON.
Filters candidates against a seen-articles history to identify genuinely new items.
Stores newly identified articles in Baserow to prevent duplicates in the future.
Sends rich Slack notifications with article details to a designated channel.
Logs results and handles errors for reliability and auditability.
Before you use this AI agent, you contend with noisy feeds, duplicate alerts, manual checks, scattered data, and slow notification cycles. After deployment, you get a streamlined, duplicate-free alert system that surfaces only genuinely new content, with all sources tracked in a central database and rapid Slack updates.
A simple 3-step flow that non-technical users can follow.
Manually trigger the agent to load RSS feed URLs from Baserow, then fetch and parse the RSS XML into structured JSON for processing.
Retrieve the list of previously processed articles from Baserow and compare each new item to identify true novelties.
Structure data for the AI agent, run AI-based filtering to select new content, save new items to the seen list, and post a Slack notification with details.
A realistic scenario showing inputs, actions, and outcomes.
Scenario: A marketing team monitors 6 feeds for product launches. Over 5 minutes, 4 new articles are detected by the AI filter, 3 are posted to Slack, and all 4 are saved to the seen list in Baserow for tomorrow’s comparisons.
Roles that gain precise, timely updates with minimal effort.
Needs timely industry and competitor updates to plan campaigns.
Sifts and shares only fresh articles for newsletters and digests.
Wants reproducible evidence of competitor activity without noise.
Monitors feature announcements and market signals.
Sources credible, new content for post-ready briefs.
Keeps alert channels clean and maintained with a single source of truth.
Key tools that power the AI agent’s workflow.
Stores RSS feed URLs and tracks seen articles to prevent duplicates.
Performs the novelty filtering and memory-based comparison to identify new content.
Receives structured notifications with article details for real-time updates.
Practical scenarios that maximize ROI from this AI agent.
Common questions and practical answers about using the AI agent.
No. The AI agent is designed to be configured with simple parameters: connect your Baserow tables, provide an OpenAI key, and set the Slack channel. The flow guides a non-technical user through setup and operation. If you encounter issues, the system logs events and can be adjusted by modifying the input data and prompts. Regular checks ensure the agent remains aligned with your sources and filters.
The architecture supports dozens to hundreds of feeds depending on your plan and the hosting environment. Performance scales with how you distribute fetch and parse tasks, and by tuning the AI filter to target only relevant items. In practice, most teams start with 6–12 feeds and expand as needed. If feeds change, you can update the Baserow configuration without touching the AI prompts. Monitoring larger sets should be complemented with rate limits and batch processing.
Yes. The AI filter prompts are designed to understand the difference between new content and duplicates based on the seen-list. You can adjust the specificity of what counts as relevant (e.g., topic, source, sentiment) and how strict the novelty check should be. Changes are applied to the AI agent's prompt and memory handling, with outputs validated by the parser before notification.
If filtering produces unexpected results, you can re-run processing on a subset of articles or adjust the seen-list lookup. The system logs the occurrence, allows manual re-evaluation, and can temporarily bypass AI filtering for a controlled test. Over time, prompts can be refined based on feedback to improve precision. The architecture is designed to fail safely and maintain a consistent seen-state.
All data transfers use secure channels and API authentication. Access to Baserow, OpenAI, and Slack is controlled via API keys with least-privilege permissions. Data is stored in your environment or managed databases, not publicly exposed. You can audit access logs and adjust permissions to meet your compliance requirements.
Yes. The setup is modular: you can re-point feed sources, switch the Slack channel, and modify Baserow tables without redeploying the AI agent. Changes propagate through the configuration layer and do not disrupt existing seen-article history. If you reorganize data structures, you can migrate artifacts and update references within the agent.
Yes. The agent can preserve a queue of pending items and notify you of a temporary AI outage. While AI-driven filtering is offline, the system can surface the latest unfiltered items or rely on a deterministic fallback (e.g., basic keyword matching) until OpenAI services return. You can configure alerting to ensure production notifications resume promptly once the AI component is back online.
Automatically monitor multiple RSS feeds, filter for genuinely new articles with AI, store seen items in Baserow, and deliver real-time Slack alerts.
