TickerPulse AI In Action
TickerPulse AI In Action
Most agent workflows are built on polling. Check the inbox every five minutes. Scrape the dashboard every hour. Query the API on a schedule. This works until it does not - you miss time-sensitive data because your polling interval was too long, or you waste resources because it was too short.
Let Data Come to You
Event-driven architecture flips the model. Instead of asking "is there new data?" repeatedly, you subscribe to a feed and get notified when data arrives. WebSocket connections, server-sent events, webhook receivers - the mechanism varies but the principle is the same.
For AI agents, this is transformative. An agent watching a real-time data feed can react in seconds instead of minutes. A stock price crosses a threshold and the agent acts immediately. A customer message arrives and the response begins before the next polling cycle.
The Agent Integration
Connecting an agent to real-time feeds requires a persistent connection layer. The agent maintains WebSocket connections to data sources. Incoming events get queued and prioritized. The agent processes them based on urgency and relevance.
This is where the desktop advantage matters. A local agent can maintain persistent connections without worrying about serverless timeouts or cloud function cold starts. It runs continuously on your machine, always connected, always ready to process incoming data.
When Polling Is Still Right
Real-time feeds are not always available or necessary. Many APIs only support polling. Some data sources update infrequently enough that polling every hour is fine. The decision depends on how time-sensitive the data is and what the cost of delayed reaction looks like.
The practical approach is real-time for critical feeds, polling for everything else, and a unified queue that makes both look the same to the agent's processing logic.
Fazm is an open source macOS AI agent. Open source on GitHub.