Why event-driven APIs matter for AI workflows
The latest
Nordic APIs LiveCast explores the role of asynchronous APIs in agentic AI and data science.
What happens when AI workflows need to rely on event-based triggers? Polling typical RESTful APIs is not going to cut it.
Yesterday, I had a great chat with
Hookdeck's
Phil Leggetter and
Ryan Day on the role of asynchronous APIs in AI and machine learning.
Compared to traditional request-response APIs, async patterns unlock real-time data flows, better scalability, and more efficient processing for long-running or event-driven tasks.
That's becoming increasingly important to enable more dynamic agentic AI and retrain underlying models.
That said, patterns differ quite a bit on the provider and consumer side — I was surprised to learn how much is still unsettled: authentication models, error handling, and how to communicate mid-process updates are pretty variable upon individual implementations.
Check out the full discussion (now on the
Nordic APIs YT channel) to explore where asynchronous API patterns stand today and what the future looks like. It was great to moderate this roundtable discussion!
If you're providing EDA interfaces, or developing AI systems that need to act on live data or trigger workflows, this is an area worth looking into.
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