AI Agents Fail for 2 Reasons. Crowdsourcing Solved Both. | Julia Dalton, SVP Product (Capacity)

Long before “AI Agent” was the buzzword in every product discussion, Julia Dalton was already deep into solving the problems of coordinating thousands of worker agents, creating clear instruction prompts, and evaluating output quality.

Julia is SVP of Product at the AI-support automation platform, Capacity. Previously, she helped scale workflow orchestration at OneSpace, formerly known as Crowdsource, where thousands of freelancers executed microtasks for major retailers. As it turns out, managing humans at scale and managing AI agents have a lot more in common than most people realize.

In this episode, Julia shares:

• The two failure modes that kill every AI agent before the model ever runs
• Why running a crowdsourced work platform gave her incredible insights into building effective agentic AI products
• How Julia and her team built an AI system to triage and prioritize product feedback, automating low-complexity builds and helping make better decisions on high-stakes tradeoffs

Links

LinkedIn: https://www.linkedin.com/in/juliadalton/
Capacity: https://capacity.com/

Chapters

00:00 Intro
00:52 Julia's startup roots at OneSpace
02:00 Transitioning from the human API to AI agents
02:47 Microtasking workflow chains
06:54 Routing rules and AI orchestration
11:55 Validating prompts with simulations
15:08 Recursive prompting in practice
17:46 Data and knowledge orchestration
20:57 PRP triage app for product requests
26:56 Conclusion

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