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Z Potentials | Exclusive Interview with ACTAVA's Co-founder, a Former Engineering Lead at Salesforce AI Research Turned Founder: CHI Bench Exposes the Biggest Weakness of Healthcare AI Agents

Over the past year, the narrative around AI agents has shifted from "can it do a demo?" to "can it go into production?" Coding agents were the first to break out, because they have clearly defined tasks, strong feedback signals, and verifiable results. But in more complex enterprise scenarios, agents still run up against a core problem: the fact that an agent can answer a question doesn't mean it can get the work done. Healthcare may be the industry where this problem is most concentrated. On one hand, U.S. healthcare workflows are extremely standardized — full of policy, compliance, approvals, documentation, and cross-system coordination. On the other hand, they're also extremely complex: between the three parties of provider, payer, and patient, there are a great many processes that are long-chain, irreversible, and heavily regulated. What this means is that the real problem a healthcare agent has to solve isn't "medical Q&A" — it's whether it can execute tasks over the long term, reliably, and compliantly within a real workflow.