Trust, Agents, And The Future Of Work

The excitement around agentic AI is loud, but the real story starts where hype meets accountability. We sit down with Eric Karofsky, CEO of Vector HX, to separate narrow task agents from agentic systems that plan, reason, and negotiate across steps—and to ask the uncomfortable question: who do you trust when your agent starts talking to someone else’s agent?

Eric walks us through the gaps leaders often miss. It’s easy to mandate AI from the top; it’s harder to redesign the workflows that make measurable impact. We explore why trust breaks down when autonomy spreads across tools, teams, and vendors, how hallucinations become career risk inside enterprises, and what guardrails look like in regulated sectors. The conversation stays grounded with two practical wins. First, a pharma literature review pipeline that shrinks from six months and $250,000 to roughly two weeks by structuring extractions, adding human checks where accuracy matters most, and instrumenting the process end to end. Second, a document discovery platform that stitches together siloed repositories with smart metadata, natural language search, and relationship mapping that surfaces parent, child, sibling, and multilingual versions of critical procedures.

If you’re wondering where to start, Eric’s advice is simple and hard: map the jobs to be done, pick a high-friction workflow with measurable outcomes, and redesign it around human needs. Save broad agentic autonomy for bounded domains with clear policies, identity, and audit trails. The best KPI for the next 12 months might be whether your core processes actually change; if you’re doing the same work the same way next year, you probably missed the point.

Ready to rethink your approach to AI beyond better emails? Listen now, subscribe for new episodes, and share this with a teammate who owns process change. Your take: where would agentic AI actually earn trust in your organization?