We've been running enterprise AI agent deployments with banking, healthcare, and manufacturing clients for the past 6 months. Across all of them, 3 failure modes come up again and again.
Sharing this from our experience building and running AI Hive our enterprise AI agent platform as well as the broader deployments we've supported
1. Scope creep from the AI itself
Agents start with a narrow, well-defined task. Within 6 weeks, stakeholders have added 3 more use cases, expanded the data sources, and the original scope is unrecognizable. Nobody planned for this. Governance frameworks that define scope formally before deployment prevent this but most teams skip it.
Thanks so much for this detailed review, Darius β and for the honest feedback on onboarding docs. You're absolutely right that non-technical teams need clearer pathways to understand how agents connect.
The case study library is also on our roadmap. Would love to feature your team's deployment as one of the first if you're open to it.
Thank you again for being one of our earliest users and for taking the time. It genuinely helps the team.