Agent runtime platform for enterprise AI workflows with compliance and observability
A self-hosted, managed agent execution layer purpose-built for regulated enterprises and mid-market software teams that need to run autonomous AI workflows 24/7 with audit trails, error recovery, and multi-tenant isolation.
The problem
Developers and enterprises building AI agents face fragmentation across open-source frameworks (LangGraph, CrewAI, Vercel), lack of production-grade observability, no built-in compliance/audit logging for regulated industries, weak error recovery, and no standardized way to host and manage agent lifecycle across teams without vendor lock-in.
Who has it: Mid-market software product teams (50-500 engineers) and regulated enterprises (healthcare, finance, legal) building multi-agent AI workflows that require self-hosting, compliance logging, and 24/7 reliability.
Why now: Enterprise adoption of AI agents is accelerating; LLM reliability remains variable; regulation (SOX, HIPAA, GDPR) now demands audit trails and reproducibility; open-source agent frameworks are maturing but lack production operations layers; mid-market software companies are building agents but lack DevOps expertise.
Where this came from
2 public sources behind this idea.
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