Since OverCR v1, I Turned It Into a Full AI Orchestration Substrate
A little over a week ago I posted OverCR v1. At the time it was mostly the foundation: persistent filesystem-native state, governed orchestration concepts, workflow routing, recovery primitives, and the beginnings of a long-lived operational runtime for AI systems.
Since then, OverCR evolved fast.
v2.10 Stable is now live:
https://github.com/GuideboardLabs/overcr
What started as a runtime experiment has become a full orchestration substrate focused on long-lived, recoverable, governed AI operations.
The biggest architectural decision stayed the same: canonical state lives on disk. Not in RAM. Not hidden inside agent sessions. Not trapped in cloud state.
Workflows, traces, provenance chains, approvals, replay artifacts, memory records, rollback snapshots, execution receipts, routing decisions, and audit logs all persist independently of any single runtime process. That design decision changed everything downstream.
Since v1, OverCR gained replayable workflow DAG execution, conditional routing and escalation policies, semantic memory with provenance tracking, contradiction-aware research pipelines, controlled web ingestion with prompt injection scanning, governed execution sandboxing with rollback support, optional kernel isolation backends, operator TUI systems, replay and recovery validation tooling, deterministic branch tracing, release reproducibility tooling, and compatibility verification.
One thing I intentionally avoided was building an opaque autonomous swarm system.
OverCR is heavily biased toward governance, replayability, operator visibility, bounded execution, deterministic recovery, auditability, and filesystem portability.
The system now passes 31/31 validation suites including replay validation, soak testing, fuzz and security validation, recovery verification, release reproducibility, install validation, and semantic compatibility checks. There is still work ahead before I would call it battle-tested infrastructure. Longer soak testing, broader platform validation, deeper sandbox security review, and performance testing under operational load are still ahead.
But at this point it feels much less like an AI agent project and much more like operational infrastructure for persistent AI systems. Would genuinely love feedback from people building orchestration runtimes, persistent agent systems, governed AI tooling, workflow engines, AI infrastructure, recovery systems, or memory substrates.