Enoch is an agentic research control plane that treats autonomous AI work as a reliability engineering problem, not a scripting problem. It queues research ideas sourced from an upstream LLM scouting process, gates dispatch through authenticated preflight checks, supervises agent runs on local AI hardware using process-tree tracking and CPU/GPU telemetry, captures evidence into structured bundles, and generates publication-style research artifacts with provenance instead of free-floating model output. Key transitions are guarded by idempotent APIs, stale-state reconciliation, and operator-visible status so you can inspect what is running, what is queued, and what the hardware appears to be doing.Documentation Index
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The problem Enoch solves
Autonomous research runs fail in mundane but important ways:- an agent session can finish while child processes keep running;
- a GPU worker can still be busy when a queue item looks idle;
- queue state can become stale across dashboards, Notion, and runtime files;
- evidence can be scattered across worker folders;
- generated reports can overstate unsupported claims if provenance is lost.
Architecture flow
Main components
- Control plane API — queue/project/run/paper state, pause controls, dashboard data, dispatch decisions, and paper review APIs.
- Wake gate — process-tree and CPU/GPU quiet-window checks for long-running local jobs.
- Worker preflight — authenticated checks against the worker API before live dispatch.
- Single active lane — the control plane blocks overlapping active queue work.
- Evidence sync — copies evidence from worker project folders before artifact generation when enabled.
- Artifact writer — creates draft research artifacts from evidence context using a deterministic template or an optional OpenAI-compatible provider.
- Corpus packaging — stores generated artifacts with provenance and quality metadata.