Golemry adds an overseer agent to review every scheduled MCP job
u/bothlabs built Golemry, a remote MCP server that schedules and runs agent jobs in sandboxed cloud workers, with a separate overseer agent that reviews every run and returns a passed, flagged, or failed verdict.
Score breakdown
Golemry targets a gap in agentic job pipelines where a scheduled job can succeed technically while failing practically — a silent quality degradation the post illustrates with a real research job that produced shallow summaries without ever erroring.
- 01Golemry is a remote MCP server for scheduling, running, and managing recurring agent jobs in the cloud.
- 02A separate overseer agent reviews every run and returns a verdict: passed, flagged, or failed, with a short reason.
- 03The overseer is intentionally decoupled from the execution agent so the job runner does not grade its own work.
Golemry addresses a specific failure mode in scheduled agent workflows: a job can complete without errors while producing output that is practically wrong. u/bothlabs describes this as the core motivation — at scale, manually checking every run becomes the bottleneck, and silent quality degradation goes undetected. Golemry sits as a layer after job creation, letting an agent set up, run, and manage scheduled jobs entirely over MCP. Configuration is a single JSON block pointing to `https://mcp.golemry.com/mcp`, and most MCP clients will self-configure if asked.
Under the hood, the server is serverless on Cloudflare using the Worker and Agents SDK for low latency and natural scaling.
Under the hood, the server is serverless on Cloudflare using the Worker and Agents SDK for low latency and natural scaling. Each job runs in a sandboxed worker agent with a small, per-job tool scope — a design choice the post frames as both a security boundary and a reliability improvement, since fewer tools mean fewer ways for the agent to wander off-task. The overseer is deliberately separate from the execution agent, returning a verdict of passed, flagged, or failed alongside a short reason. u/bothlabs notes the overseer caught a weekly research job that was producing clean-looking summaries while the underlying work had quietly gone shallow.
V1 ships with scheduled jobs, sandboxing, per-job tool scoping, a connector library, and the overseer review-and-escalation loop. Features listed as on the roadmap but not yet shipped include event-based triggers, live human-in-the-loop intervention, and an overseer that learns from user feedback. The service is live, free to try, and requires users to bring their own model key.
Key facts
- 01Golemry is a remote MCP server for scheduling, running, and managing recurring agent jobs in the cloud.
- 02A separate overseer agent reviews every run and returns a verdict: passed, flagged, or failed, with a short reason.
- 03The overseer is intentionally decoupled from the execution agent so the job runner does not grade its own work.
- 04Each job runs as a sandboxed worker agent with a small, per-job tool scope to limit what it can access and reduce drift.
- 05The server runs serverless on Cloudflare using the Worker and Agents SDK.
- 06It works with Claude Code, Claude Desktop, Cursor, or any MCP client, and authenticates via OAuth.
- 07V1 is live and free to try; event-based triggers, human-in-the-loop intervention, and a feedback-learning overseer are on the roadmap.
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