Part of one argument: agents propose, a human decides, the record survives.
Governed agent operations

JB OS: agents doing scheduled work, with a human on the gate

A production system where AI agents run on a schedule, ingest documents, sync calendars, track what every provider costs, and compile a weekly brief. Anything consequential stops at an approval card and waits for a person.

Agent action approval gateAgent actions split at a decision point: routine work applies automatically; anything consequential waits at a gate for the owner to sign off.AgentactionNeedsreview?ApprovalgateOwnerAppliedSkipsgate
Agent actions split at a decision point: routine work applies automatically; anything consequential waits at a gate for the owner to sign off.
Why it matters

Scheduled AI agents do real operational work here while I stay accountable for anything consequential. The approval gate is designed into the architecture, not bolted on: an agent proposes, I confirm, and that confirmation is what commits the change. Because it runs on my own operational data, it is shown as a walkthrough on request rather than a public link.

IN PRODUCTIONWalkthrough on request

Problem

Most organizations putting agents into production cannot answer three questions: what did the agent do, when did it do it, and what did it cost. Without those answers you cannot audit the system, you cannot defend it, and you cannot budget it.

I built the answer where I could be my own regulator first. Every agent action is logged, everything consequential blocks on a human signature, and provider spend is tracked daily across the whole fleet.

What it does

Scheduled functions do the routine work: they ingest documents, sync calendars, track what every AI provider costs, and compile a weekly brief. Routine actions apply automatically. Anything consequential stops, pushes an approval card, and blocks until a person signs. The gate is architectural, not a setting: an agent proposes, and my confirmation is what commits the change. Every pipeline action lands in an append-only audit trail, and provider spend is totaled daily across the fleet.

Outcomes - every claim graded

Every claim here is either verified against an artifact I can show you, or marked as my own report. Ask for either.

  • Scheduled agents run the routine operational work: document ingest, calendar sync, provider cost tracking, and a weekly brief
  • Propose-then-approve runs end to end in the data pipeline: an agent proposes, a person signs, and the signature is what commits the change

Governance

  • Pre-commit boundary enforcement: no real operational data can enter the repository, checked mechanically on every commit
  • Human approval gates: consequential agent actions block until signed
  • Propose-then-approve with dry-run transport before anything writes
  • Append-only audit trail on every pipeline action
  • Role-based access enforced at the database row level, not in application code
  • Daily spend accounting across providers, instrumented and scheduled

What transfers

The pattern is the same one I would deploy in an operating company: agents handle the routine, humans hold the consequential, every action leaves a record, and the cost is visible daily. Proven here on data I own, where I could take the risk of being wrong.

Stack

Next.jsTypeScriptSupabase Postgres with row-level securityScheduled serverless functionsAnthropic SDK

If you build or hire around systems like this, say hello.

julia@jbroberg.com

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