Scheduled multi-machine agent operations portal

Reduce manual coordination of recurring agent work while keeping cost, credentials and risky steps under control. For engineering teams running recurring software tasks with AI coding agents, convert an issue tracker, repository access, machine inventory and run policies into reviewed agent runs with logs, cost reports and approval records. The benefit is a testable hypothesis, measured through accepted agent runs per operator hour and rework after unattended execution; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect an issue tracker, repository access, machine inventory and run policies, then follow this sequence: 1. Schedule agent runs from minutes to weekly. 2. Run agents unattended on a recurring cadence. 3. Isolate each run in a sandbox. Resolve uncertain cases with qualified reviewers, approve reviewed agent runs with logs, cost reports and approval records, and measure accepted agent runs per operator hour and rework after unattended execution against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and one issue tracker per pilot; final code review and merge decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve code ownership, source attribution, license accuracy and usage permissions. Engineers approve substantive changes and deployment scope. One repository and one issue tracker per pilot; final code review and merge decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One repository and one issue tracker per pilot; final code review and merge decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: schedule agent runs from minutes to weekly; run agents unattended on a recurring cadence. Support the third module with operator review: isolate each run in a sandbox. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned repositories, issue trackers and machine inventory. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Fleet and schedule board, Run detail and live log, Approval and cost review. Use a calendar and machine list for jobs, a central run view with streaming logs, and a right-hand panel for budgets, credentials policy and approval state. Let users compare runs side by side. Display scheduled, running, paused, approved and failed states. Provide a client preview link with comments anchored to the relevant run. Make the task-specific outcome reviewed agent runs with logs, cost reports and approval records visible beside its evidence, review state and value baseline.