Managed persistent agent deployment workspace

Run agents as managed, persistent services instead of one-off sandbox executions. For engineering teams and platform operators running AI agents as production services, convert agent code, prompts, credentials, routing rules and integration settings into a deployed, versioned agent service with API access, logs and rollback. The benefit is a testable hypothesis, measured through successful agent invocations per operator hour and recovery time after restart; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect agent code, prompts, credentials, routing rules and integration settings, then follow this sequence: 1. Deploy an agent from a command, prompt or API call. 2. Keep agent sessions alive across restarts and long gaps. 3. Run agents continuously beyond temporary sandbox executions. Resolve uncertain cases with qualified reviewers, approve a deployed, versioned agent service with API access, logs and rollback, and measure successful agent invocations per operator hour and recovery time after restart 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 fixed deployment target and approved model set; final production release and security checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve agent behavior, source attribution, credential security and usage permissions. Engineering owners approve substantive changes and production scope. One fixed deployment target and approved model set; final production release and security checks remain engineering. 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 fixed deployment target and approved model set; final production release and security checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: deploy an agent from a command, prompt or API call; keep agent sessions alive across restarts and long gaps. Support the third module with operator review: run agents continuously beyond temporary sandbox executions. 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 agent code, authorized model providers and permitted SaaS tools. Cloud container runtime, secret stores, logging destinations and billing providers. Start with file exchange and validate destination specifications before promising direct deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Agent registry and deployment, Live session and execution monitor, API and integration settings. Use a list of deployed agents with status and version, a central execution timeline with logs and retry controls, and a right-hand panel for routing, tools, credentials and integrations. Let users compare versions and roll back. Display running, paused, failed and rolled-back states. Provide a client-facing API key page with usage and billing. Make the task-specific outcome a deployed, versioned agent service with API access, logs and rollback visible beside its evidence, review state and value baseline.