Plain-language automation and agent delivery workspace

Turn plain-language descriptions into working AI-powered automations and agents that run under review. For operations and product teams that need working AI automations and agents but lack dedicated automation engineers, convert plain-language task descriptions, existing workflow files, connected app events and document sources into a tested, versioned and monitored automation owned by the client. The benefit is a testable hypothesis, measured through accepted automations per delivery hour and post-deployment failure rate; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect plain-language task descriptions, existing workflow files, connected app events and document sources, then follow this sequence: 1. Generate a draft workflow from a plain-language task description. 2. Show and edit the workflow as nodes and logic on a visual canvas. 3. Create multi-step AI agents from prompts. 4. Modify existing workflows directly in the editor. 5. Detect broken nodes and suggest fixes. 6. Build, test and deploy the workflow from the workspace. 7. Run server-side with retries and throttling. Resolve uncertain cases with qualified reviewers, approve the tested automation, and measure accepted automations per delivery hour and post-deployment failure rate against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate workflows for the 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 connected app set and one document source; final production deployment and governance decisions remain with the client's named owner. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve client data boundaries, source attribution, credential handling and usage permissions. The client's named owner approves production deployment and governance scope. One connected app set and one document source; final production deployment and governance decisions remain with the client's named owner. 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 connected app set and one document source; final production deployment and governance decisions remain with the client's named owner. Implement one approved input format, a bounded representative case set and the first two task modules: generate a draft workflow from a plain-language task description; show and edit the workflow as nodes and logic on a visual canvas. Support the third module with operator review: create multi-step AI agents from prompts. 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

Client-owned workflow files, authorized app accounts and permitted document sources. Cloud and self-hosted deployment targets, app event sources and language model providers. 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: Plain-language task intake, Visual workflow editor, Run and deployment console. Use a project list for automations, a central node canvas with a right-hand panel for prompts, credentials, test data and comments. Let users compare draft and production versions side by side. Display draft, tested, deployed and paused states. Provide a run log with per-step inputs, outputs and retries. Make the task-specific outcome tested, versioned and monitored automation visible beside its evidence, review state and value baseline.