Plain-language cross-app agent operations portal

Reduce manual coordination across connected apps while keeping every automated action reviewable. For operations leads and small teams running repetitive work across several connected business apps, convert plain-language instructions, connected app permissions and observed team behavior into reviewed agent workflows with audit trails. The benefit is a testable hypothesis, measured through completed task runs per operations hour and correction rate after agent action; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect plain-language instructions, connected app permissions and observed team behavior, then follow this sequence: 1. Create agents from plain-language task descriptions. 2. Connect third-party apps and authorize actions. 3. Run agents automatically in the background. Resolve uncertain cases with qualified reviewers, approve reviewed agent workflows with audit trails, and measure completed task runs per operations hour and correction rate after agent action 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 app set and permission model; final approval of consequential actions remains human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve source attribution, permission scope and audit accuracy. Named owners approve consequential actions and external integrations. One fixed app set and permission model; final approval of consequential actions remains 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 fixed app set and permission model; final approval of consequential actions remains human. Implement one approved input format, a bounded representative case set and the first two task modules: create agents from plain-language task descriptions; connect third-party apps and authorize actions. Support the third module with operator review: run agents automatically in the background. 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 app accounts, authorized team activity data and permitted research sources. Cloud storage, app connectors and export 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: Agent builder and instructions, Workflow canvas and run monitor, Approvals and audit log. Use a list of agents and playbooks, a central canvas showing steps and triggers, and a right-hand panel for permissions, trust level and run history. Let users compare a draft plan with an executed run. Display draft, awaiting approval, running, completed and failed states. Provide a client preview link with comments anchored to the relevant step. Make the task-specific outcome reviewed agent workflows with audit trails visible beside its evidence, review state and value baseline.