No-code agent build and deployment workspace

Reduce the number of rented tools and handoffs needed to build, test and deploy custom AI agents. For operations and technical teams building custom AI agents without coding, convert plain-language instructions, uploaded documents, integration credentials and workflow rules into a deployed, monitored agent owned by the client. The benefit is a testable hypothesis, measured through deployed agents per build hour and post-deployment correction rate; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect plain-language instructions, uploaded documents, integration credentials and workflow rules, then follow this sequence: 1. Create agents from plain-language instructions without code. 2. Design agent logic on a visual node-and-connection canvas. 3. Attach built-in tools and connect external apps. 4. Train agents on uploaded documents and data. 5. Test sub-agents independently and evaluate performance. 6. Track agent behavior step-by-step for debugging. 7. Refine agents through manual or conversational edits. 8. Schedule proactive tasks and publish agents to the web. Resolve uncertain cases with qualified reviewers, approve deployed, monitored agents owned by the client, and measure deployed agents per build hour and post-deployment correction rate against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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 approved integration set and one deployment target; final workflow approval and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve client data ownership, source attribution, credential handling and usage permissions. Clients approve substantive workflow changes and deployment scope. One approved integration set and one deployment target; final workflow approval and consequential actions 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 approved integration set and one deployment target; final workflow approval and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create agents from plain-language instructions without code; design agent logic on a visual node-and-connection canvas. Support the remaining modules with operator review: attach built-in tools and connect external apps; train agents on uploaded documents and data; test sub-agents independently and evaluate performance; track agent behavior step-by-step for debugging; refine agents through manual or conversational edits; schedule proactive tasks and publish agents to the web. 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 documents, authorized app credentials and permitted workflow data. Cloud storage, app connectors and deployment 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 brief and inputs, Visual workflow canvas, Test and evaluation, Deployment and monitoring. Use a thumbnail gallery for agent projects, a large central canvas for nodes and connections, and a right-hand panel for instructions, tools, documents and comments. Let users compare agent versions side by side. Display draft, in test, deployed and paused states. Provide a client preview link with comments anchored to the relevant agent step. Make the task-specific outcome deployed, monitored agents owned by the client visible beside its evidence, review state and value baseline.