Atlas - Building and deploying a new AI agent required a technical person. Atlas lets anyone on the team turn a client brief into a working agent in under five minutes.

3Creation modes

3Agent types

< 5 minBrief to live agent

0Prompt writing required

The problem

Building the agent wasn’t the bottleneck. Configuring every one by hand was.

Every new client engagement created the same layer of repetitive technical work. Requirements had to be interpreted, prompts written, personas and objectives structured, configuration assembled, organization context mapped, and the final blueprint deployed into the underlying agent runtime. The knowledge for doing that lived with technical people. That meant even when the underlying agent platform was reusable, delivering a new implementation still required someone to manually translate a client’s requirements into exactly the structure the system expected. Atlas was built to remove that translation layer. It turns raw requirements into deployment-ready agent blueprints through an internal interface, allowing operators to move from a conversation, structured brief, or specification document to a live configuration without manually writing prompts or touching the deployment platform. The goal was to turn a technical delivery process into an operational workflow.

How it runs · 06 steps

Requirements Come InThe operator starts with a guided conversation, structured form, or client specification document.

trigger

Input Is StructuredAnswers are validated, documents are parsed, and unstructured requirements are cleaned into a consistent internal format.

process

AI Builds the CoreOpenAI generates the persona, objectives, and conversation logic required for the selected agent type.

Blueprint Is AssembledAtlas converts the generated content into the exact configuration schema expected by the underlying runtime.

process

Organization Context Is AppliedThe blueprint is linked to the correct organization and the required team or client context is inserted.

storage

Agent Goes LiveThe completed blueprint is deployed into the agent runtime while Atlas surfaces progress back to the operator.

output

Engineering detail · 04

Three ways in, one schema outDifferent engagements arrive in different forms. Atlas accepts requirements through a guided AI conversation, a structured form, or an uploaded specification document. Each path performs its own input processing, but all three converge on the same internal blueprint structure. That keeps the deployment pipeline consistent regardless of how the requirements arrived.

Turning requirements into configurationRaw client language is not deployment-ready configuration. Atlas uses OpenAI to translate requirements into structured personas, objectives, and conversation logic, then assembles those outputs into the schema required by the target agent type. The AI handles interpretation. The platform controls the structure.

Different agents need different assemblySupport, sales, and analyst agents do not share identical configuration requirements. Atlas handles those differences behind the interface. Analyst agents, for example, require additional extraction targets to be created sequentially and linked back to the parent configuration. Operators see one creation workflow while the backend handles the implementation-specific assembly underneath.

Deployment is part of the productAtlas doesn’t stop after generating a prompt or JSON file. The completed blueprint is assigned to the target organization, enriched with the required team context, and deployed directly into the underlying runtime. Authentication, token scopes, refresh logic, deployment state, and failure handling are managed by the platform rather than exposed to the operator.

Result

Atlas moved agent creation out of manual configuration and into an internal product. Operators can begin with whatever form the requirements already exist in: a conversation, structured brief, or specification document. Atlas handles the translation into prompts, configuration, organization context, and deployment. Support, sales, and analyst agents can all move through the same interface while the implementation differences stay behind the scenes. What previously depended on someone knowing how the underlying platform worked can now be handled through a repeatable operational process. The expertise stayed in the system instead of staying in someone’s head.

NextDocsy →AI could write the content in minutes, but teams still had to format and brand it before sending it to a client. Docsy turns that content into a client-ready document in under five minutes.

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