From plain-language description to working application — every step of the orchestration process, explained.
Best for: Anyone evaluating or using CEO.ai — technical or not. CEOs who want to understand what they're buying. Developers who want to understand the architecture. Team members who'll be using it daily.
Prerequisites: Basic understanding of what AI agents are. If you're starting from zero, read What Is RAG Training? first.
Before we walk through the steps, let's establish what the CEO Agent is — and equally important, what it isn't.
The CEO Agent is an AI project manager that takes your plain-language description of what you want built, assembles the right team of AI specialists, and delivers a complete, working project — committed to your GitHub repository.
It doesn't write all the code itself. It doesn't just generate one file. It orchestrates an entire team of AI agents — selecting the right architect to design the system, then assigning every sub-task to the best available specialist agent.
Think about what happens when a CEO at a software company says: "We need an app that captures leads from Telegram and puts them in Salesforce."
In a traditional company, here's what follows:
The CEO Agent does steps 2 through 9. Automatically. In hours instead of weeks.
The CEO (you) handles step 1 (describe what you need) and step 10 (review the result). Everything in between is orchestrated.
The CEO Agent doesn't produce a prototype, a mockup, or a "proof of concept." It produces:
All committed to YOUR GitHub repository. You own it entirely.
Now let's walk through how it happens, step by step.
What you do: Write a description of what you want built. In plain language.
You type something like this:
"Build an app that captures lead information from Telegram conversations in natural language, transforms the data using an AI agent on the backend, and inserts structured lead records into our connected Salesforce account. Include a monitoring dashboard to view captured leads and their status. Use React for the frontend, AWS Lambda for the backend, and DynamoDB for storage. Deploy using Terraform."
Or it could be simpler:
"Build an internal tool where our support team can search across all our knowledge base articles and get AI-generated answer suggestions for customer tickets."
Or more complex:
"Build a multi-channel messaging system where AI agents respond to customer inquiries on WhatsApp, Slack, and Telegram. Each channel should route to the same AI agent via our Agent API. Include conversation logging, a search interface for reviewing past conversations, and admin controls for updating the agent's knowledge base. Deploy on AWS."
The key point: You describe the WHAT — what you want the system to do, who will use it, and what the expected behavior is. You do NOT need to describe the HOW — the architecture, the specific code patterns, the database schema, or the deployment configuration. That's the architect's job (Step 3).
When you submit your project description, the CEO Agent analyzes it to understand:
This analysis informs the most important decision the CEO Agent makes: which architect to assign.
This is not random assignment. It's not round-robin. It's an intelligent selection based on multiple factors.
The CEO Agent evaluates every available architect agent and picks the one best suited for YOUR specific project. It maintains awareness of every agent in the system — your private agents, system-provided agents, and any community agents you've opted into.
Does the architect have experience (via RAG knowledge or past ratings) with the type of project you've described? An architect trained on API integration patterns is a better fit for an integration project than one specialized in data visualization.
If you've specified technologies (React, AWS, Terraform), the CEO Agent looks for architects with demonstrated capability in those areas — either through RAG training or through past project ratings involving them.
Every time you rate an architect's performance (Step 7), that rating feeds into future selection decisions. Architects that consistently receive high ratings on similar project types are more likely to be selected.
If an architect has been trained on knowledge relevant to your project — your company's API docs, your infrastructure preferences, your coding standards — it has an advantage over a generic architect.
Simple projects don't need your most sophisticated architect. Complex projects shouldn't get a lightweight one. The CEO Agent matches the complexity of the project to the capability of the architect.
Why this matters
Architect selection is the highest-leverage decision in the entire process. A great architect produces a clear, well-structured specification that makes every downstream agent's job easier. A mediocre architect produces a vague specification that leads to inconsistent, poorly integrated output.
After the CEO Agent selects the architect, you can see which one was chosen and why. This isn't a black box — the selection is visible in the project details, so you understand the reasoning and can evaluate whether the choice was appropriate.
This is the step that separates the CEO Agent from every "AI coding assistant" on the market.
The selected architect agent takes your project description and produces a complete specification — the blueprint that all other agents will work from. Those other tools generate code file by file, with no overarching architecture. The CEO Agent's architect creates a unified design FIRST, then delegates implementation.
How all the components connect and communicate. Which services handle which responsibilities. Where data flows.
Database schemas, API contracts, data structures, and how information is represented at each layer.
Specific frameworks, services, libraries, and tools — and the reasoning behind each choice.
This is the critical output. The architect breaks the entire project into discrete, assignable sub-tasks. Each task is specific enough that a single agent can complete it, independent enough that it can be worked on without waiting for other tasks, and clear about inputs and outputs.
Example task list for the Telegram-to-Salesforce project:
| # | Task | Category | Description |
|---|---|---|---|
| 1 | Frontend: Lead dashboard layout | Frontend | Build the main dashboard component with lead list, status indicators, and search |
| 2 | Frontend: Lead detail view | Frontend | Build the individual lead detail view with all fields and raw message display |
| 3 | Frontend: API integration | Frontend | Connect the dashboard to the backend API for data retrieval |
| 4 | Database: Schema and migrations | Database | Create DynamoDB table definitions and initial setup |
| 5 | Backend: Telegram webhook handler | Backend | Lambda function to receive and validate Telegram webhook events |
| 6 | Backend: AI data transformation | Backend | Lambda function that processes raw message text through AI agent for structured data extraction |
| 7 | Backend: Salesforce sync | Integration | Lambda function that syncs processed leads to Salesforce via REST API |
| 8 | Backend: Dashboard API | Backend | API Gateway + Lambda endpoints for the frontend to query lead data |
| 9 | Infrastructure: Terraform configs | Infra | Complete Terraform definitions for all AWS resources |
| 10 | Integration: Telegram Bot setup | Integration | Bot registration, webhook URL configuration, authentication |
| 11 | Integration: Salesforce OAuth | Integration | OAuth flow implementation for Salesforce API access |
Which tasks can run in parallel and which depend on others completing first. This lets the CEO Agent optimize execution.
This is where architect RAG training pays enormous dividends.
An architect trained on YOUR Salesforce field mappings will produce a data model that matches your CRM configuration perfectly. An architect trained on YOUR AWS infrastructure patterns will produce Terraform configs that follow your naming conventions and security policies.
Without RAG training, the architect produces a generically correct specification. With RAG training, it produces a specification that's correct for YOUR specific environment.
With the architect's task list in hand, the CEO Agent becomes a staffing manager.
For each sub-task, it selects the best available agent. The CEO Agent sees your complete agent roster — custom agents you've built, system agents provided by the platform, and any community agents you've enabled.
For each task on the list, the CEO Agent evaluates:
What type of work is this?
Frontend development? Backend logic? Database design? Infrastructure configuration? API integration?
Which agents are available?
Each agent has a defined type (Architect or Executor), category, model, and performance history.
Which agent is the best match?
Based on agent category, the model powering the agent, RAG knowledge relevance, and historical ratings on similar tasks.
This is the multi-agent advantage in action.
A single AI trying to write everything — frontend React components, Lambda functions, Terraform configs, Salesforce integration code — produces inconsistent quality. It might be great at React but mediocre at Terraform.
When each task goes to a specialist:
Each agent produces its best work on the task it's best suited for. The combined output is dramatically higher quality than any single agent could produce across all domains.
Every assigned agent executes its sub-task. Code is generated for every component of the project.
| Layer | What's Generated |
|---|---|
| Frontend | React/Next.js components, pages, routing, state management, API client, styling |
| Backend | Lambda functions (or server routes), business logic, request handling, validation, error handling |
| Database | Schema definitions, migration files, seed data, query patterns, index configurations |
| Infrastructure | Terraform configs (or Docker/CloudFormation), IAM roles, networking, service configurations |
| Integrations | API client code, webhook handlers, OAuth flows, authentication, data transformation |
| Configuration | Environment variables, build scripts, package.json, tsconfig, deployment scripts |
The generated code isn't "AI slop" — throwaway code that looks right but doesn't work. It's structured to be:
Clean variable names, logical file organization, comments where they add value.
Components work together because the architect designed the system first.
Error handling, input validation, environment variable management, sensible defaults.
Not just the "interesting" code — the boring-but-essential parts too. Deployable, not just compilable.
No. And we're transparent about that.
First-pass output is typically 80-95% of the way to production-ready. The remaining 5-20% falls into predictable categories:
This is where Step 7 (review, rate, and refine) comes in. And it's where the system improves most dramatically — because the fixes you apply through RAG training and feedback become permanent improvements for future projects.
The complete project — every file, every component — is committed to your GitHub repository.
This isn't one massive commit with everything dumped together. Each sub-task gets its own commit (or series of commits), meaning:
The code is in YOUR repository. On YOUR GitHub account. Under YOUR control. You can fork it, modify it, extend it, deploy it, open-source it, or sell it. There's no vendor lock-in on the output.
For teams that care about code provenance (regulated industries, security-conscious organizations, or just good engineering practice), the commit history provides a clear trail of what was generated, when, and by which agent.
If you have human developers on your team, they can immediately start working with the generated code. It's in Git. They can create branches, make changes, submit pull requests — the same workflow they're already using.
The project is delivered. Now you evaluate the output — and your evaluation teaches the CEO Agent to do better next time.
You look at the output. Maybe you deploy it to a test environment. Maybe your developer reviews the code. Maybe you just try using the monitoring dashboard and see if it works. Then you provide feedback through three mechanisms:
You rate the output at three levels:
⭐ Total Project Rating
"How good was the overall output?"
Tells the CEO Agent about the quality of its end-to-end orchestration — architect selection, task breakdown, agent assignments, and overall coherence.
⭐ Architect Performance Rating
"How good was the specification and task breakdown?"
A high rating means "this architect was a good choice." The CEO Agent learns to select this architect more often for similar projects. A low rating means the CEO Agent will consider other architects next time.
⭐ Individual Sub-Agent Ratings
"How good was each agent's specific contribution?"
The most granular and arguably most valuable feedback. An agent rated highly on frontend work gets more frontend assignments. An agent rated poorly on database work gets fewer database assignments.
Each sub-task maintains its conversation thread with the assigned agent. You can go back to ANY specific agent and refine their contribution:
The agent responds with updated code, and you can continue the conversation as many times as needed — drilling into specifics without starting over.
For systemic issues — where the architect's fundamental approach needs adjustment — you can:
This is the most common refinement pattern. The first pass reveals what the architect didn't know about your specific environment. You fill that gap with a RAG update. The second pass is dramatically better.
Real example: The Telegram-to-Salesforce project's first pass was about 90% there. The architect didn't know the exact Salesforce field mappings for our org. We uploaded those mappings as RAG knowledge, re-ran, and the second pass handled them correctly. Total refinement time: about an hour.
The rating system isn't just feedback for you — it's the learning mechanism for the entire platform.
Project 1
The CEO Agent makes reasonable but generic selections. Results are good but not customized to your preferences.
Project 5
The CEO Agent has learned which architects work well for your project types and which sub-agents produce work you rate highly. Results improve noticeably.
Project 15
The CEO Agent has a detailed model of your preferences, your standards, and which agents produce the best results. It's like having a project manager who's worked with you for a year.
Project 50
The system deeply understands your business's technical environment, quality standards, and the agent roster's strengths and weaknesses. Selections are precise. First-pass quality is consistently high.
This is why the CEO Agent is fundamentally different from a static code generation tool. It doesn't produce the same generic output forever. It learns. It improves. It adapts to YOU.
It's the single most effective lever you have for improving CEO Agent output quality.
The architect can only design what it knows how to design. RAG knowledge defines the boundaries of what it knows.
An architect without RAG knowledge designs generic, best-practice systems. An architect with deep RAG knowledge designs systems tailored to your infrastructure, your conventions, your integrations, and your standards.
| Knowledge Type | What It Does | Example |
|---|---|---|
| Your API documentation | Architects design correct integration points | Your Salesforce custom field definitions, endpoint URLs, auth requirements |
| Your coding standards | Generated code follows your conventions | Your ESLint config, naming conventions doc, PR review checklist |
| Your infrastructure patterns | Terraform/deployment configs match your environment | Your existing VPC configuration, naming patterns, security policies |
| Past specifications | Architects learn your architectural style | Spec documents from previous projects your team has built |
| Third-party platform docs | Architects design correct connections | API docs for the specific version of Salesforce, Stripe, or HubSpot you use |
| Your tech stack documentation | Architects make appropriate technology choices | "We use Next.js 14, Prisma for ORM, and deploy to Vercel" |
| Lessons learned | Architects avoid past mistakes | "We tried X approach and it didn't work because Y — use Z instead" |
Here's the most common success pattern for CEO Agent projects:
Architect RAG Training Is the Highest-ROI Investment
A well-trained architect doesn't just improve its own output — it improves every agent that works from its specification. If the architect gets the data model right, the database agent, backend agent, and frontend agent all work with the correct structure. If the architect gets the API contracts right, the integration agents produce correct connections on the first try.
The CEO Agent adapts to whatever level of detail you provide. But more detail leads to better results on the first pass.
"Build a lead capture app for Telegram to Salesforce."
The CEO Agent will interpret this and produce something reasonable — but it'll make many assumptions. Some will match your preferences. Some won't.
"Build an app that captures lead information from Telegram conversations in natural language, transforms the data using an AI agent, and inserts structured lead records into our Salesforce account. Include a monitoring dashboard."
"Build an app that captures lead information from Telegram conversations in natural language, transforms the data using an AI agent on the backend, and automatically inserts structured lead records into our connected Salesforce account with fields: Name, Company, Email, Interest Area, Follow-Up Date, and Source Channel. Include a monitoring dashboard where our sales team can view captured leads, filter by status (New, Synced, Error), and see the raw Telegram message alongside the structured data. Use React with TypeScript for the frontend, AWS Lambda with Node.js for the backend, DynamoDB for storage, and Terraform for infrastructure. The Telegram bot should handle both private messages and group chat mentions."
Before submitting a project, try to include:
| Element | Question to Answer | Example |
|---|---|---|
| What it does | What is the core function? | "Captures leads from Telegram and puts them in Salesforce" |
| Who uses it | Who are the end users? | "Our sales team uses the monitoring dashboard" |
| Key features | What are the must-have capabilities? | "Filter by status, view raw messages, search by company" |
| Data model | What information is captured? | "Name, Company, Email, Interest, Follow-Up Date" |
| Integrations | What external systems connect? | "Telegram Bot API, Salesforce REST API" |
| Tech preferences | Any tech stack requirements? | "React, AWS Lambda, DynamoDB, Terraform" |
| Behavioral details | Any specific behaviors? | "Handle both private messages and group mentions" |
The "Second Project" Shortcut
Your first project is a learning experience for both you AND the system. Don't agonize over the perfect description. Write what you know, submit it, review the output, and use what you learn to write better descriptions, add RAG knowledge, and rate agents. By your third or fourth project, you'll intuitively know what level of detail the CEO Agent needs.
Here's the final mental model:
| Traditional Team | CEO Agent Equivalent |
|---|---|
| CEO describes what they need | You write the project description |
| CTO selects the right architect | CEO Agent evaluates and selects the best architect |
| Architect designs the system | Architect agent generates specification and task list |
| Project manager assigns tasks | CEO Agent assigns each sub-task to the best agent |
| Development team builds each component | Specialized executor agents generate code for each task |
| Code is committed to version control | Everything is committed to your GitHub with full history |
| CEO reviews and provides feedback | You rate the project, architect, and sub-agents |
| Team learns from feedback | CEO Agent improves selection decisions for future projects |
The entire cycle — which takes a traditional team 2–12 weeks — takes the CEO Agent 1–3 hours.
That's not an exaggeration for marketing purposes. The Telegram-to-Salesforce app (frontend, database, AWS Lambda backend, Terraform infrastructure, and two third-party integrations) was one-shotted in about an hour. Refinement took another hour. Total: ~2 hours for what would have been a 2–4 week project with a human team or a $10,000–$25,000 contract with an agency.
The math either works for your business or it doesn't. But at least now you understand exactly what's happening under the hood.
The Non-Technical CEO's Complete Guide — understand the knowledge system that makes agents effective.
Learn about creating, cloning, and managing the agents that the CEO Agent orchestrates.
Real projects with real timelines, including the refinement process.
The CEO Agent builds things; workflows run things. Understand the non-project side of automation.
Bring a project idea to your setup call. We'll show you how the CEO Agent breaks it down, assigns agents, and delivers a working result — so you can see the orchestration process firsthand.
Every CEO.ai plan includes access to the CEO Agent. Most customers run their first project within days of starting.
Keep this walkthrough handy for your team. Includes the Description Checklist as a bonus one-page reference.
No spam. We'll send the PDF and occasional product updates. Unsubscribe anytime.