Overview
Agentic AI Developer is an AI software developer you talk to in plain words. You describe a business problem; it asks a few smart questions, writes a full plan, then builds a complete AI agent with tests, quality scoring, packaging, and a delivery report, following the standards of a professional software team.
What is an AI agent? A computer worker that understands normal language and completes tasks on its own. A chatbot only answers; an agent acts: it reads, decides, uses tools, and finishes the job.
Who this guide is for: anyone, no technical background needed.
| What you give | A plain-words description of the business problem |
|---|---|
| What you get | A complete, working agent project: code, tests, quality scorecard, deployment package, and a written report |
| Time | One working session for a pilot; larger systems in stages |
| Works inside | Claude Code, Codex, ChatGPT desktop app (any AI that can save files) |
| Built for | Real use: from 1,000 users to millions, not just demos |
Before you begin
You need two things:
- A coding-capable AI tool. One of: Claude Code, Codex, or the ChatGPT desktop app. (This developer writes real project files, so a plain chat window is not enough.)
-
The connector added to that tool. A one-time step:
Claude Code
claude mcp add --transport http agentic-developer https://agentic-developer-psi.vercel.app/mcpCodex or ChatGPT desktop app
codex mcp add agentic-developer --url https://agentic-developer-psi.vercel.app/mcp
Quick start: your first agent in 5 steps
-
Open a new chat and describe the business problem in one paragraph. Include: who you are, what is painful, who will use the agent, and what should happen. Example:
"We run a 6-hospital network. After discharge, patients call our nurses all day about medications and wound care. I want an agent in our patient portal that answers ONLY from our approved care documents, alerts a nurse immediately on red-flag symptoms, and books follow-up appointments. About 8,000 patients a month, English and Spanish."
- Answer its questions. It replies with a handful of questions made for YOUR problem: where your documents live, which systems it must connect to, what must never happen. Plain-words answers are fine; specific answers make a better agent.
- Review the plan. Before any code, it writes the architecture plan and short "specs": readable documents stating exactly what will be built and how you will know it works. Skim them; flag anything that looks wrong. Changing the plan now costs a sentence.
- Let it build. It writes the tests first, then the agent code until the tests pass, then the quality scorecard, the deployment package, and the report. This takes a while, because it is doing the full professional cycle, not a quick demo.
- Receive the project. You get a complete folder: the working agent, passing tests, the scorecard, the ready-to-deploy package, and a plain-language report telling you how to run it and what (if anything) remains. Ask: "How do I run this?" The exact commands are in the report.
Understanding what happens during a build
You never have to manage these steps, but knowing them explains why the result is dependable. Every build moves through one cycle, in this order:
- UnderstandQuestions about your specific problem, never a fixed form.
- PlanA written architecture: what the agent does, what information it uses, how big it must be, what could go wrong.
- AgreeShort specs: "it will do exactly this; we'll know it works when exactly that happens."
- Build, tests firstThe exam is written before the class is taught; code is written until the exam passes. Safety rules are built in; secret keys are never written into the code.
- ScoreAn "eval" ships with the agent: a report card of real example questions and expected behavior, so quality is measured, not guessed, today and after every future change.
- PackageThe agent is sealed into a standard container (Docker) that runs the same on any computer or cloud.
- Automate shippingA pipeline (CI/CD) that re-tests and re-ships every future update the same way, every time.
- ScaleFor serious traffic, a manager (Kubernetes) runs many containers side by side, replaces crashed ones, and adds more at rush hour. This is how the same agent serves 10,000 or 10 million users.
- WatchLogs, health checks, and score tracking, so you know it is healthy after launch, not just on day one.
A demo is step 4 alone. Enterprise-level means all nine, and that is what this developer delivers.
Capabilities: everything it can build
Agents
- Single agents: support, intake, research, booking, routing, follow-up
- Agent teams: multiple agents that hand work to each other (for example: one reads the email, one researches, one drafts the reply)
- Safety built in: guardrails on what the agent may say and do, human escalation for sensitive actions, no secrets in the code
Knowledge and data
- Answer only from YOUR documents: manuals, policies, price lists, care instructions, instead of guessing (this is called RAG)
- Databases: where the agent stores what it learns and does
- Connections to your systems: calendars, CRMs, email, existing company APIs
Interfaces
- APIs: the "plug socket" that lets your website, app, or WhatsApp talk to the agent
- Chat interfaces: a chat window for your website or product
Quality (included with every build)
- Test suites: written before the code, passing before delivery
- Evals: the agent's report card, so quality is measurable forever
- A written delivery report: what was built, how to run it, what remains
Deployment and scale
- Docker packaging: ships ready to run anywhere, every build
- CI/CD pipelines: automatic test-and-deploy for future updates
- Kubernetes deployments: for thousands to millions of users
- Monitoring: health checks and logs for life after launch
How-to guides
How to request a new agent
One paragraph, five ingredients: who you are, the pain, the users, the must-haves, the scale:
"We are [business]. [The painful situation]. I want an agent that [does what], for [who uses it], connected to [your systems/documents]. It must never [red lines]. Expect [number] users/messages per month."
How to build for serious scale
State the numbers. The design is sized to them:
"…around 8,000 users a month now, 15,000 within 18 months. Include the deployment setup for that scale."
How to connect it to your documents
Point to the material and state the rule:
"Answer ONLY from the documents in [folder/path or system]. If the answer is not there, say so and offer a human handoff, never guess."
How to take a pilot to production
After a pilot delivery:
"The pilot is approved. Now add [the deferred items from the report: e.g. the real API endpoint, the Kubernetes deployment], as a new build on the same plan."
Best practices for a great result
- Describe the problem, not the technology. "Nurses are overloaded with discharge calls" beats "build me a RAG chatbot."
- State the red lines. "It must never give medical advice / never double-book / never email a customer without approval". These become built-in rules.
- Give real numbers. Users per month, documents count, response-time hopes. The design is sized to them.
- Answer its questions specifically. Every answer becomes part of the plan.
- Read the delivery report. It lists exactly what remains (like adding your real API keys), usually five minutes of work it could not do without your accounts.
Troubleshooting and FAQ
It is asking questions instead of building. Is something wrong?+
No. That is the process. It always understands first, plans second, builds third. If you ask it to skip ahead, it will politely refuse and finish the plan first.
Do I need to know how to code?+
No. You describe and approve in plain words. For putting the finished package on your own server, ask it for step-by-step instructions, or hand the folder to any technical person.
How is this different from asking ChatGPT for code?+
A chat gives you a code snippet. This gives you the full cycle: plan, agreement, tests, report card, packaging, and a delivery report. Those are the parts that make an agent dependable enough for real customers.
Where is my project?+
In the project folder on your computer (it tells you the exact path), with the report inside. In the ChatGPT app, make sure your workspace folder was set. It will remind you if not.
Can it work with my existing systems?+
Yes. Name them in the brief (calendar, CRM, email, internal API). If a connection needs an account or key from you, the report will say exactly what to provide.
What if my problem is too big for one sitting?+
It delivers in stages: a working pilot first, with the remaining stages listed in the report. Each stage builds on the same plan.
The first reply took a long time.+
The service sleeps when unused; the first response can take 20–30 seconds. After that it is fast.
Glossary
| Term | Plain meaning |
|---|---|
| AI agent | A computer worker that understands language and completes tasks on its own |
| Specs | Short written promises of what will be built and how success is measured |
| Tests | Automatic checks, written before the code, that must pass before delivery |
| Evals | The agent's report card: example questions with expected behavior, scored |
| RAG | Answering only from your own documents instead of guessing |
| API | The plug socket other software uses to talk to the agent |
| Docker | A sealed box that makes the agent run the same on any computer |
| CI/CD | The automatic conveyor belt that re-tests and ships every update |
| Kubernetes | The manager that runs many boxes for thousands–millions of users |
| Delivery report | The plain-language note in your folder: what was built, how to run it, what remains |
Agentic AI Developer is one of the live MCP connectors on this site. For websites and landing pages, see the Website Connector guide.