How to Build Custom AI Agents in Minutes Using Chai (Vibe Code)

We have seen artificial intelligence (AI) tools and models advance in front of our very own eyes; some of us adopted these tools more easily and earlier than others. Large language model (LLM) tooling exploded after the launch and success of ChatGPT over the past two and a half years, yet most business teams are still struggling to push the idea of an AI agent from whiteboard to production.

Why? The majority of business professionals are non-technical and do not have a deep understanding of what goes on behind the scenes. Trust me, business professionals aren't alone; even the technical software developers struggle to stay up to date with all the latest updates, research, and developments. They might juggle model APIs, vector stores, tool integrations, and inflexible frameworks—often for weeks—before any employee sees value.

For most businesses, creating a custom AI agent that is

1. Intelligent and autonomous

2. Can understand and chat with your data

3. Can automate specific workflows.

Has the ability to turn things around for companies by taking on repetitive and even demanding specialized technical knowledge.

What is stopping them? Development time and cost, and insufficient technical understanding. But there is always a but, but what if you could describe the AI agent you need in plain language and literally watch it come to life, ready for work, in a remarkably short period?

Langbase, a serverless AI agent platform, has compressed the production timeline to minutes with technical expertise into Chai.

In this article, we'll look at what Chai is, how it works, why it matters to non‑technical professionals, and how to build custom AI agents in minutes using Chai by Langbase (vibe code).

What is Chai by Langbase?

Chai by Langbase is a prompt‑first service that builds, deploys, and scales AI agents straight from plain English. In much simpler terms, Chai can build AI agents for you. Users can vibe code production-ready AI agents within minutes after entering the prompt/ agent idea.

The company has been processing over 100 million agent runs monthly, so the concept is battle‑tested rather than experimental. With Chai, users do not need technical knowledge or understanding of what is happening behind the scenes; you need an idea of what you want help with and how to automate it.

What sets Chai apart?

Langbase describes Chai with three simple verbs—"Prompt. Sip. Ship," which literally means enter a prompt for your agent, sip chai tea while it vibe codes the agent for you, and ship it to your clients. The real highlight of the tool is its goal of converting natural‑language instructions into production‑ready services without the usual engineering grind.

Under the hood, four AI primitives do the heavy lifting:

• Agents. Chai coordinates reasoning and planning steps so complex tasks unfold coherently.

• Tools. It connects those agents to external APIs or internal data, letting them "take action" instead of just chatting.

• Memory: The platform adds long‑term context, using automatic retrieval‑augmented generation (Auto‑RAG) to recall past interactions and business facts.

• Workflows: It chains multiple agents and tools, so an end‑to‑end job like summarising a contract and filing it in a document system runs with a single request.

Because these primitives sit behind a unified API, product managers can mix and match them without learning a new framework every quarter. The platform's serverless design also means you never touch infrastructure; Langbase handles scaling, security patches, and model upgrades.

Complexity removal

Most AI projects stall on four pain points. Chai addresses each out‑of‑the‑box:

• LLM orchestration: Pick your preferred model (OpenAI, Google, Anthropic, local) and call it through one endpoint.

• Tool integration: Register a third‑party API once; Chai injects it where the agent needs it.

• Memory management: Auto‑RAG slots past conversations and proprietary files into prompts; no vector database setup is required.

• Production scale: AI agents that prove useful in a pilot can ride Langbase's existing cluster to millions of monthly calls without re‑architecting code.

Chai's Business Impact

• Minutes vs. weeks: Pilot AI agents can live the same afternoon they're imagined, cutting exploratory costs.

• One seat, many models: Finance might stick with GPT‑4o while legal prefers a private llama model; both run through the same dashboard.

• Predictable spend: Usage‑based pricing tied to Langbase's metered tokens means no surprise hosting bills.

• Observability by default: Every run is logged with latency, cost, and tool‑call traces, making audit and optimisation routine rather than forensic.

How to Build Custom AI Agents in Minutes Using Chai (Vibe Code):

Step 1: Visit Chai.new.

• Click on Get Started to sign up using your email or GitHub account.

Step 2: Decide what type of AI agent you need and enter a prompt for that specific agent. For this article, we requested Chai to generate a project management agent that can manage work-life balance.

Step 3: Chai will start by thinking and creating an overview of the AI agent. Finally, once Chai is done, you will be asked to enter the API key to deploy the AI agent.

Step 4: As soon as you enter your API key, you can deploy the AI agent and view the UI.

Conclusion

Chai by Langbase is noteworthy for businesses looking to automate tasks and for developers. It makes the creation of a custom production-ready AI agent more attainable even for someone with no coding knowledge. Being a prompt-based platform opens up new possibilities for more businesses to use specialized AI agents to help their operations, as Chai takes care of the technical difficulties. Full power is in the hands of users, and they can articulate what they want their agent to do using simple text prompts.