No-code AI agent builder for teams

AI Agent Builder: Build No-Code AI Agents on Any Model

Pick a base model, name your agent, attach your documents, and write its instructions. Your first agent is ready in minutes - and it works every session after that without re-briefing.

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Quick answerQolaba's AI agent builder lets you create custom AI agents with no code in under 10 minutes. Choose any of 60+ foundation models (GPT-5.5, Claude Opus 4.8, Gemini), upload knowledge bases up to 1,000 pages, write behavioral instructions, and deploy agents for customer support, research, writing, and more.

What it is

What a custom AI agent actually does, and what it doesn't

Quick answerA custom AI agent is a named, persistent workspace assistant that reads from an uploaded knowledge base, follows consistent instructions across every session, and can be shared with an entire team. Unlike a saved system prompt, an agent retains context between sessions and produces the same output for every team member who uses it, regardless of who set it up.

Most platforms call a saved system prompt an “agent.” It is not. A real AI agent has instructions, a knowledge source it can read, memory across sessions, and the ability to be used consistently by a whole team, not just the person who set it up.

On Qolaba, an agent is a named, persistent workspace assistant. It knows your documents. It follows your instructions every time. Every team member gets the same output. The context does not disappear when the browser tab closes. It builds on the same shared workspace that powers AI Studio.

	What most platforms call an "agent"	What Qolaba’s agent actually does
Persistence	Saved system prompt, resets each session	Persistent across every session
Visibility	Visible only to the person who created it	Shared across your whole team
Model choice	Locked to one AI model	Choose from 60+ models per agent
Knowledge base	No document access	Reads your uploaded knowledge base
Context	Starts blank each time	Loads full context automatically

Use cases

Five AI agents teams actually use

Quick answerThe most common custom AI agents for professional teams are: brand voice agents trained on brand guidelines; client brief processors with client history in the knowledge base; research and summarisation agents; weekly report writers set to a consistent structure; and competitor monitors that search, filter, and report on a set schedule. Each agent persists across sessions.

1. Brand voice agent

Upload your brand guidelines, tone document, and approved copy examples. Set the instructions: “Write in our brand voice. Never use passive voice. Always use British English.” Assign Claude Sonnet 4.6 for balanced quality. Every team member uses the same agent, every brief produces consistent output.

2. Client brief processor

One agent per client. Upload the client's brand doc, past briefs, and approved work. When a new brief lands, the agent already knows the client: the voice, the audience, the constraints, the history. No re-briefing. Start producing on the first prompt.

3. Research and summarisation agent

Set it to search the web, summarise sources in your format, and flag conflicting information. Assign Sonar for search-connected tasks or GPT-5.5 for reasoning-heavy synthesis. One agent, consistent research output, available to your whole team.

4. Weekly report writer

Feed it your data format. Set the instructions for structure, depth, and tone. Assign a model with strong analytical reasoning. Every week: paste in the raw numbers, get a structured report. Same format, every time, in the time it used to take to open the template.

5. Competitor monitor

Brief it on your market: who the competitors are, what signals matter, what format you want updates in. Run it weekly. It searches, reads, filters, and reports. One agent, one format, every time.

How it works

How to build an AI agent on Qolaba's no-code AI agent platform

Quick answerBuilding a custom AI agent on Qolaba takes six steps inside the chatbot: open the agent creation panel, select a base model, name your agent, add resources such as a knowledge base, write the agent instructions, and create the agent. Most agents are ready in a few minutes, and pre-built agents let you start instantly.

Six steps, right inside the chat.

Open the agent creation panel

In the chatbot, open the chat input area and select Create Agent to start a new custom agent.

Select a base model

Choose from 60+ models on reasoning, creativity, context length and credit cost. Pick a tool-calling-enabled model if you plan to attach files or a knowledge base.

Name your agent

Give it a clear, descriptive name that reflects its role, plus an optional one-line tagline.

Add resources (optional)

Attach an existing knowledge base or upload files - PDF, CSV, DOC, DOCX, Excel or TXT. Documents up to 1,000 pages or 200 MB; images up to 20 MB.

Write the agent instructions

Shape the system prompt across four areas: Background, Role, Expertise and Behavioral Instructions for tone and output style.

Create the agent

Click Create Agent. It appears in your Agent Selector, ready to use in any chat and to share with your team.

The difference

Qolaba vs. ChatGPT GPTs, what's different

Quick answerChatGPT's custom GPTs are limited to OpenAI GPT models, require a Teams plan for organisational sharing, and do not share session history across users. Qolaba agents support 60+ model choices including Claude, Gemini, and DeepSeek, share workspace context across all assigned team members by default, and use credit-based pricing with no per-seat fee.

ChatGPT offers custom GPTs. They are useful for individual use. They are not built for teams.

The fundamental difference: GPTs are a feature inside one model's subscription. Qolaba agents are a workflow layer across 60+ models, designed for teams.

Feature	ChatGPT GPTs	Qolaba agents
Team sharing	GPT must be published publicly or to org (Teams plan required)	Shared within workspace, private by default
Knowledge base	Upload files per GPT	Upload to workspace, available to all agents in it
Model choice	OpenAI GPT models	60+ models including Claude, Gemini, DeepSeek, Grok
Session memory	Limited, per-user	Persistent workspace context, team-shared
Credit model	$20 to $30/user/month fixed	Pay per use, light use costs almost nothing
Access control	Organisation-level	Workspace-level, role-based

Pricing

What running an AI agent on Qolaba actually costs

Quick answerRunning an AI agent on Qolaba costs roughly 2 to 10 credits per session depending on the model selected. Light models such as DeepSeek are the cheapest. Standard models such as GPT-5.5 and Claude Sonnet 4.6 sit in the middle. Premium models such as Claude Opus 4.8 cost the most. Generating an image in chat runs around 30 to 40 credits. All credit costs are displayed before each task is run.

Every action has a fixed credit cost. You see it before you run it.

Task	Model	Credit cost
First draft of a 500-word brief	DeepSeek (light)	~2 credits
Client-ready document edit	Claude Sonnet 4.6 (standard)	~5 credits
Complex research synthesis	Claude Opus 4.8 (premium)	~10 credits
Query a 100-page knowledge base	Any assigned model	~3-8 credits
Generate a supporting image in chat	Nano Banana 2	~30-40 credits
Voice summary (~100 words)	Text-to-speech	~5-8 credits

A team of 5 running 20 agent sessions per week, a realistic agency workload, spends roughly 300 to 800 credits per week depending on model choice. On the Teams plan (25,000 credits/month), that is covered many times over.

No per-seat fee. Add a team member: they draw from the same pool. Light users cost almost nothing. Heavy users cost more. The total reflects actual usage.

Who uses Qolaba agents

The teams that get the most from AI agents on Qolaba

🏢

Marketing agencies

One agent per client. Brand voice loaded once. Every team member (copywriter, strategist, designer writing captions) works from the same context. Client A cannot see Client B's workspace.

🧑‍💼

Solo consultants

Five clients, five workspaces, five agents. Switch between them without losing context. Each agent knows the client history, the brief, and the approved tone. Context switching cost drops to near zero.

✍️

In-house content teams

One brand voice agent for the whole team. One research agent for weekly reporting. One social agent for platform-specific formatting. Each role uses the agent relevant to their work. One credit pool, one billing line.

🚀

Enterprise teams (bottom-up adoption)

One person starts. Builds an agent that saves their team 2 hours a week. Shares the workspace. The team adopts it. The credit pool grows. No procurement conversation required to start.

Start building

Start building. Your first agent takes minutes.

Pick a use case from the list above. Create a workspace. Upload one document. Write five sentences of instructions. Assign a model. Run it. That is the whole setup.

FAQ

AI agent builder questions

A custom AI agent is a named, persistent workspace assistant that reads from an uploaded knowledge base, follows consistent instructions across every session, and can be shared with an entire team. Unlike a saved system prompt, an agent retains context between sessions and produces the same output for every team member who uses it.

No. This is a fully no-code AI agent builder: from the chat, open Create Agent, choose a base model, name the agent, add a knowledge base or files, write the instructions in plain language, then create it. Most agents are ready in a few minutes.

Agents read from an uploaded knowledge base of PDFs, Word documents, text files, CSVs and URLs. They can also be assigned search-connected models such as Sonar for web research tasks.

Yes. Agents are shared within a workspace with role-based access covering Owner, Admin, Editor, and Viewer. Every assigned team member gets the same context, instructions and output. Workspaces are isolated, so one client’s agent cannot see another’s.

Any of 60+ models including GPT, Claude, Gemini, DeepSeek and Grok. DeepSeek suits fast first drafts, Claude Sonnet 4.6 balanced quality, and Claude Opus 4.8 complex reasoning and long documents.

Roughly 2 to 10 credits per session depending on the model. Lighter models such as DeepSeek are the cheapest, GPT-5.5 and Claude Sonnet 4.6 sit in the middle, and Claude Opus 4.8 is at the top for heavy reasoning. Generating an image in chat is around 30 to 40 credits. Every cost is shown before each task and there is no per-seat fee.

You build an AI agent in six steps: open Create Agent from the chat, select a base model, name the agent, add resources such as a knowledge base, write the instructions across background, role, expertise and behaviour, then create the agent. No code is required and most agents take just a few minutes.

Start with a clear job for the agent, gather the documents it should read, and write plain-language instructions for tone and format. On Qolaba you open Create Agent, pick a base model, add your files or a knowledge base, and create it. There is no coding or infrastructure to manage.

No. You can build custom AI agents without any technical background. Pick one repetitive task, upload a couple of reference files, describe the output you want in a few sentences, and choose a model. The no-code AI agent builder handles the rest, so your first agent is running in minutes.

Building a custom AI agent on Qolaba takes under 10 minutes with no code. The process: select a foundation model (GPT-5.5, Claude, Gemini, etc.), name your agent, upload your knowledge base (up to 1,000 pages), write behavioral instructions, and deploy. Compared to building AI agents from scratch with code, Qolaba’s no-code AI agent creator saves weeks of development.

Proof

Agents already running in production

“Qolaba changed the way our staff learns. The AI training agents made practice feel real, not theoretical. This saved us weeks of classroom time.”

Smriti SharmaLead Trainer, Apollo Hospitals

“We built a fully working AI learning and productivity platform with Qolaba faster than most teams write a proposal. They ship.”

Sanjeev BhattVice President, EXL

⚙️

Advanced agent capabilities

Beyond the basics, Qolaba agents handle deep web scraping, batch processing across many inputs, PII protection for sensitive data, and a shared prompt library your team reuses.

Build your first AI agent free, no card, 400 credits on signup

Name it, train it on your documents, assign any of 60+ models, and share it with your team.