Build and deploy your first AI agent without installing anything. All you need is a browser and an account.
Prerequisites
The platform supports three paths depending on your team’s skills and workflow:
| Approach | What It Is | Best For |
|---|
| Arch AI | A built-in multi-agent system that generates blueprints, authors ABL, runs tests, and continuously optimizes. | Teams who want AI to drive the full agent lifecycle. |
| Agent Studio + DSL | A browser-based IDE with a visual form editor and Monaco-based ABL code editor that stay in two-way sync. | Builders who prefer visual configuration or direct ABL authoring with full control. |
| Programmable Interface | REST APIs, MCP, SDK, Lambda, and A2A integrations to build, deploy, and manage agents from code. | Developers integrating agents into CI/CD pipelines or external systems. |
Setup Guide
Set up your first multi-agent system in four phases:
Phase 1: Access Studio
Sign up and log in
Go to agents.kore.ai and create your account. After verifying your email, you land in Studio - the browser-based IDE where you build, test, and manage your agents.
Create your first project
Click New Project from the Studio dashboard. Give it a name like “My First Agents” and select a workspace. Projects organize your agents, supervisors, tools, and knowledge sources in one place.
Phase 2: Build your First Agent
Inside your project, click New Agent. Open the ABL editor and paste this definition:
AGENT: Support_Assistant
EXECUTION:
model: claude-sonnet-4-5-20250929
GOAL: |
Help customers with product questions. Be concise
and friendly. If you do not know the answer, say so.
PERSONA: |
Helpful product support assistant. Answers questions
clearly and concisely.
LIMITATIONS:
- "Cannot process payments or refunds"
- "Cannot access customer account information"
TOOLS:
search_knowledge(query: string) -> {results: object[], totalCount: number}
description: "Search the product knowledge base"
INSTRUCTIONS: |
1. Understand the customer's question
2. Search the knowledge base for relevant information
3. Provide a clear, sourced answer
4. If unsure, offer to connect with a human agent
- Uses an LLM to understand customer questions.
- Searches a knowledge base for answers.
- Responds with sourced information.
- Has clear boundaries on what it can and cannot do.
Click Save to validate your definition. Studio parses ABL in real time and flags syntax issues inline.
Phase 3: Test your Agent
Open the Test panel on the right side of Studio and send a message:
What is your return policy?
Phase 4: Add a Supervisor
Create a new Supervisor in your project and paste this definition:
SUPERVISOR: Product_Supervisor
EXECUTION:
model: claude-sonnet-4-5-20250929
GOAL: |
Route customer queries to the right specialist agent.
HANDOFF:
- TO: Support_Assistant
WHEN: user asks about products, features, or general help
PASS: query
- TO: Billing_Agent
WHEN: user asks about invoices, payments, or subscriptions
PASS: query
What Have You Built
In a few minutes, you created:
- An agent that understands natural language, retrieves knowledge, and enforces boundaries.
- A supervisor that routes messages to the right specialist.
- Observable traces for every execution step, visible right in Studio.
Next Step
Check out the Template Gallery in Studio for ready-made agent definitions across industries - airlines, retail, banking, telecom, travel, and more.
