Documentation Index
Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Understand the building blocks of the Agent Platform.
Agentic Apps
An agentic app is an autonomous system that uses AI agents to understand user intent, reason about tasks, and take actions to deliver outcomes.
Unlike traditional chatbots that follow predefined scripts, agentic apps:
- Reason dynamically about how to approach tasks
- Select tools based on context and user needs
- Coordinate multiple agents for complex workflows
- Maintain state across conversations
Traditional Bot Agentic App
──────────── ───────────────
User Input User Input
↓ ↓
Match Intent Understand Intent
↓ ↓
Execute Script Reason & Plan
↓ ↓
Return Response Select Agent(s)
↓
Invoke Tools
↓
Generate Response
Agents
An agent is a specialized AI worker with a defined scope, instructions, and access to specific tools and knowledge.
Agent Components
| Component | Purpose |
|---|
| Profile | Name, description, and avatar that identify the agent |
| AI Model | The LLM that powers reasoning and generation |
| Scope | Boundaries defining what tasks the agent handles |
| Instructions | Behavioral guidelines and response patterns |
| Tools | Actions the agent can perform |
| Knowledge | Information sources for RAG retrieval |
Example Agent Definition
name: Billing Agent
description: Handles billing inquiries, payment processing, and invoice management
scope: |
Responsible for:
- Payment status inquiries
- Invoice generation and retrieval
- Payment method updates
- Refund processing
instructions: |
- Verify customer identity before discussing billing details
- Always confirm transaction amounts before processing
- Provide clear breakdowns of charges when asked
- Escalate disputed charges over $500 to a human agent
tools:
- get_payment_status
- generate_invoice
- process_refund
- update_payment_method
knowledge:
- billing_faqs
- pricing_documentation
Orchestrator
The orchestrator is the central coordinator that manages how agents work together. It:
- Interprets user requests to understand intent
- Delegates tasks to appropriate agents
- Coordinates multi-agent workflows
- Resolves conflicts between agent outputs
- Validates responses before delivery
Orchestration Patterns
| Pattern | Best For |
|---|
| Single Agent | Simple, focused use cases with one domain |
| Supervisor | Complex tasks requiring parallel agent coordination |
| Adaptive Network | Dynamic hand-offs between specialized agents |
- Workflow Tools: Visual, no-code tools built with a drag-and-drop interface. Best for well-defined processes that benefit from visual traceability.
Example: A policy lookup flow that checks user eligibility, fetches the relevant policy details, and returns a formatted response.
Start → Check eligibility → Fetch Details → Format Response → End
- Code Tools: Custom JavaScript or Python functions for complex logic. Best for dynamic processing, calculations, or custom integrations.
def calculate_shipping(weight: float, destination: str) -> dict:
rate = get_shipping_rate(destination)
cost = weight * rate
return {"cost": cost, "estimated_days": get_delivery_estimate(destination)}
- MCP Tools: Remote functions exposed via Model Context Protocol servers. Best for shared toolsets and enterprise integrations.
{
"server": "https://tools.company.com/mcp",
"tools": ["crm_lookup", "inventory_check", "order_create"]
}
1. User: "What's my account balance?"
2. Agent identifies need for external data
└─ Selected tool: get_account_balance
3. Agent prepares parameters
└─ { "user_id": "usr_123" }
4. Platform invokes the tool
└─ API call to banking system
5. Tool returns result
└─ { "balance": 1234.56, "currency": "USD" }
6. Agent generates response
└─ "Your current account balance is $1,234.56"
Parallel vs Sequential Calling
Sequential: Tools execute one after another when outputs are dependent.
get_user_profile → get_account_balance → format_statement
┌─ get_account_balance ─┐
├─ get_recent_transactions ─┼─→ combine_results
└─ get_rewards_points ──┘
Knowledge
Knowledge connects agents to your data sources, enabling context-aware responses through Retrieval-Augmented Generation (RAG).
How Knowledge Works
User Query: "What's your return policy for electronics?"
1. Query embedded into vector representation
2. Semantic search across knowledge base
3. Relevant chunks retrieved (return policy docs)
4. Context injected into agent prompt
5. Agent generates accurate, grounded response
Knowledge Sources
- Document uploads (PDF, DOCX, TXT)
- Web crawlers
- Third-party applications such as Confluence, SharePoint, Google Drive
- Structured Data
- Custom connectors
State Management
Agentic apps maintain state to enable natural, multi-turn conversations.
State Types
Short-term (Session)
Temporary context within a conversation:
- Current order being discussed
- Selected product options
- Pending confirmations
Long-term (Memory)
Persistent preferences and history:
- User preferences
- Past interactions
- Account information
Context Window
The context window limits how many messages agents retain. Configure it based on your use case.
| Setting | Messages | Use Case |
|---|
| Minimal | 25 | Simple Q&A, transactional |
| Default | 50 | General conversations |
| Extended | 100 | Complex multi-step workflows |
| Maximum | 200 | Deep contextual discussions |
