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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.

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Creating Agents

Agents are the autonomous units of your application. Each agent has a role, an LLM for decision-making, tools to act with, and a prompt that defines its behavior.

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Prerequisites

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Agent types

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Autonomous agents

AI-powered agents that use LLM-based decision-making. Most agents use this type: 
from agenticai_core.designtime.models.agent import Agent
from agenticai_core.designtime.models.llm_model import LlmModel, LlmModelConfig
from agenticai_core.designtime.models.prompt import Prompt

agent = Agent(
    name="FinanceAssist",
    description="Banking assistant for account management",
    role="WORKER",
    sub_type="REACT",
    type="AUTONOMOUS",
    llm_model=LlmModel(
        model="gpt-4o",
        provider="Open AI",
        connection_name="Default Connection",
        modelConfig=LlmModelConfig(
            temperature=0.7,
            max_tokens=1600
        )
    ),
    prompt=Prompt(
        system="You are a helpful banking assistant.",
        custom="Assist with account inquiries and transactions."
    )
)

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Proxy agents

Agents that delegate processing to an external system: 
agent = Agent(
    name="ExternalService",
    description="Integrates with external API",
    role="WORKER",
    sub_type="PROXY",
    type="AUTONOMOUS",
    llm_model=...,
    prompt=...
)

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Agent sub-types

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REACT (ReAct pattern)

The REACT sub-type uses a Reasoning + Acting loop. The agent iterates through:
  1. Reason about the task.
  2. Select an action (tool).
  3. Observe the result.
  4. Continue until complete.
Best suited for general-purpose tasks, interactive problem-solving, and tool-heavy workflows. 
agent = Agent(
    name="ReactAgent",
    sub_type="REACT",
    ...
)

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Agent roles

RoleDescription
WORKERExecutes specific tasks delegated by the orchestrator.
SUPERVISORCoordinates other agents; typically used for the orchestrator agent.
# Worker
agent = Agent(
    name="BillingWorker",
    role="WORKER",
    description="Handles billing and payment tasks",
    ...
)

# Supervisor
agent = Agent(
    name="Supervisor",
    role="SUPERVISOR",
    description="Routes requests to appropriate workers",
    ...
)

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Add tools to agents

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Builder pattern

from agenticai_core.designtime.models.agent import AgentBuilder
from agenticai_core.designtime.models.tool import Tool

# Define tools
tool1 = Tool(name="GetBalance", type="toolLibrary", ...)
tool2 = Tool(name="Transfer", type="toolLibrary", ...)

# Build agent with tools
agent_config = AgentBuilder() \
    .set_name("BankingAgent") \
    .set_description("Banking operations agent") \
    .set_role("WORKER") \
    .set_sub_type("REACT") \
    .set_type("AUTONOMOUS") \
    .set_llm_model(llm_model) \
    .set_prompt(prompt) \
    .set_tool(tool1) \
    .set_tool(tool2) \
    .build()

agent = Agent(**agent_config)

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Direct assignment

agent = Agent(
    name="BankingAgent",
    tools=[tool1, tool2, tool3],
    ...
)

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Configure prompts

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Basic prompt

prompt = Prompt(
    system="You are a helpful assistant.",
    custom="You help with banking operations."
)

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With instructions

prompt = Prompt(
    system="You are a banking assistant.",
    custom="""You help customers manage accounts and transactions.

    ## Your Capabilities
    - Check account balances
    - Process transactions
    - Answer policy questions
    """,
    instructions=[
        "Never ask for passwords or PINs",
        "Always confirm transaction amounts",
        "Be professional and courteous"
    ]
)

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With memory context

Use {{memory.store.field}} template variables to inject stored data directly into the prompt, reducing unnecessary tool calls: 
prompt = Prompt(
    system="You are a helpful assistant.",
    custom="""You have access to customer information:

    <AccountInfo>
    {{memory.accountInfo.accounts}}
    </AccountInfo>

    Use this to provide quick responses without invoking tools.
    """
)
For full prompt and LLM configuration options, see Prompts and LLM Configuration.

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Additional configuration

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Agent icon

from agenticai_core.designtime.models.icon import Icon

agent = Agent(
    name="FinanceAssist",
    icon=Icon(
        name="avatar3",
        color="#D9D6FE",
        type="human"
    ),
    ...
)

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Real-time (voice/audio)

agent = Agent(
    name="VoiceAgent",
    is_real_time_enabled=True,
    ...
)

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Custom metadata

agent = Agent(
    name="CustomAgent",
    metadata={
        "versionInfo": "Draft version",
        "customField": "value"
    },
    ...
)

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Register with the orchestrator

Convert an Agent to a lightweight AgentMeta for orchestrator registration: 
# Convert full Agent to lightweight AgentMeta
agent_meta = agent.to_agent_meta()

# Use in orchestrator
app_agents = [agent.to_agent_meta() for agent in agents]
orchestrator = CustomOrchestrator(agents=app_agents)

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Best practices

  • Naming: Use descriptive names that reflect the agent’s purpose. The orchestrator uses names for routing.
  • Descriptions: Write detailed capability descriptions so the orchestrator knows when to use the agent. Include known limitations and constraints.
  • LLM configuration: Match temperature to the task type. Set token limits appropriate to expected response length. Consider cost versus quality trade-offs.
  • Prompts: Be specific about the agent’s role. Include relevant context and clear guidelines. Use memory template variables to reduce unnecessary tool calls.
  • Tools: Only assign tools relevant to the agent’s scope. Avoid tool overload — too many tools increase latency and reduce routing precision.
  • Testing: Test agents individually before wiring them into a multi-agent system. Verify tool integration, memory access, and prompt effectiveness separately.