How to Use the UKG Pro Learning MCP in AutoGen

Achieve consensus on UKG Pro Learning status using AutoGen.

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Connect UKG Pro Learning MCP to AutoGen

Create your Vinkius account to connect UKG Pro Learning to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup UKG Pro Learning with AutoGen

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Perplexity

Debating User Access Requirements

Set up agents: one checks user identity via `users`, and another agent verifies if the user is properly enrolled using `enrollments`. They debate which access level is appropriate based on the conflicting data points. The resulting consensus dictates a final, agreed-upon action.

Negotiating Optimal Curriculum Changes

Assign one agent to list existing curricula (`curricula`). Assign another agent to check for prerequisite courses using `courses`. They argue over whether the current curriculum structure is optimal or if a change is necessary. This process forces deliberation between competing knowledge sources.

Automating Training Audit Deliberation

One agent pulls all available user data with `users`, while a second agent checks the history using `enrollments`. They challenge each other's findings—for instance, if an employee is listed as active but has no recent enrollments. The final decision is based on resolving these conflicting reports.

Setup guide

Set up UKG Pro Learning MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes UKG Pro Learning tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="UKG Pro Learning_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent UKG Pro Learning data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="UKG Pro Learning_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent UKG Pro Learning data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by UKG Pro Learning. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about UKG Pro Learning MCP in AutoGen

Instead of just executing a tool, AutoGen uses multiple agents that discuss the outcomes. For example, they might use `curricula` and then debate which specific courses are most relevant to an employee's role.

Yes. You build systems where the answer isn't obvious—it requires deliberation between multiple perspectives, such as comparing data from `users` and `enrollments` to determine compliance.

The server provides access to user details, course definitions, curriculum structures, and enrollment records. These are the facts that the debating agents must work with.

It requires it. Each agent might call a different tool—one calling `users`, another calling `courses`—and they use those diverse outputs to debate and converge on one final answer.

The benefit is consensus-driven decision making. You get a resolution that has been challenged and confirmed by multiple specialized agents, ensuring accuracy for UKG Pro Learning processes.

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