We’re launching an open-source, supervised AI agent platform built for Human–AI collaboration.

TL;DR

🎯 Supervised Learning
As issues arise, data is labeled under human supervision and added to the agent’s knowledge base for continuous learning.

🛡️ Hallucination Control (Human-in-the-Loop)
Agents only respond when sufficient knowledge exists. If not, tasks are escalated to human supervisors.

⚡ Event-Driven Agentic Platform
Inspired by DDD, GreyCollar uses a platform layer to orchestrate tasks through decentralized, choreographed events.

🔗 GitHub: github.com/GreyCollar/GreyCollar

What are Supervised AI agents?

GreyCollar AI is a supervised AI agent platform for human–AI collaboration. The platform provides an environment to continuously learn from human supervisors, so they can adapt to real-world workloads.

Each AI colleague works within defined responsibilities and uses a knowledge base to complete tasks. When uncertain, they escalate to human supervisors—enabling "Hallucination Control" to prevent mistakes.

Human-AI Collabs (Human-in-the-Loop)

Human-in-the-Loop (HITL) is a collaborative approach where AI agents work alongside human experts to enhance decision-making, automate processes, and refine task execution. In this model, human supervision plays a key role in guiding, correcting, and improving AI-driven workflows.

• Improved Accuracy – Human feedback enables AI colleagues to refine responses in real time, reducing errors and increasing reliability.
• Continuous Learning – AI adapts to new tasks and domains by integrating ongoing human input, improving with every interaction.
• Safe & Responsible AI – Human oversight ensures ethical alignment, reduces bias, and mitigates unintended or harmful outputs.
• Operational Efficiency – AI handles routine, repetitive work at scale, freeing human supervisors to focus on higher-value, strategic decisions.

⚡n8n Integration

GreyCollar can be part of your favorite flow tools like n8n, enabling you to embed supervised AI directly into automated workflows.

Features

• Colleague (AI): AI assistants that handle tasks based on assigned responsibilities and knowledge.
• Supervising (Human): Humans who guide AI with feedback, questions, or extra info.
• Knowledge: The info AI uses—documents, FAQs, or other sources.
• Responsibility and Task: Defines what tasks the AI performs and how.
• Team: A group of AI colleagues for managing knowledge and leadership.
• Communication: How you interact with AI—via chat, email, Slack, WhatsApp, etc.
• Integration: Connects to third-party tools via Model Context Protocol (MCP).

Colleague (AI)

Colleagues are AI assistants that help you with your tasks based on responsibilities and knowledge. They are designed to:

• Complete tasks standalone for given responsibilities
• Continuously learn and persist to knowledge base
• Collaborate with other human supervisors or human colleagues

Supervising (Human)

Supervising by human is raised when the AI is not able to complete the task or needs human input. The supervisor can provide feedback, ask questions, or give additional information to help the AI complete the task.

⚠️ This is the core concept to eliminate hallucination that the AI evaluates knowledge existed before the execution of the task.

Knowledge

Knowledge is the information that the AI uses when working on responsibilities. It can be in the form of documents, FAQs, or any other.

Knowledge can be added manually or part of the supervising process during task execution.

Responsibility and Task

Responsibility is a blueprint of the tasks that the AI will perform based on knowledge. It defines what the AI can do and how it can help you.

Tasks are the actions that the AI performs for a given responsibility with knowledge. Once the task is initiated through communication, the AI will execute the task and provide feedback to the supervisor.

Team

Team is a logical grouping of AI colleagues. Mainly this grouping provides 2 major benefits:

• Knowledge Management: Knowledge can be shared between AI colleagues within the team, while each colleague can also maintain their own individual knowledge. In agentic AI, effective knowledge management is crucial to eliminate hallucinations, ensuring that each AI colleague has sufficient knowledge to complete tasks without being misled by irrelevant or unnecessary information. a
• Team Lead: The team lead is the person responsible for handing off the task to the AI colleagues.

Communication

Communication is the primary way to interact with AI colleagues. It can occur through various channels, such as chat, email, or voice, depending on the context and user preferences. These communication channels are linked to specific responsibilities that AI colleagues are capable of handling, ensuring interactions are efficient and task-relevant. Multiple channels can be used simultaneously, allowing for flexibility in how users engage with AI colleagues.

In short, communication opens up AI colleagues to the outside world, enabling them to perform tasks.

Integration

All integrations are based on MCP that allows you to connect to any third-party service. The integration can be used for bidirectional communication:

• Incoming: Pulling data such as reading from Google Drive or checking Google Calendar
• Outgoing: Sending data such as writing to Google Drive or posting to a Slack channel

Event-Driven Agentic AI Platform

GreyCollar is an Event-Driven AI Agent Platform designed for dynamic and adaptive AI workflows and autonomous decision-making. While frameworks like LangChain and LlamaIndex are specialized in creating static flows, but it is significantly more challenging to have flexible AI agent compared to event-drive architecture.

Key Advantages:

⚡ Dynamic Workflows:

• Instead of a rigid sequence of actions, GreyCollar agents react to events in real-time. These events could be anything: a new email, a sensor reading, a user interaction, or even the output of another AI agent.
• This allows for highly adaptable and context-aware behavior. The agent's next action is determined by the current situation, not a pre-programmed path.

🧠 Autonomous Decision-Making:

• Agents can make decisions without constant human intervention. They can monitor their environment, identify relevant events, and take appropriate actions based on predefined rules or learned behaviors.
• This is crucial for applications that require rapid response times or operate in dynamic environments.

🔄 Modularity and Scalability:

• Event-driven systems are naturally modular. Agents can be designed as independent components that communicate with each other through events.
• This makes it easier to build complex systems by combining smaller, specialized agents. It also allows for easier scaling, as new agents can be added without disrupting the existing system.

🔍 Real-time responsiveness:

• Because the system is based on events, it can react very quickly to changes in the enviroment. This is very important for applications that need to be up to date.

Hello World

Customer: "Do you have a parking spot at your store?"
> SESSION.USER_MESSAGED
{
  sessionId: "2116847c",
  content: "Do you ... at your store?"
}

AI: "Please wait a moment while working on the answer."
> SUPERVISING.RAISED
{
  sessionId: "2116847c",
  question: "Do you ... at your store?"
}

Supervisor: "Yes, we have a parking spot in the back of the store."
> SUPERVISING.ANSWERED
{
  sessionId: "2116847c",
  question: "Yes, we have ... of the store."
}

# Knowledge is stored for future reference. 🧠

AI: "Yes, we have a parking spot in the back of the store."

> SESSION.USER_MESSAGED
{
  sessionId: "2116847c",
  question: "Yes, we have ... of the store."
}

# A Few Moments Later... 🍍

Customer #2: "Planning to come down there, how is parking situation?"

> SESSION.USER_MESSAGED
{
  sessionId: "3746a52b",
  content: "Planning ... situation?"
}

AI: "Yes, most certainly, we have a parking spot in the back. 😎"
> SESSION.USER_MESSAGED
{
  sessionId: "3746a52b",
  question: "Yes, most ... in the back."
}

🚀 Join us on GitHub
Thanks to supervised learning, we’re taking a fresh approach to AI agents. Join us in shaping the future of human–AI collabs — we welcome all kinds of contributions!

🔗 GitHub: github.com/GreyCollar/GreyCollar