DEV Community: GreyCollar

How to Use AI Agents in Supply Chain & Logistics

Can Mingir — Thu, 31 Jul 2025 14:44:39 +0000

Before anything else, what are AI Agents?

AI Agents are autonomous versions of LLMs used for decision-making, and the main reason is they are highly effective at complex tasks by breaking them down into smaller, manageable steps. In addition, they can perform interactive actions such as retrieving data from databases, making API calls, or even reaching out to humans for additional information.

AI Agents are often compared with LLMs, but their usage in enterprise systems is very different. Today, microservices are considered best practice and can be written in multiple languages like Java or Python. Traditional coding is great at highly complex algorithm and deterministic requirements, but AI Agents can be replaced some of those microservices if particular module requires large number of business logic, especially those business rule often changed.

AI Agents can replace some of your microservices, especially where business rules change frequently.

This is especially true in Supply Chain & Logistics, where processes like inventory management, order fulfillment, and route planning rely on countless business rules. These rules often vary from one customer to another and change frequently due to seasonal demand, market fluctuations, or regulatory updates. AI Agents are particularly useful in these scenarios because they can adapt quickly to evolving requirements, reducing the need for constant manual intervention or system reconfiguration.

Which case applies to you?

When deciding between traditional coding and AI Agents for a microservice, the choice largely depends on the nature of your business logic and system requirements. Traditional coding works best when dealing with a small number of complex, deterministic rules. However, it comes with higher maintenance costs, as every change requires manual updates, testing, and deployment. In contrast, AI Agents excel when there are numerous basic rules that frequently change. They offer dynamic adaptability, learning from feedback and new data to adjust logic without heavy developer intervention. Their goal-based approach reduces maintenance costs and allows for error handling through supervised learning, making them ideal for fast-evolving environments like Supply Chain & Logistics.

The relentless growth of e-commerce has placed an unsustainable strain on traditional, manual methods of product catalog management. The imperative to provide rich, accurate, and engaging product information at scale has created a critical need for intelligent automation solutions that can navigate the complex trade-offs between creative quality, factual integrity, operational speed, and cost.
— Ryan M., Cloud Principal Architect, Retail at Google

Conclusion

As Supply Chains & Logistics grow more dynamic and complex, relying solely on traditional coding for every business rule update can slow you down. AI Agents offer a powerful alternative—bringing flexibility, adaptability, and intelligence to processes that demand constant change. Whether your goal is to boost automation, lower maintenance costs, or build resilience, now is the perfect time to explore where AI Agents can fit into your architecture.

P.S. coding is still here, not going anywhere 😎

📢 GreyCollar: Supervised Agentic AI Project

Can Mingir — Wed, 25 Jun 2025 16:28:06 +0000

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

GreyCollar: Supervised AI Agent | Human-AI Collabs

Can Mingir — Wed, 26 Feb 2025 16:44:08 +0000

Hello y'all, we are launching a project for Human-AI collabs with supervised learning capabilities. You're more than welcome to jump in and brainstorm with us!

What is Supervised AI Agent?

GreyCollar is a Supervised AI Agent that functions under human guidance and feedback, operating within a supervised learning framework. It is trained on labeled data, where each input is paired with a corresponding correct output, enabling the model to learn from explicit examples and improve decision-making accuracy.

Designed for human-AI collaboration (Human-in-the-Loop), GreyCollar is highly effective in scenarios that require data-driven decision-making, automation, and real-time adaptability. It incorporates human-in-the-loop mechanisms, enabling iterative learning through continuous feedback and model adjustments. This enhances its ability to handle complex tasks at work environment.

Autonomous Workflow

Autonomous workflows are self-driven processes where AI agents can independently execute multi-step tasks with human supervision. These workflows integrate task planning, execution, decision-making, and learning based on changing inputs or goals.

Task Decomposition
- The AI agent breaks down complex goals into smaller, executable steps.
- Uses methods like hierarchical planning or task graphs.
Decision-Making & Adaptation
- Dynamically adjusts workflows based on new information.
- Uses supervised learning to adapt itself to workspace-related tasks and directions.
Memory & Context Awareness
- Agents retain context across workflow steps.
- Utilizes vector databases, episodic memory, or long-term storage.
Multi-Agent Coordination
- Multiple AI agents collaborate by delegating and verifying tasks.
- Uses shared knowledge bases to improve coordination and efficiency.
Human-in-the-Loop & Supervised Learning
- Uses human feedback to improve models through supervised learning.
- Helps refine edge cases and prevents unintended consequences.

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

Benefits

Enhanced Accuracy – Human feedback helps the AI refine its responses and correct errors in real-time.
Adaptive Learning – AI models improve continuously by integrating human insights, ensuring adaptability to evolving tasks.
Safe AI – Human oversight prevents biases, ensures fairness, and mitigates unintended consequences.
Task Optimization – AI streamlines repetitive processes while humans focus on strategic and complex decision-making.

Welcome! I’ve been expecting you—"Skynet was gone. And now one road has become many." 🌐

The future is building up! AI Agents are now an emerging field within AI communities and marks a crucial milestone on the journey to AGI. Just like any other tooling in computer science, we must be mindful of when and where to use them. LangChain, or LlamaIndex might be a better fit if your application has a static flow—where AI doesn't need to make dynamic decisions—like in ChatBots, RAG etc. That said, if your business rules are well-defined and deterministic, there’s no shame in coding them directly!

However, if you need continuous learning combined with autonomous decision-making—in other words, true AI Agent—GreyCollar may suit you well. Choosing the right tool for the job is key.

Can Mingir
@canmingir

Get Started

git clone https://github.com/greycollar/greycollar.git
npm install

// Add env variables
JWT_SECRET=<JWT_SECRET>
LLM=OPENAI
OAUTH_CLIENT_SECRET=<OAUTH_CLIENT_SECRET>
OPENAI_API_KEY=<OPENAI_API_KEY>

npm start

This will start three applications simultaneously: Dashboard, API Server and Proxy Server. Once started, the dashboard should be accessible in your browser at http://localhost:3000

Learn more at https://greycollar.ai/docs

Features

Teams and Colleagues

In GreyCollar, Colleagues aka AI agents are organized into structured teams based on their areas of expertise and operational roles. This hierarchical framework ensures that AI agents work efficiently within the organization, providing clarity and structure to their contributions.

One of the biggest challenges in AI agents swarms in the workplace is preventing knowledge management. GreyCollar’s structured hierarchy provides visibility, clarity, and organizational coherence, allowing AI agents to seamlessly adapt to day-to-day tasks with minimal human supervision.

Async Chat (Email, Slack, WhatsApp...)

GreyCollar is designed as a standalone AI addition to operational workspaces, where efficient communication is essential for daily task execution. Unlike traditional chatbots, GreyCollar’s AI agents require ongoing, context-aware communication to function effectively within dynamic team environments.

The Async Chat feature enables AI agents to engage in continuous, asynchronous communication—both with human team members and other AI colleagues. This ensures that agents can participate actively in task execution, collaborate with teammates, and respond to evolving instructions over time.

Task Execution

Task flows in GreyCollar is automatically generated by the AI, allowing it to dynamically adjust its actions based on real-time data and predefined objectives. The agent continuously monitors inputs, applies decision-making algorithms, and initiates actions to fulfill its goals efficiently. This autonomous process enables the agent to handle complex tasks, adapt to changing conditions, and optimize performance across various applications, such as automated customer support, predictive maintenance, or autonomous driving systems.

https://github.com/user-attachments/assets/5111dfb1-687e-4fe6-a6bc-9c5e1a11abd4

Team Chat

Team Chat is an internal communication tool designed for interaction between supervisors and AI Colleagues. It enables human supervisors to engage directly with AI agents for a variety of purposes—whether it’s assigning tasks, asking questions, providing new information, or offering real-time feedback.

This feature transforms AI agents from passive tools into active collaborators, creating a dynamic environment where human expertise and AI capabilities work together effortlessly. Team Chat is also fully integrated with Slack, allowing users to communicate with AI agents within familiar workflows without the need for additional tools or platforms.

AI Marketplace

You can use any LLM, or even bring your own—we support and welcome them all. 🚀

Event-Driven AI Agent Platform

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."
}

⭐️ Star us on GitHub for the support

Thanks to supervising learning, we have a brand-new approach to AI Agents. Join us in shaping the future of AI! We welcome all kinds of contributions!

Join us at
https://github.com/greycollar/greycollar

greycollar / greycollar

Supervised AI Agent 🌎 Human-AI Collabs | Autonomous Workflow | Human-in-the-Loop | Async Communication | Open-ended Task Execution

GreyCollar

Supervised AI Agent

What is Supervised AI Agent?

Autonomous Workflow

Task Decomposition
- The AI agent breaks down complex goals into smaller, executable steps.
- Uses methods like hierarchical planning…

View on GitHub