Shards AI Agent System (shards-agent-core)

The Foundational Operating System for Autonomous Enterprise Intelligence.

The Shards AI Agent System is a modular, high-performance framework designed for Shards Inc / Shards Foundation. It provides the infrastructure to build, deploy, and orchestrate specialized autonomous agents that integrate seamlessly with the NVIDIA NemoClaw ecosystem and Shards Kernels.

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📑 Table of Contents

• Overview
• Core Architecture
• The "Claw" Ecosystem Integration
• Specialized Agents
• Multi-Agent Orchestration
• Installation & Setup
• Usage & Examples
• Security & Governance
• Documentation
• Contributing
• License

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🌟 Overview

In the rapidly evolving landscape of 2026, AI is shifting from static models to autonomous agents. The Shards AI Agent System is built to capture this shift by providing a robust runtime layer for enterprise-grade agents.

Key Capabilities:

• Deterministic Control: Integration with Shards Kernels for precise, verifiable operations.
• Advanced Reasoning: Powered by NVIDIA Nemotron models for complex, multi-step problem solving.
• Enterprise Orchestration: A sophisticated workflow engine that manages state across multiple specialized agents.
• MCP Native: Built-in support for the Model Context Protocol (MCP) to connect with any external tool or service.

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🏗 Core Architecture

The system follows a layered architecture designed for scalability, security, and modularity.

Application Layer (Enterprise Workflows)
    │
Orchestration Layer (WorkflowOrchestrator)
    │
Agent Layer (Kernel, Revenue, Nemotron, NemoClaw Agents)
    │
Core Engine (BaseAgent, Memory, Reasoning)
    │
Infrastructure Layer (MCP, NIM, Shards Kernels)

Components:

• core/: The heartbeat of the system. Contains the BaseAgent with integrated memory management and tool-calling logic.
• agents/: Specialized agent implementations with domain-specific logic.
• workflows/: State management and orchestration logic for multi-agent tasks.
• tools/: Standardized connectors, including the production-ready MCPClient.

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🦞 The "Claw" Ecosystem Integration

The Shards AI Agent System is fully compatible with the NVIDIA "Claw" ecosystem, enabling enterprise-scale deployment and high-performance inference.

System	Integration Level	Focus
NemoClaw	Platform Native	Enterprise agent platform and NIM container orchestration.
Nemotron	Reasoning Engine	Advanced reasoning, coding, and multi-step logic.
NIM	Inference Runtime	High-performance, containerized microservices for agent deployment.
NeMo	Toolkit Integration	Leveraging NVIDIA's agent toolkit for enhanced capabilities.

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🤖 Specialized Agents

The system includes a suite of pre-built agents, each optimized for specific enterprise functions:

1. KernelAgent: Manages Shards Foundation kernels. Focuses on deterministic control and security audits.
2. RevenueAgent: The financial brain. Analyzes ERP data, identifies growth drivers, and optimizes revenue workflows.
3. NemotronAgent: The logic specialist. Decomposes complex tasks and provides high-accuracy reasoning.
4. NemoClawAgent: The platform integrator. Manages agent lifecycles within the NVIDIA enterprise ecosystem.
5. OrchestratorAgent: The conductor. Coordinates the other agents to complete end-to-end business processes.

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🎼 Multi-Agent Orchestration

Orchestration is handled by the WorkflowOrchestrator, which manages a WorkflowState across multiple steps.

Example: Financial Closing Workflow

1. RevenueAgent pulls and analyzes Q1 data.
2. NemotronAgent predicts Q2 growth based on Q1 results.
3. KernelAgent verifies the integrity of the ledger on the Shards Kernel.
4. NemoClawAgent deploys the final executive report to the enterprise dashboard.

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🚀 Installation & Setup

Prerequisites

• Python 3.11 or higher
• Access to Shards Foundation MCP servers (optional for local testing)
• NVIDIA NIM API keys (for production deployment)

Local Setup

1. Clone the repository:

   git clone https://github.com/Shards-inc/shards-agent-core.git
   cd shards-agent-core

2. Install dependencies:

   pip install -r requirements.txt

3. Run the demo:

   python main.py

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📖 Usage & Examples

Creating a Custom Agent

from core.agent import BaseAgent, AgentResult

class MyCustomAgent(BaseAgent):
    async def run(self, task: str) -> AgentResult:
        self.log(f"Processing: {task}")
        # Your custom logic here
        return AgentResult(output="Task completed", status="success")

Executing a Workflow

from workflows.orchestration import WorkflowOrchestrator
from agents.shards_agents import RevenueAgent, NemotronAgent

orchestrator = WorkflowOrchestrator(agents=[RevenueAgent(), NemotronAgent()])
state = await orchestrator.run_enterprise_closing()
print(f"Workflow Status: {state.status}")

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🛡 Security & Governance

Security is baked into the core of the Shards AI Agent System:

• Sandboxed Execution: Agents operate in restricted environments.
• Deterministic Oversight: Integration with Kloc-kernel-level-oversight-council for policy enforcement.
• Audit Logging: Every thought, action, and observation is logged with timestamps for full traceability.
• Access Control: Enterprise-grade authentication for all MCP and NIM connections.

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📚 Documentation

Detailed documentation is available in the docs/ directory:

• Shards Agent Guide: Comprehensive system overview.
• API Reference: Detailed class and method documentation.
• Deployment Guide: Instructions for local, Docker, and Cloud (NIM) deployment.
• NemoClaw Integration: Deep dive into the NVIDIA ecosystem.
• Nemotron Integration: Leveraging advanced reasoning models.

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🤝 Contributing

We welcome contributions from the community! Please see our CONTRIBUTING.md for guidelines on how to get started. All contributors are expected to adhere to our CODE_OF_CONDUCT.md.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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© 2026 Shards Inc / Shards Foundation. Built with precision by Manus AI.