Add agentic tool-calling use-case example for Nemotron 3 Super

use-case-examples/Agentic-Tool-Calling-with-Nemotron-Super/README.md

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+# Agentic Tool-Calling with Nemotron 3 Super
+
+Build multi-step AI agents that plan, call tools, and synthesize results using Nemotron 3 Super's structured function-calling capabilities.
+
+## Overview
+
+This example demonstrates how to build agentic workflows with Nemotron 3 Super, progressing from simple tool calls to a fully autonomous agent loop:
+
+1. **Single Tool Call** - Model selects and invokes one function
+2. **Multi-Turn Tool Calling** - Model chains tool results across conversation turns
+3. **Autonomous Agent Loop** - Model plans a strategy, executes multiple tools, and synthesizes a final report
+4. **Reasoning Modes** - Compare `reasoning-off`, `regular`, and `low-effort` modes with tool calling
+
+## Models Used
+
+| Component | Model | Parameters | Deployment |
+|-----------|-------|------------|------------|
+| **Reasoning + Tool Calling** | `nvidia/nemotron-3-super-120b-a12b` | 120B total / 12B active | NVIDIA API or self-hosted (vLLM) |
+
+## Why Nemotron 3 Super for Agents?
+
+- **85.6% on PinchBench** - Best open model for agentic tasks
+- **Trained on 21 RL environments** including TerminalBench, TauBench V2, and SWE-Bench
+- **Structured tool calling** with JSON schema support via OpenAI-compatible API
+- **Three reasoning modes** for balancing speed vs. depth in tool-calling scenarios
+- **Hybrid Mamba-Transformer MoE** architecture delivers high throughput at inference time
+
+## Requirements
+
+- Python 3.10+
+- NVIDIA API Key ([get one here](https://build.nvidia.com/))
+
+## Quick Start
+
+```bash
+# Install dependencies
+pip install openai
+
+# Set your API key
+export NVIDIA_API_KEY="your-key-here"
+
+# Run the notebook
+jupyter notebook agentic_tool_calling_tutorial.ipynb
+```
+
+## What You'll Learn
+
+- How to define tools with JSON schema for Nemotron 3 Super
+- Building a tool-calling conversation loop with proper message threading
+- Implementing an autonomous agent that plans and executes multi-step tasks
+- Choosing the right reasoning mode for different agentic scenarios
+- Best practices for system prompts, error handling, and tool result formatting