Common Mistakes Orgs Make When Adopting Agentic AI
Agentic AI is quickly becoming one of the most talked-about innovations in enterprise technology. Unlike traditional automation tools, agentic systems can plan, reason, take initiative, and execute complex multi-step tasks with minimal human intervention. Powered by advances in large language models and autonomous decision frameworks, agentic AI promises to transform how organizations operate. But here’s the hard truth: many organizations rush into adoption without fully understanding what they’re implementing. The result? Wasted budgets, frustrated teams, compliance risks, and AI initiatives that quietly fade away. If your organization is exploring or actively implementing agentic AI, understanding the common pitfalls can save you time, money, and reputation. Below are the most frequent mistakes companies make — and what to do instead. https://dellenny.com/common-mistakes-orgs-make-when-adopting-agentic-ai/Copilot for Planner and To Do Automating Task Management
In today’s fast-paced digital workplace, managing tasks effectively is one of the most important skills for productivity and team success. Between meetings, emails, chats, and deadlines, it’s easy for key action items to fall through the cracks. Microsoft’s latest innovation—Copilot for Planner and To Do—aims to change that by using artificial intelligence to automate and simplify task management. This powerful integration between Microsoft 365 Copilot, Planner, and To Do allows individuals and teams to automatically generate, organize, and monitor tasks using natural language. Instead of spending hours setting up task lists or following up manually, you can let Copilot do the heavy lifting. https://dellenny.com/copilot-for-planner-and-to-do-how-ai-automates-task-management/Optimizing Network and Connectivity for Copilot Performance
In today’s AI-driven workplace, tools like Microsoft Copilot are quickly becoming essential for productivity. Whether you’re drafting documents, analyzing spreadsheets, summarizing meetings, or generating code, Copilot relies heavily on fast, stable network connectivity. Yet many organizations focus on licensing and deployment while overlooking one critical component: network optimization. If Copilot feels slow, inconsistent, or unreliable, the issue is often not the AI itself it’s the network. In this guide, we’ll walk through how to optimize network and connectivity for Copilot performance, explore technical configuration steps, and share practical best practices to ensure your users experience seamless AI assistance. https://dellenny.com/optimizing-network-and-connectivity-for-copilot-performance/Upcoming webinar: Maximize the Cost Efficiency of AI Agents on Azure
AI agents are quickly becoming central to how organizations automate work, engage customers, and unlock new insights. But as adoption accelerates, so do questions about cost, ROI, and long-term sustainability. That’s exactly what the Maximize the Cost Efficiency of AI Agents on Azure webinar is designed to address. The webinar will provide practical guidance on building and scaling AI agents on Azure with financial discipline in mind. Rather than focusing only on technology, the session helps learners connect AI design decisions to real business outcomes—covering everything from identifying high-impact use cases and understanding cost drivers to forecasting ROI. Whether you’re just starting your AI journey or expanding AI agents across the enterprise, the session will equip you with strategies to make informed, cost-conscious decisions at every stage—from architecture and model selection to ongoing optimization and governance. Who should attend? If you are in one of these roles and are a decision maker or can influence decision makers in AI decisions or need to show ROI metrics on AI, this session is for you. Developer Administrator Solution Architect AI Engineer Business Analyst Business User Technology Manager Why attending the webinar? In the webinar, you’ll hear how to translate theory into real-world scenarios, walk through common cost pitfalls, and show how organizations are applying these principles in practice. Most importantly, the webinar helps you connect the dots faster, turning what you’ve learned into actionable insights you can apply immediately, ask questions live, and gain clarity on how to maximize ROI while scaling AI responsibly. If you care about building AI agents that are not only innovative but also efficient, governable, and financially sustainable, this training—and this webinar that complements it—are well worth your time. Register for the free webinar today for the event on March 5, 2026, 8:00 AM - 9:00 AM (UTC-08:00) Pacific Time (US & Canada). Who will speak at the webinar? Your speakers will be: Carlotta Castelluccio: Carlotta is a Senior AI Advocate with the mission of helping every developer to succeed with AI, by building innovative solutions responsibly. To achieve this goal, she develops technical content, and she hosts skilling sessions, enabling her audience to take the most out of AI technologies and to have an impact on Microsoft AI products’ roadmap. Nitya Narasimhan: Nitya is a PhD and Polyglot with 25+ years of software research & development experience spanning mobile, web, cloud and AI. She is an innovator (12+ patents), a visual storyteller (@sketchtedocs), and an experienced community builder in the Greater New York area. As a senior AI Advocate on the Core AI Developer Relations team, she acts as "developer 0" for the Microsoft Foundry platform, providing product feedback and empowering AI developers to build trustworthy AI solutions with code samples, open-source curricula and content-initiatives like Model Mondays. Prior to joining Microsoft, she spent a decade in Motorola Labs working on ubiquitous & mobile computing research, founded Google Developer Groups in New York, and consulted for startups building real-time experiences for enterprise. Her current interests span Model understanding & customization, E2E Observability & Safety, and agentic AI workflows for maintainable software. Moderator Lee Stott is a Principal Cloud Advocate at Microsoft, working in the Core AI Developer Relations Team. He helps developers and organizations build responsibly with AI and cloud technologies through open-source projects, technical guidance, and global developer programs. Based in the UK, Lee brings deep hands-on experience across AI, Azure, and developer tooling. .Data Boundaries and Permissions in an Agentic Copilot World
We’re entering a new era of AI one where copilots don’t just answer questions but take action. They schedule meetings, update CRM records, draft contracts, analyze dashboards, trigger workflows, and even coordinate across tools. These “agentic” copilots move beyond passive assistance into active participation. But as AI systems become more capable and autonomous but a critical question emerges: Where are the data boundaries? And who controls permissions in an agentic copilot world? If your AI can act on your behalf, it must also respect the same guardrails you would. Otherwise, the promise of productivity quickly turns into a governance nightmare. Let’s dig what data boundaries mean in this new landscape and how organizations can think clearly about permissions before scaling AI agents across their systems. https://dellenny.com/data-boundaries-and-permissions-in-an-agentic-copilot-world/Complete Guide to Deploying OpenClaw on Azure Windows 11 Virtual Machine
1. Introduction to OpenClaw OpenClaw is an open-source AI personal assistant platform that runs on your own devices and executes real-world tasks. Unlike traditional cloud-based AI assistants, OpenClaw emphasizes local deployment and privacy protection, giving you complete control over your data. Key Features of OpenClaw Cross-Platform Support: Runs on Windows, macOS, Linux, and other operating systems Multi-Channel Integration: Interact with AI through messaging platforms like WhatsApp, Telegram, and Discord Task Automation: Execute file operations, browser control, system commands, and more Persistent Memory: AI remembers your preferences and contextual information Flexible AI Backends: Supports multiple large language models including Anthropic Claude and OpenAI GPT OpenClaw is built on Node.js and can be quickly installed and deployed via npm. 2. Security Advantages of Running OpenClaw on Azure VM Deploying OpenClaw on an Azure virtual machine instead of your personal computer offers significant security benefits: 1. Environment Isolation Azure VMs provide a completely isolated runtime environment. Even if the AI agent exhibits abnormal behavior or is maliciously exploited, it won't affect your personal computer or local data. This isolation mechanism forms the foundation of a zero-trust security architecture. 2. Network Security Controls Through Azure Network Security Groups (NSGs), you can precisely control which IP addresses can access your virtual machine. The RDP rules configured in the deployment script allow you to securely connect to your Windows 11 VM via Remote Desktop while enabling further restrictions on access sources. 3. Data Persistence and Backup Azure VM managed disks support automatic snapshots and backups. Even if the virtual machine encounters issues, your OpenClaw configuration and data remain safe. 4. Elastic Resource Management You can adjust VM specifications (memory, CPU) at any time based on actual needs, or stop the VM when not in use to save costs, maintaining maximum flexibility. 5. Enterprise-Grade Authentication Azure supports integration with Azure Active Directory (Entra ID) for identity verification, allowing you to assign different access permissions to team members for granular access control. 6. Audit and Compliance Azure provides detailed activity logs and audit trails, making it easy to trace any suspicious activity and meet enterprise compliance requirements. 3. Deployment Steps Explained This deployment script uses Azure CLI to automate the installation of OpenClaw and its dependencies on a Windows 11 virtual machine. Here are the detailed execution steps: Prerequisites Before running the script, ensure you have: Install Azure CLI # Windows users can download the MSI installer https://aka.ms/installazurecliwindows # macOS users brew install azure-cli # Linux users curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash 2. Log in to Azure Account az login 3. Prepare Deployment Script Save the provided deploy-windows11-vm.sh script locally and grant execute permissions: chmod +x deploy-windows11-vm.sh Step 1: Configure Deployment Parameters The script begins by defining key configuration variables that you can modify as needed: RESOURCE_GROUP="Your Azure Resource Group Name" # Resource group name VM_NAME="win11-openclaw-vm" # Virtual machine name LOCATION="Your Azure Regison Name" # Azure region ADMIN_USERNAME="Your Azure VM Administrator Name" # Administrator username ADMIN_PASSWORD="our Azure VM Administrator Password" # Administrator password (change to a strong password) VM_SIZE="Your Azure VM Size" # VM size (4GB memory) Security Recommendations: Always change ADMIN_PASSWORD to your own strong password Passwords should contain uppercase and lowercase letters, numbers, and special characters Never commit scripts containing real passwords to code repositories Step 2: Check and Create Resource Group The script first checks if the specified resource group exists, and creates it automatically if it doesn't: echo "Checking resource group $RESOURCE_GROUP..." az group show --name $RESOURCE_GROUP &> /dev/null if [ $? -ne 0 ]; then echo "Creating resource group $RESOURCE_GROUP..." az group create --name $RESOURCE_GROUP --location $LOCATION fi A resource group is a logical container in Azure used to organize and manage related resources. All associated resources (VMs, networks, storage, etc.) will be created within this resource group. Step 3: Create Windows 11 Virtual Machine This is the core step, using the az vm create command to create a Windows 11 Pro virtual machine: az vm create \ --resource-group $RESOURCE_GROUP \ --name $VM_NAME \ --image MicrosoftWindowsDesktop:windows-11:win11-24h2-pro:latest \ --size $VM_SIZE \ --admin-username $ADMIN_USERNAME \ --admin-password $ADMIN_PASSWORD \ --public-ip-sku Standard \ --nsg-rule RDP Parameter Explanations: --image: Uses the latest Windows 11 24H2 Professional edition image --size: Standard_B2s provides 2 vCPUs and 4GB memory, suitable for running OpenClaw --public-ip-sku Standard: Assigns a standard public IP --nsg-rule RDP: Automatically creates network security group rules allowing RDP (port 3389) inbound traffic Step 4: Retrieve Virtual Machine Public IP After VM creation completes, the script retrieves its public IP address: PUBLIC_IP=$(az vm show -d -g $RESOURCE_GROUP -n $VM_NAME --query publicIps -o tsv) echo "VM Public IP: $PUBLIC_IP" This IP address will be used for subsequent RDP remote connections. Step 5: Install Chocolatey Package Manager Using az vm run-command to execute PowerShell scripts inside the VM, first installing Chocolatey: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString( 'https://community.chocolatey.org/install.ps1'))" Chocolatey is a package manager for Windows, similar to apt or yum on Linux, simplifying subsequent software installations. Step 6: Install Git Git is a dependency for many npm packages, especially those that need to download source code from GitHub for compilation: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "C:\ProgramData\chocolatey\bin\choco.exe install git -y" Step 7: Install CMake and Visual Studio Build Tools Some of OpenClaw's native modules require compilation, necessitating the installation of C++ build toolchain: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "C:\ProgramData\chocolatey\bin\choco.exe install cmake visualstudio2022buildtools visualstudio2022-workload-vctools -y" Component Descriptions: cmake: Cross-platform build system visualstudio2022buildtools: VS 2022 Build Tools visualstudio2022-workload-vctools: C++ development toolchain Step 8: Install Node.js LTS Install the Node.js Long Term Support version, which is the core runtime environment for OpenClaw: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "$env:Path = [System.Environment]::GetEnvironmentVariable('Path','Machine') + ';' + [System.Environment]::GetEnvironmentVariable('Path','User'); C:\ProgramData\chocolatey\bin\choco.exe install nodejs-lts -y" The script refreshes environment variables first to ensure Chocolatey is in the PATH, then installs Node.js LTS. Step 9: Globally Install OpenClaw Use npm to globally install OpenClaw: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "$env:Path = [System.Environment]::GetEnvironmentVariable('Path','Machine') + ';' + [System.Environment]::GetEnvironmentVariable('Path','User'); npm install -g openclaw" Global installation makes the openclaw command available from anywhere in the system. Step 10: Configure Environment Variables Add Node.js and npm global paths to the system PATH environment variable: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts " $npmGlobalPath = 'C:\Program Files\nodejs'; $npmUserPath = [System.Environment]::GetFolderPath('ApplicationData') + '\npm'; $currentPath = [System.Environment]::GetEnvironmentVariable('Path', 'Machine'); if ($currentPath -notlike \"*$npmGlobalPath*\") { $newPath = $currentPath + ';' + $npmGlobalPath; [System.Environment]::SetEnvironmentVariable('Path', $newPath, 'Machine'); Write-Host 'Added Node.js path to system PATH'; } if ($currentPath -notlike \"*$npmUserPath*\") { $newPath = [System.Environment]::GetEnvironmentVariable('Path', 'Machine') + ';' + $npmUserPath; [System.Environment]::SetEnvironmentVariable('Path', $newPath, 'Machine'); Write-Host 'Added npm global path to system PATH'; } Write-Host 'Environment variables updated successfully!'; " This ensures that node, npm, and openclaw commands can be used directly even in new terminal sessions. Step 11: Verify Installation The script finally verifies that all software is correctly installed: az vm run-command invoke -g $RESOURCE_GROUP -n $VM_NAME --command-id RunPowerShellScript \ --scripts "$env:Path = [System.Environment]::GetEnvironmentVariable('Path','Machine') + ';' + [System.Environment]::GetEnvironmentVariable('Path','User'); Write-Host 'Node.js version:'; node --version; Write-Host 'npm version:'; npm --version; Write-Host 'openclaw:'; npm list -g openclaw" Successful output should look similar to: Node.js version: v20.x.x npm version: 10.x.x openclaw: openclaw@x.x.x Step 12: Connect to Virtual Machine After deployment completes, the script outputs connection information: ============================================ Deployment completed! ============================================ Resource Group: Your Azure Resource Group Name VM Name: win11-openclaw-vm Public IP: xx.xx.xx.xx Admin Username: Your Administrator UserName VM Size: Your VM Size Connect via RDP: mstsc /v:xx.xx.xx.xx ============================================ Connection Methods: Windows Users: Press Win + R to open Run dialog Enter mstsc /v:public_ip and press Enter Log in using the username and password set in the script macOS Users: Download "Windows App" from the App Store Add PC connection with the public IP Log in using the username and password set in the script Linux Users: # Use Remmina or xfreerdp xfreerdp /u:username /v:public_ip Step 13: Initialize OpenClaw After connecting to the VM, run the following in PowerShell or Command Prompt # Initialize OpenClaw openclaw onboard # Configure AI model API key # Edit configuration file: C:\Users\username\.openclaw\openclaw.json notepad $env:USERPROFILE\.openclaw\openclaw.json Add your AI API key in the configuration file: { "agents": { "defaults": { "model": "Your Model Name", "apiKey": "your-api-key-here" } } } Step 14: Start OpenClaw # Start Gateway service openclaw gateway # In another terminal, connect messaging channels (e.g., WhatsApp) openclaw channels login Follow the prompts to scan the QR code and connect OpenClaw to your messaging app. 4. Summary Through this guide, we've successfully implemented the complete process of automatically deploying OpenClaw on an Azure Windows 11 virtual machine. The entire deployment process is highly automated, completing everything from VM creation to installing all dependencies and OpenClaw itself through a single script. Key Takeaways Automation Benefits: Using az vm run-command allows executing configuration scripts immediately after VM creation without manual RDP login Dependency Management: Chocolatey simplifies the Windows package installation workflow Environment Isolation: Running AI agents on cloud VMs protects local computers and data Scalability: Scripted deployment facilitates replication and team collaboration, easily deploying multiple instances Cost Optimization Tips Standard_B2s VMs cost approximately $0.05/hour (~$37/month) on pay-as-you-go pricing When not in use, stop the VM to only pay for storage costs Consider Azure Reserved Instances to save up to 72% Security Hardening Recommendations Change Default Port: Modify RDP port from 3389 to a custom port Enable JIT Access: Use Azure Security Center's just-in-time access feature Configure Firewall Rules: Only allow specific IP addresses to access Regular System Updates: Enable automatic Windows Updates Use Azure Key Vault: Store API keys in Key Vault instead of configuration files 5. Additional Resources Official Documentation OpenClaw Website: https://openclaw.ai OpenClaw GitHub: https://github.com/openclaw/openclaw OpenClaw Documentation: https://docs.openclaw.ai Azure CLI Documentation: https://docs.microsoft.com/cli/azure/ Azure Resources Azure VM Pricing Calculator: https://azure.microsoft.com/pricing/calculator/ Azure Free Account: https://azure.microsoft.com/free/ (new users receive $200 credit) Azure Security Center: https://azure.microsoft.com/services/security-center/ Azure Key Vault: https://azure.microsoft.com/services/key-vault/Copilot Makes Branding Easy! New PowerPoint Brand Images Feature
🚀 New PowerPoint Feature: Brand Images + Copilot Enhances Brand Consistency! Microsoft has just released a powerful new capability in PowerPoint: Brand Images, allowing users to insert company‑approved logos, icons, and photos directly from within the app. No more searching through folders, Teams chats, or old emails to find the “right” logo. With the help of Microsoft 365 Copilot, users can now build professional, brand‑consistent presentations faster than ever. 📌 What this means for organizations: Centralized, controlled access to brand assets Ensures up‑to‑date logos and visuals Eliminates incorrect or outdated branding Seamless user experience inside PowerPoint To enable this feature, admins must configure the Organization Assets Library (OAL) in SharePoint and upload approved brand assets. Adding metadata and tags greatly improves search and filtering. I’ve created a full video breaking down everything you need to know: 👉 Feature overview 👉 Admin setup steps 👉 Best practices for asset organization 👉 How Copilot enhances the experience 🎥 Watch the full video here: https://youtu.be/Uv8JpHCvgg0 #Microsoft365 #PowerPoint #Copilot #BrandManagement #M365Admin #SharePoint #Productivity #ModernWorkplace #MicrosoftUpdates #GiulianoDeLucaAdding AI Personality to Browser Games
Introduction Browser games traditionally follow predictable patterns, fixed text messages, static tutorials, scripted NPC responses. Players see the same "Game Over" message whether they nearly won or failed spectacularly. Tutorial text remains identical regardless of player skill level. The game experience, while fun, lacks the dynamic reactivity of human-moderated gameplay. What if your Space Invaders game could comment on gameplay in real-time? Taunt players when they miss easy shots? Celebrate close victories with personalized messages? Adjust difficulty suggestions based on actual performance metrics? This article demonstrates exactly that: integrating AI-powered dynamic commentary into a browser game using Spaceinvaders-FoundryLocal, vanilla JavaScript, and Microsoft Foundry Local. You'll learn how to integrate local AI into client-side games, design AI personality systems that enhance rather than distract, implement context-aware commentary generation, and architect optional AI features that don't break core gameplay when unavailable. Whether you're building educational games, interactive training simulations, or simply adding personality to entertainment projects, this approach provides a blueprint for AI-enhanced gaming experiences. Why Local AI Transforms Browser Gaming Adding AI to games sounds expensive, cloud API costs scale with player counts, introducing per-gameplay pricing that makes free-to-play models challenging. Privacy concerns emerge when gameplay data leaves user devices. Latency affects real-time experiences, waiting 2 seconds for commentary after an action breaks immersion. Network requirements exclude offline play. Local AI solves all these challenges simultaneously. Foundry Local runs Small Language Models (SLMs) entirely on player devices, no API costs, no data leaving the machine, no network dependency. Inference happens in milliseconds, enabling truly real-time responses. Games work offline after initial load, perfect for mobile or low-connectivity scenarios. SLMs excel at personality-driven tasks like game commentary. They don't need perfect factual recall or complex reasoning, they generate entertaining, contextually relevant text based on game state. A 1.5B parameter model produces engaging taunts and celebration messages indistinguishable from hand-written content, while running easily on mid-range laptops. Integrating AI as an optional enhancement demonstrates good architecture. Core gameplay must function perfectly without AI, commentary enhances the experience but failure doesn't break the game. This graceful degradation pattern ensures maximum compatibility while offering AI features to capable devices. Architecture: Progressive Enhancement with AI The Spaceinvaders-FoundryLocal implementation uses progressive enhancement, the game fully works without AI, but adds dynamic personality when available: The base game implements classic Space Invaders mechanics entirely in vanilla JavaScript. Player ship movement, bullet physics, enemy patterns, collision detection, scoring, and power-up systems all operate independently of AI. This ensures universal compatibility across browsers, devices, and network conditions. The AI layer adds dynamic commentary through a backend Node.js proxy. The proxy runs locally, communicates with Foundry Local, and provides game context to the AI for generating personalized messages. The game polls the proxy periodically, sending current game state (score, accuracy, wave number, power-up usage) and receiving commentary responses. The architecture flow for AI-enhanced gameplay: Player Action (e.g., destroys enemy) ↓ Game Updates State (score += 100, accuracy tracked) ↓ Game Checks AI Status (polling every 5 seconds) ↓ If AI Available: Send Game Context to Backend → { event: 'wave_complete', score: 2500, accuracy: 78%, wave: 3 } ↓ Backend builds prompt with context ↓ Foundry Local generates comment ↓ Return commentary to game → "Wave 3 conquered! Your 78% accuracy shows improving skills." ↓ Display in game UI (animated text bubble) This design demonstrates several key patterns: Zero-dependency core: Game playable immediately, AI adds value incrementally Graceful degradation: If AI unavailable, game shows generic messages Asynchronous enhancement: AI runs in background, never blocks gameplay Context-aware generation: Commentary reflects actual player performance Local-first architecture: Everything runs on player's machine—no servers, no tracking Implementing Context-Aware AI Commentary Effective game commentary requires understanding current gameplay context. The AI needs to know what just happened, how the player is performing, and what makes this moment interesting: // llm.js - AI integration module export class GameAI { constructor() { this.baseURL = 'http://localhost:3001'; // Local proxy server this.available = false; this.checkAvailability(); } async checkAvailability() { try { const response = await fetch(`${this.baseURL}/health`, { method: 'GET', timeout: 2000 }); this.available = response.ok; return this.available; } catch (error) { console.log('AI server not available (optional feature)'); this.available = false; return false; } } async generateComment(gameContext) { if (!this.available) { return this.getFallbackComment(gameContext.event); } try { const response = await fetch(`${this.baseURL}/api/comment`, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(gameContext) }); if (!response.ok) { throw new Error('AI request failed'); } const data = await response.json(); return data.comment; } catch (error) { console.error('AI comment generation failed:', error); return this.getFallbackComment(gameContext.event); } } getFallbackComment(event) { // Static messages when AI unavailable const fallbacks = { 'wave_complete': 'Wave cleared!', 'player_hit': 'Shields damaged!', 'game_over': 'Game Over. Try again!', 'high_score': 'New high score!', 'power_up': 'Power-up collected!' }; return fallbacks[event] || 'Good job!'; } } The backend processes game context and generates contextually relevant commentary: // server.js - Node.js backend proxy import express from 'express'; import { FoundryLocalClient } from 'foundry-local-sdk'; const app = express(); const foundry = new FoundryLocalClient({ endpoint: process.env.FOUNDRY_LOCAL_ENDPOINT || 'http://127.0.0.1:5272' }); app.use(express.json()); app.use(express.cors()); // Allow browser game to connect app.get('/health', (req, res) => { res.json({ status: 'AI available', model: 'phi-3.5-mini' }); }); app.post('/api/comment', async (req, res) => { const { event, score, accuracy, wave, lives, combo } = req.body; // Build context-rich prompt const prompt = buildCommentPrompt(event, { score, accuracy, wave, lives, combo }); try { const completion = await foundry.chat.completions.create({ model: 'phi-3.5-mini', messages: [ { role: 'system', content: `You are an AI commander providing brief, encouraging commentary for a Space Invaders game. Be energetic, supportive, and sometimes humorous. Keep responses to 1-2 sentences maximum. Reference specific game metrics when relevant.` }, { role: 'user', content: prompt } ], temperature: 0.9, // High temperature for creative variety max_tokens: 50 }); const comment = completion.choices[0].message.content.trim(); res.json({ comment, model: 'phi-3.5-mini', timestamp: new Date().toISOString() }); } catch (error) { console.error('AI generation error:', error); res.status(500).json({ error: 'Commentary generation failed' }); } }); function buildCommentPrompt(event, context) { switch(event) { case 'wave_complete': return `The player just completed wave ${context.wave} with score ${context.score}. Their shooting accuracy is ${context.accuracy}%. ${context.lives} lives remaining. Generate an encouraging comment about their progress.`; case 'player_hit': return `The player got hit by an enemy! They now have ${context.lives} lives left. Score: ${context.score}. Provide a brief motivational comment to keep them engaged.`; case 'game_over': if (context.accuracy > 70) { return `Game over at wave ${context.wave}, score ${context.score}. The player had ${context.accuracy}% accuracy - pretty good! Generate an encouraging comment acknowledging their skill.`; } else { return `Game over at wave ${context.wave}, score ${context.score}. Accuracy was ${context.accuracy}%. Provide a supportive comment with a tip for improvement.`; } case 'combo_streak': return `Player achieved a ${context.combo}x combo streak! Score: ${context.score}. Generate an excited celebration comment.`; case 'power_up_used': return `Player activated a ${context.power_up_type} power-up. Generate a brief tactical comment about using it effectively.`; default: return `General gameplay comment. Score: ${context.score}, Wave: ${context.wave}.`; } } const PORT = 3001; app.listen(PORT, () => { console.log(`✓ Game AI server running on http://localhost:${PORT}`); console.log(`✓ Foundry Local endpoint: ${process.env.FOUNDRY_LOCAL_ENDPOINT || 'http://127.0.0.1:5272'}`); }); This backend demonstrates several best practices: Context-sensitive prompting: Different events get different prompt templates with relevant metrics Personality consistency: System message establishes tone and style guidelines Brevity constraints: max_tokens: 50 ensures comments don't overwhelm UI Creative variety: High temperature (0.9) produces diverse commentary on repeated events Performance-aware feedback: Comments adapt based on accuracy, lives remaining, combo streaks Integrating AI into Game Loop Without Performance Impact Games require 60 FPS to feel smooth, any blocking operation creates stutter. AI integration must be completely asynchronous and non-blocking: // game.js - Main game loop class SpaceInvadersGame { constructor() { this.ai = new GameAI(); this.lastAIUpdate = 0; this.aiUpdateInterval = 5000; // Poll AI every 5 seconds this.pendingAIRequest = false; // ... other game state } update(deltaTime) { // Core game logic (always runs) this.updatePlayer(deltaTime); this.updateEnemies(deltaTime); this.updateBullets(deltaTime); this.checkCollisions(); this.updatePowerUps(deltaTime); // AI commentary (optional, async) this.updateAI(deltaTime); } updateAI(deltaTime) { this.lastAIUpdate += deltaTime; // Only check AI periodically, never block gameplay if (this.lastAIUpdate >= this.aiUpdateInterval && !this.pendingAIRequest) { this.requestAICommentary(); } } async requestAICommentary() { // Check if there's an interesting event to comment on const event = this.getSignificantEvent(); if (!event) return; this.pendingAIRequest = true; // Fire-and-forget async request this.ai.generateComment({ event: event.type, score: this.score, accuracy: this.calculateAccuracy(), wave: this.currentWave, lives: this.lives, combo: this.comboMultiplier }) .then(comment => { this.displayAIComment(comment); this.lastAIUpdate = 0; }) .catch(error => { console.log('AI comment failed (non-critical):', error); }) .finally(() => { this.pendingAIRequest = false; }); } getSignificantEvent() { // Determine what's worth commenting on if (this.justCompletedWave) { this.justCompletedWave = false; return { type: 'wave_complete' }; } if (this.justGotHit) { this.justGotHit = false; return { type: 'player_hit' }; } if (this.comboMultiplier >= 5) { return { type: 'combo_streak' }; } return null; // Nothing interesting right now } displayAIComment(comment) { // Show comment in animated text bubble const bubble = document.createElement('div'); bubble.className = 'ai-comment-bubble'; bubble.textContent = comment; document.getElementById('game-container').appendChild(bubble); // Animate in setTimeout(() => bubble.classList.add('show'), 50); // Remove after 4 seconds setTimeout(() => { bubble.classList.remove('show'); setTimeout(() => bubble.remove(), 500); }, 4000); } calculateAccuracy() { if (this.shotsFired === 0) return 0; return Math.round((this.shotsHit / this.shotsFired) * 100); } } This integration pattern ensures: Zero gameplay impact: AI runs completely asynchronously—game never waits for AI Periodic updates only: Check AI every 5 seconds, not every frame (60 FPS → minimal CPU overhead) Event-driven commentary: Only request comments for significant moments, not continuous chatter Non-blocking display: Comments appear as animated overlays that don't interrupt gameplay Graceful failure: AI errors logged but never shown to players—game continues normally Designing AI Personality Systems Effective game AI has consistent personality that enhances rather than distracts. The system message establishes tone, response templates ensure variety, and context awareness makes commentary relevant: // Enhanced system message for consistent personality const AI_COMMANDER_PERSONALITY = ` You are AEGIS, an AI defense commander providing tactical commentary for a Space Invaders-style game. Your personality traits: - Enthusiastic but professional military commander tone - Celebrate victories with tactical language ("Excellent flanking maneuver!") - Acknowledge defeats with constructive feedback ("Regroup and maintain formation!") - Reference specific metrics to show you're paying attention - Keep responses to 1-2 sentences maximum - Use occasional humor but stay in character - Be encouraging even when player struggles Examples of your style: - "Wave neutralized! Your 85% accuracy shows precision targeting." - "Shield integrity compromised! Fall back and reassess the battlefield." - "Impressive combo multiplier! Sustained fire superiority achieved." - "That power-up spread pattern cleared the sector perfectly." `; // Context-aware response variety const RESPONSE_TEMPLATES = { wave_complete: { high_performance: [ "Your {accuracy}% accuracy led to decisive victory, Commander!", "Wave {wave} eliminated with tactical excellence!", "Strategic brilliance! {accuracy}% hit rate maintained." ], medium_performance: [ "Wave {wave} cleared. Solid tactics, Commander.", "Sector secured. Your {accuracy}% accuracy shows improvement potential.", "Objective achieved. Recommend tightening shot discipline." ], low_performance: [ "Wave {wave} cleared, but {accuracy}% accuracy needs work.", "Victory secured. Focus on accuracy in next engagement.", "Mission accomplished, though your hit rate needs improvement." ] }, player_hit: { lives_critical: [ "Critical damage! Only {lives} lives remain - exercise extreme caution!", "Shields failing! {lives} backup systems active.", "Red alert! Hull integrity at {lives} units." ], lives_okay: [ "Shields damaged. {lives} lives remaining. Stay focused!", "Hit sustained. {lives} backup systems online.", "Damage taken. Maintain defensive posture." ] } }; function selectResponseTemplate(event, context) { const templates = RESPONSE_TEMPLATES[event]; if (!templates) return; // Choose template category based on context let category; if (event === 'wave_complete') { if (context.accuracy >= 75) category = templates.high_performance; else if (context.accuracy >= 50) category = templates.medium_performance; else category = templates.low_performance; } else if (event === 'player_hit') { category = context.lives <= 2 ? templates.lives_critical : templates.lives_okay; } // Randomly select from category for variety const template = category[Math.floor(Math.random() * category.length)]; // Fill in context variables return template .replace('{accuracy}', context.accuracy) .replace('{wave}', context.wave) .replace('{lives}', context.lives); } This personality system creates: Consistent character: AEGIS always sounds like a military commander, never breaks character Context-appropriate responses: Different situations trigger different tones (celebration vs concern) Natural variety: Template randomization prevents repetitive commentary Metric awareness: Specific references to accuracy, lives, waves show AI is "watching" Encouraging feedback: Even in failure scenarios, provides constructive guidance Key Takeaways and Game AI Design Patterns Integrating AI into browser games demonstrates that advanced features don't require cloud services or complex infrastructure. Local AI enables personality-driven enhancements that run entirely on player devices, cost nothing at scale, and work offline. Essential principles for game AI integration: Progressive enhancement architecture: Core gameplay must work perfectly without AI—commentary enhances but isn't required Asynchronous-only integration: Never block game loop for AI—60 FPS gameplay is non-negotiable Context-aware generation: Commentary reflecting actual game state feels intelligent, generic messages feel robotic Personality consistency: Well-defined character voice creates memorable experiences Graceful failure handling: AI errors should be invisible to players—fallback to static messages Performance-conscious polling: Check AI every few seconds, not every frame Event-driven commentary: Only generate responses for significant moments This pattern extends beyond games, any interactive application benefits from context-aware AI personality: educational software providing personalized encouragement, fitness apps offering adaptive coaching, productivity tools giving motivational feedback. The complete implementation with game engine, AI integration, backend proxy, and deployment instructions is available at github.com/leestott/Spaceinvaders-FoundryLocal. Clone the repository to experience AI-enhanced gaming—just open index.html and start playing immediately, then optionally enable AI features for dynamic commentary. Resources and Further Reading Space Invaders with AI Repository - Complete game with AI integration Quick Start Guide - Play immediately or enable AI features Microsoft Foundry Local Documentation - SDK and model reference MDN Game Development - Browser game development patterns HTML5 Game Devs Forum - Community discussions and techniques
