GitHub Copilot App - Canvas Is Not a UI Builder
What if your development environment didn't just help you write code, but helped you observe, steer, and evolve a living system while it runs? That's the shift GitHub Copilot App Canvas represents. Canvas redefines how developers interact with agent-driven software: not by building traditional user interfaces, but by creating interactive environments where humans and AI co-create, test, and iterate in real time. This post walks through a real Canvas extension we built, a Multi-Agent Dev Canvas that demonstrates how Canvas becomes a runtime observability and control plane for an agent-driven system. We'll cover why Canvas exists, how it differs from traditional UI development, and how you can use it to accelerate the design-test-evolve loop for any multi-agent application. The Misconception: "Canvas Is for Building UIs" The first instinct many developers have when they see Canvas is to treat it like a UI framework, a place to build dashboards, boards, or user-facing applications. That's not what Canvas is for. Here's the distinction that matters: Traditional UIs are for using software. They serve end-users who interact with a finished product. Canvas is for shaping software while it runs. It serves developers and AI agents who are actively building, testing, and evolving a system. Canvas solves problems your final UI should never try to solve in a visible way. It's the observability layer, the control plane, the validation surface — all the things you need during development that disappear before production. Think of it this way: you wouldn't ship your debugger to users, but you absolutely need it while building. What We Built: A Multi-Agent Dev Canvas To demonstrate Canvas as a development runtime, we built a Multi-Agent Dev Canvas, a standalone GitHub Copilot Canvas extension (this repo, copilot-canvas-runtime) that treats an entire multi-agent system as a living, observable environment. The same pattern applies to any agent-driven system built on services such as Microsoft Foundry. The Multi-Agent Dev Canvas: a runtime observability and control plane where developers and AI agents collaborate to design, test, and evolve an agent-driven system in real time. The canvas provides four integrated panels: System View: See Your Agents Working Five specialised agents are displayed as live cards with real-time status indicators. Each card shows the agent's name, responsibility, current status (idle, running, done, or error), task count, and last action taken. When an agent is active, its card pulses blue. When it fails, it glows red. You see the system breathe. decompose_system — Breaks requirements into agent tasks execute_workflow — Coordinates agents to perform tasks validate_output — Runs evaluation tests and returns structured results update_system_design — Modifies architecture based on feedback track_state — Persists and updates system state over time Task Flows: Watch Work Move Through the Pipeline Below the agents, a flow graph visualises how tasks route between agents. When you decompose a system requirement like "Build an AI-powered code review agent," the canvas shows five components (pr-ingestion, code-analysis, feedback-generator, learning-loop, notification-service) flowing from the decomposer to the executor and designer agents. Each flow carries a status badge, pending, pass, or fail. Validation Panel: Continuous Testing, Not Afterthought Testing The validation panel displays structured test results with pass/fail badges and reasoning. When you run validation, each test case evaluates against specific criteria: ✅ "PR ingestion handles large diffs" — Meets criteria: process diffs over 5,000 lines without timeout ❌ "Feedback is actionable" — Failed: does not satisfy criteria that each suggestion includes a code fix ✅ "Learning loop converges" — Meets criteria: accept rate improves over 10 iterations ✅ "Notifications are non-blocking" — Meets criteria: delivery latency under 500ms This isn't a test runner you invoke separately, it's a validation surface embedded in the development loop. You see failures the moment they happen, in context, alongside the agents and flows that produced them. Live State Timeline: Every Mutation, Visible The right panel tracks every state change with timestamps. Decomposition events, workflow executions, validation runs, failure injections — all appear chronologically. This is the system's memory, visible to both the human developer and the AI agents working alongside them. Canvas as a Runtime: The Key Capabilities What makes Canvas a runtime rather than a display layer is that the agent can act through it. The canvas exposes seven agent-callable actions: Action What It Does decompose_system Accept requirements and components, generate task flows, update the system design execute_workflow Run pending tasks through the agent pipeline, produce artifacts validate_output Evaluate test cases against criteria, return structured pass/fail with reasoning update_system_design Modify the architecture description, constraints, or component list live track_state Read the full system state — agents, flows, validations, history, artifacts inject_failure Force an agent into an error state to test system adaptation pause_resume Toggle execution on and off The human developer can click Decompose, Execute, or Validate directly in the canvas. The AI agent can invoke the same actions programmatically. Both parties operate on the same surface, the same state, the same system, that's what makes Canvas collaborative in a way traditional tooling is not. Why This Matters: Canvas vs. Figma vs. Traditional UIs It helps to position Canvas against tools developers already know: Figma is Human-to-Human collaboration on design. Multiple people interact with the same visual surface, but nothing executes. It's a design tool. Traditional UIs are Human-to-System. Users interact with finished software through a polished interface. Canvas is Human-to-AI-to-System. It's a shared space where things actually execute. The developer steers, the AI acts, and the system evolves, all visible, all in real time. Canvas is collaborative in the Figma sense — it's a shared space, it's visual, multiple participants interact with the same surface. But unlike Figma, the participants include AI agents, and the surface isn't a mockup — it's a live system. How the Extension Works: Under the Hood A Canvas extension is a standard GitHub Copilot CLI extension, a single extension.mjs file that speaks JSON-RPC over stdio. The key components: 1. State Management Each canvas instance maintains its own system state: agents, task flows, validations, a state history timeline, artifacts, and the current system design. State is held in-memory per instance and pushed to the iframe via Server-Sent Events whenever it changes. function createInitialState() { return { agents: [ { id: "decomposer", name: "decompose_system", status: "idle", responsibility: "Break requirements into agent tasks" }, { id: "executor", name: "execute_workflow", status: "idle", responsibility: "Coordinate agents to perform tasks" }, // ... three more agents ], taskFlows: [], validations: [], stateHistory: [], artifacts: [], systemDesign: { description: "", constraints: [], components: [] }, execution: { paused: false, stepCount: 0 }, }; } 2. Real-Time Updates via Server-Sent Events The canvas runs a loopback HTTP server per instance. The iframe connects to an /events endpoint and receives state updates as they happen — no polling, no websocket complexity. if (req.url === "/events") { res.writeHead(200, { "Content-Type": "text/event-stream", "Cache-Control": "no-cache" }); clients.add(res); // Push current state immediately on connect res.write(`data: ${JSON.stringify(getState(instanceId))}\n\n`); } 3. Dual Interaction Model Every action is available through two paths. The human clicks a button in the iframe, which POSTs to the local server. The AI agent calls invoke_canvas_action through the SDK. Both paths mutate the same state and trigger the same SSE broadcast. Neither is privileged over the other. 4. Canvas Declaration The canvas registers with the Copilot SDK using createCanvas , declaring its identity, description, and all agent-callable actions with JSON Schema validation on inputs: createCanvas({ id: "multi-agent-dev", displayName: "Multi-Agent Dev Canvas", description: "Runtime observability and control plane for multi-agent development", actions: [ { name: "decompose_system", description: "Break requirements into agent tasks", inputSchema: { type: "object", properties: { requirements: { type: "string" }, components: { type: "array", items: { type: "string" } } }, required: ["requirements"] }, handler: async (ctx) => { /* ... */ }, }, // ... six more actions ], open: async (ctx) => { /* start server, return URL */ }, onClose: async (ctx) => { /* clean up */ }, }); Scenarios This Enables The Multi-Agent Dev Canvas supports four development scenarios that would be impossible with traditional tooling: 1. End-to-End Feature Design Tell the agent "Build an AI-powered code review system." Watch it decompose the requirement into five components, route tasks to specialist agents, execute the workflow, and validate the outputs, all visible in real time. Iterate by modifying constraints or components and re-running. 2. Live Agent Collaboration Observation See how agents hand off work to each other. The flow graph shows which agent produced what, which tasks are pending, and where bottlenecks form. This is the kind of observability you need when debugging multi-agent orchestration but would never expose in a production UI. 3. Fault Injection and Adaptation Testing Use inject_failure to force an agent into an error state. Watch how the system responds. Does the orchestrator recover? Do downstream tasks fail gracefully? This chaos-engineering approach, applied during development, visible in real time, catches integration failures before they reach production. 4. Validation-Driven Iteration Define test criteria, run validation, see which tests fail, update the system design, re-run. The validation panel isn't a separate CI pipeline, it's embedded in the development surface, creating a continuous feedback loop between design decisions and their measurable outcomes. Getting Started: Build Your Own Canvas Extension To create a Canvas extension in your own project: Read the SDK docs — Run extensions_manage({ operation: "guide" }) in GitHub Copilot CLI to get the canonical documentation paths. Scaffold — Run extensions_manage({ operation: "scaffold", kind: "canvas", name: "my-canvas", location: "project" }) to generate the boilerplate. Implement — Edit extension.mjs with your canvas logic: state model, actions, renderer HTML, and SSE updates. Reload — Run extensions_reload to activate your changes. Drive — Open with open_canvas , invoke actions with invoke_canvas_action , and iterate. The canvas extension lives in .github/extensions/your-canvas/extension.mjs for project-scoped extensions, or in your user extensions directory for personal use. No package.json needed, the github/copilot-sdk import is auto-resolved. Key Takeaways Canvas is a development runtime, not a UI framework. You don't build Canvas instead of your UI, you use Canvas to figure out, test, and evolve the UI and system before and during building it. Canvas solves problems your final UI should never expose. Agent observability, fault injection, live state mutation, validation feedback loops, these are development concerns, not user concerns. Canvas is Human-to-AI-to-System collaboration. Both the developer and the AI agent operate on the same surface, the same state, the same running system. It's Figma-like collaboration, but with AI agents, and things actually execute. Canvas turns debugging, testing, and execution into a continuous visual feedback loop. Instead of switching between an editor, a terminal, a test runner, and a monitoring dashboard, you have one surface where the system lives and evolves. Canvas extensions are lightweight. A single extension.mjs file, no dependencies, loopback HTTP server with SSE, the infrastructure gets out of the way so you can focus on the system you're building. The Bigger Picture Canvas redefines software development by shifting from writing static code to orchestrating living systems. Developers and AI co-create, observe, and evolve solutions in real time. Instead of building UIs for users, we build interactive environments for agents, turning debugging, testing, and execution into a continuous, visual feedback loop that accelerates innovation and brings ideas to production faster than ever. The Multi-Agent Dev Canvas we built here is one example. The pattern applies anywhere you're building agent-driven systems: AI orchestration, workflow automation, data pipelines, autonomous services. Anywhere you need to see, steer, and validate a complex system as it runs, that's where Canvas belongs. Resources copilot-canvas-runtime — this repository: the Multi-Agent Dev Canvas extension, scenario, and demo prompt GitHub Copilot Documentation — Official documentation for GitHub Copilot features Microsoft Foundry Documentation — Build and deploy AI agents with Microsoft FoundryFrom Draft to Decision: How to Integrate Copilot Studio Agents with Power Automate
Artificial intelligence is changing the way businesses communicate. Today, AI assistants can understand requests, generate content, summarize information, and help employees complete tasks faster. However, when it comes to sending emails, many organizations still need an important layer of control: approval. https://dellenny.com/from-draft-to-decision-how-to-integrate-copilot-studio-agents-with-power-automate-for-smart-email-approval-workflows/MCP for Beginners: Why Every AI Engineer and Developer Should Learn the Model Context Protocol
If you have spent any time building with large language models in the last year, you have hit the same wall everyone hits: your model is brilliant at reasoning but blind to the real world. It cannot read your database, call your internal API, search your documents, or trigger a deployment unless you hand-write glue code for every single integration. The Model Context Protocol (MCP) exists to tear that wall down, and Microsoft's open-source MCP for Beginners curriculum (reachable via the short link https://aka.ms/mcp-for-beginners) is the most complete, hands-on way to learn it. This post explains what MCP is, walks through the latest updates to the course, shows real code, and makes the case for why MCP belongs on your learning roadmap right now. Whether you are an AI engineer shipping agents to production, a developer wiring tools into Copilot, or a student trying to build a standout portfolio project. What is MCP, and why does it matter? Think of MCP as a universal translator for AI applications. Just as a USB-C port lets you connect any peripheral to any laptop without a custom cable per device, MCP lets an AI model connect to any tool or data source through one standardized protocol. The course uses exactly this analogy, and it holds up well. Before MCP, integrations were an M × N problem: every one of your M AI applications needed bespoke code to talk to each of your N tools. MCP turns that into an M + N problem. Build a tool once as an MCP server, and any MCP-compatible client, Claude Desktop, VS Code, Cursor, GitHub Copilot, and many others — can use it immediately. The protocol is built on a clean client–server model with a small set of primitives: Tools — functions the model can call (query a database, send an email, run code). Resources — data the server exposes for context (files, records, documents). Prompts — reusable, parameterized prompt templates. Sampling — a server asking the client's LLM to generate a completion, enabling collaborative workflows. Elicitation — a server requesting structured input from the user mid-task. Roots — boundaries that tell a server which directories or resources it is allowed to operate on. Communication runs over JSON-RPC, with transports for local processes ( stdio ) and remote servers (streamable HTTP). That standardization is the whole point: write to the spec, and you interoperate with the entire ecosystem. What's new: the latest updates to the course The MCP for Beginners curriculum is actively maintained, and the public changelog reads like a release log for a living product. Here are the most important recent changes, drawn directly from that changelog. 1. Aligned to MCP Specification The biggest update: the entire curriculum has been validated against the current MCP Specification 2025-11-25 and the latest official SDKs. Stale references to older spec revisions (2025-03-26 and 2025-06-18) were corrected across the security, transport, real-time search, sampling, and stdio-server modules, with links repointed to the canonical modelcontextprotocol.io spec paths. A gap analysis confirmed the course already covers every primitive introduced or expanded in the latest spec: Sampling — covered in lesson 3.14 and Advanced Topics. Elicitation (including URL mode) — in Core Concepts and Protocol Features. Roots — in the Introduction, Core Concepts, and Root Contexts. Tasks (experimental, long-running operations) — in Core Concepts and Protocol Features. Tool Annotations ( readOnlyHint / destructiveHint ) — in Core Concepts and Protocol Features. 2. Samples validated against current SDKs Code that does not run is worse than no code at all, so the maintainers re-validated the core samples: TypeScript: @modelcontextprotocol/sdk resolved to 1.29.0 ; a tsc --noEmit type-check passed with no errors — the McpServer and StdioServerTransport APIs remain valid. Python: validated in an isolated virtual environment with mcp[cli] (1.27.2); FastMCP.list_tools() correctly returned the sample add and subtract tools. SDK version pins across labs were bumped (for example mcp>=1.26.0 ) and lockfiles regenerated so every sample tracks the current release. 3. A serious security pass Security is treated as a first-class concern, not an afterthought. A full audit across every dependency manifest and the sample source code was run, and npm audit now reports 0 vulnerabilities in every audited directory. Highlights: Transitive npm advisories (in the MCP Inspector dev tool, the OpenAI client, and the SDK) were remediated by bumping @modelcontextprotocol/inspector to 0.22.0 and pinning a patched shell-quote . A real code-level command-injection fix (OWASP A03): an open_in_vscode tool that used subprocess.run(..., shell=True) was rewritten to launch the resolved executable directly with no shell — closing a metacharacter-injection vector. Python dependencies were audited with pip-audit , and a vulnerable transitive werkzeug was pinned to a patched >=3.1.6 . For anyone learning to ship agents, this is gold: the course demonstrates the whole secure-development loop, not just the happy path. 4. New lessons and a growing curriculum The curriculum keeps expanding with practical, modern lessons: 5.17 Adversarial Multi-Agent Reasoning — two agents argue opposite sides of a question using shared MCP tools ( web_search + run_python ), judged by a third agent. Includes a Mermaid architecture diagram, orchestrators in Python, TypeScript, and C#, and use cases like hallucination detection, threat modeling, and API design review. 3.12 MCP Hosts — configuration for Claude Desktop, VS Code, Cursor, Cline, and Windsurf, with JSON templates and a transport comparison table. 3.13 MCP Inspector — a debugging guide for testing tools, resources, and prompts. 4.1 Pagination — cursor-based pagination patterns in Python, TypeScript, and Java. 5.16 Protocol Features — progress notifications, request cancellation, resource templates, and lifecycle management. 5. Microsoft product rebranding Content was updated to reflect Microsoft's rebranding: Azure AI Foundry → Microsoft Foundry, and the AI Toolkit (AITK) → Microsoft Foundry Toolkit Extension for VS Code. If you have seen older tutorials referencing the previous names, the curriculum is now current. Your first MCP server: see how little code it takes The course's "first server" lesson builds a simple calculator. Here is the shape of a minimal MCP server in Python using FastMCP , which mirrors the validated sample in the repo. Notice how the protocol plumbing disappears — you just decorate functions. # server.py — a minimal MCP server with two tools from mcp.server.fastmcp import FastMCP # Name your server; this identifies it to MCP clients mcp = FastMCP("Calculator") @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers and return the result.""" return a + b @mcp.tool() def subtract(a: int, b: int) -> int: """Subtract b from a and return the result.""" return a - b if __name__ == "__main__": # Run over stdio so local hosts (VS Code, Claude Desktop) can connect mcp.run() The same idea in TypeScript, using the official SDK validated at version 1.29.0 : // server.ts — minimal MCP server in TypeScript import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; const server = new McpServer({ name: "Calculator", version: "1.0.0" }); // Register a tool with a typed input schema server.tool( "add", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }], }) ); // Connect over stdio and start listening const transport = new StdioServerTransport(); await server.connect(transport); That is a complete, runnable server. The docstrings and schemas matter: MCP exposes them to the model so it knows when and how to call each tool. Clear descriptions are effectively prompt engineering for your tools — a common pitfall is leaving them vague, which leads to the model misusing or ignoring the tool. Connecting it in VS Code Once your server runs, an MCP host connects to it. A typical VS Code / host configuration looks like this: { "servers": { "calculator": { "command": "python", "args": ["server.py"] } } } Lesson 3.12 (MCP Hosts) covers the equivalent JSON for Claude Desktop, Cursor, Cline, and Windsurf, and lesson 3.13 shows how to use the MCP Inspector to test your tools before wiring them into a host — the single best debugging habit you can build early. How the course is structured The curriculum is organized as a progressive journey with hands-on code in C#, Java, JavaScript, Python, Rust, and TypeScript. It is grouped into phases: Foundations (Modules 0–2): Introduction, Core Concepts, and Security. Building (Module 3): Getting Started — 15 lessons covering your first server and client, LLM clients, VS Code integration, stdio and HTTP streaming, testing, deployment, auth, hosts, the Inspector, sampling, and MCP Apps. Growing (Modules 4–5): Practical Implementation and Advanced Topics — 17 advanced lessons including Azure integration, OAuth2, Entra ID auth, scaling, multi-modality, context engineering, custom transports, and adversarial multi-agent reasoning. Mastery (Modules 6–11): Community Contributions, Lessons from Early Adoption, Best Practices, Case Studies, a Microsoft Foundry Toolkit workshop, and an end-to-end 13-lab PostgreSQL capstone. That final module is the standout for portfolio building: a complete, production-flavored path that takes you from architecture and row-level security through database design, a FastMCP server, semantic search with pgvector and Azure OpenAI, testing, Docker deployment to Azure Container Apps, and monitoring with Application Insights. Why developers should learn MCP now For AI engineers MCP is becoming the default integration layer for agents. Instead of re-implementing tool calling for every framework, you write to one open protocol and your tools work everywhere. The advanced modules — sampling, roots, elicitation, scaling, routing, and adversarial multi-agent patterns — are exactly the techniques you need to move agents from demo to production. For developers MCP is already wired into tools you use daily: VS Code, GitHub Copilot, Claude Desktop, Cursor, and more. Learning to build an MCP server means you can expose your systems — internal APIs, databases, CI/CD — to AI assistants safely. The security-first approach in the course (OAuth2, Entra ID, RBAC, dependency auditing) teaches you to do this the right way from day one. For students MCP is a rare opportunity to learn a technology while it is still early, with a free, beginner-friendly, Microsoft-maintained curriculum and code in six languages. The 13-lab capstone alone is a genuine portfolio project. And with content translated into 50+ languages, the barrier to entry is low no matter where you are. Responsible and secure by design A recurring theme worth calling out: the course does not treat security and governance as optional extras. It models real practices you should carry into your own work: Least privilege via roots — constrain what a server can touch. Tool annotations — mark tools readOnlyHint or destructiveHint so clients can warn users before destructive actions. No shells for user input — the command-injection fix is a textbook example of why you never pass untrusted input through a shell. Dependency hygiene — audit with npm audit and pip-audit , and pin patched releases. Proper auth — dedicated lessons on OAuth2 and Microsoft Entra ID. Key takeaways MCP standardizes how AI connects to tools and data, turning a combinatorial integration problem into a simple, reusable one. The course is current, validated against MCP Specification 2025-11-25 with SDKs at TypeScript 1.29.0 and Python mcp 1.27.2 . Samples actually run, and the repo demonstrates a full secure-development loop with 0 reported vulnerabilities after auditing. It is broad and deep: from a 10-line calculator server to a 13-lab production capstone, in six languages. It is the fastest credible path to MCP fluency for AI engineers, developers, and students alike. Get started today Open the course: https://aka.ms/mcp-for-beginners (redirects to the GitHub repository). Fork and clone it — use a sparse checkout to skip translations for a faster download: git clone --filter=blob:none --sparse https://github.com/microsoft/mcp-for-beginners.git cd mcp-for-beginners git sparse-checkout set --no-cone "/*" "!translations" "!translated_images" Build your first server with lesson 3.1 in your language of choice. Debug it with the MCP Inspector, then connect it in VS Code. Go deep with the 13-lab database capstone, and read the official spec at modelcontextprotocol.io. Track what's new in the changelog and join the community discussions. MCP is quietly becoming the connective tissue of the AI ecosystem. The earlier you learn it, the more leverage you will have — and Microsoft's MCP for Beginners is the clearest on-ramp available. Star the repo, build a server this week, and start connecting your AI to the world.Agent Optimizer in Foundry Agent Service: Building Smarter and More Efficient AI Agents
Artificial intelligence is moving beyond simple question-and-answer systems. Today, AI agents are becoming powerful digital assistants capable of understanding goals, making decisions, using tools, and completing complex workflows. However, building an AI agent is only one part of the journey. The real challenge is improving its performance, making it more accurate, efficient, and reliable. https://dellenny.com/agent-optimizer-in-foundry-agent-service-building-smarter-and-more-efficient-ai-agents/Copilot Feedback - Power User Experience Improvements
As a frequent Copilot user for technical discussions, architecture, governance, Microsoft 365, Power Platform, and problem-solving activities, I would like more control over the user experience. Copilot has become a daily productivity tool, but several UI and usability improvements would significantly benefit advanced users. 1. Allow Disabling Suggested Follow-Up Prompts The suggested replies shown below every response may be useful for new users, but for experienced users they often add little value and consume screen space. Please provide an option to: Disable suggested prompts entirely Adjust their frequency or aggressiveness Enable them only for new conversations or learning scenarios 2. Make '/' and '@' Shortcuts Configurable The automatic triggering of slash commands and @ mentions while typing can be disruptive and may interrupt or replace text unexpectedly. Please allow users to: Disable these shortcuts Configure alternative trigger characters Require a keyboard shortcut before activating them 3. Add a Power User Mode Consider introducing a dedicated Power User Mode featuring: No suggested prompts Reduced UI clutter Compact layouts Fewer interruptions while typing Advanced customization options A productivity-focused experience 4. Compact Conversation View Long technical conversations can become difficult to navigate due to excessive spacing and scrolling. Please provide: A compact view option Reduced vertical whitespace More conversation content visible on screen 5. Conversation Search Many conversations contain valuable information that users want to revisit later. Please add the ability to: Search within a conversation Search code snippets and formulas Search by date or topic Quickly jump to matching results 6. Pin Important Messages Allow users to pin key answers, code snippets, formulas, decisions, or reference messages within a conversation so they can be easily found later. 7. Enhanced Copy Experience For technical users working with Power Apps, Power Automate, SQL, PowerShell, and other technologies: One-click copy for all code and formulas Preserve formatting during copy operations Support copying multiple related blocks together 8. User-Defined Response Profiles Allow users to create and switch between response styles such as: Technical Troubleshooting Power User Learning Mode Executive Summary Architecture Review This would reduce the need to repeatedly explain personal preferences. 9. Memory Transparency and Management Allow users to: View all stored memories and preferences Edit or delete individual memory items Temporarily disable specific memories Understand which memories are influencing responses This would improve transparency and trust. Summary Copilot is evolving from a simple chat tool into a professional productivity platform. Providing greater control over the interface, shortcuts, suggestions, memory, and conversation management would allow experienced users to tailor the experience to their workflow while preserving the current experience for those who prefer more guidance. Thank you for considering these improvements.Microsoft Work IQ vs Foundry IQ vs Fabric IQ: Understanding the Future of AI-Powered
The world of artificial intelligence is moving beyond simple chatbots and automated tasks. Businesses today are looking for AI systems that can understand information, connect data, improve workflows, and help people make smarter decisions. This evolution has introduced powerful concepts like Work IQ, Foundry IQ, and Fabric IQ. https://dellenny.com/microsoft-work-iq-vs-foundry-iq-vs-fabric-iq-understanding-the-future-of-ai-powered-business-intelligence/GitHub Copilot Desktop App Launches a New Era of AI Development: The Future of Coding Is Here
Artificial intelligence is changing the way software is created, and developers are entering a new chapter where coding is no longer just about writing lines of code. It is becoming a smarter, faster, and more collaborative experience. https://dellenny.com/github-copilot-desktop-app-launches-a-new-era-of-ai-development-the-future-of-coding-is-here/Multi-Agent Systems with Semantic Kernel: Beyond Single Copilots
The AI landscape is moving beyond the era of single assistants. Early AI applications focused on creating one powerful copilot that could answer questions, generate content, write code, and automate tasks. But as real-world use cases become more complex, a new architecture is emerging: multi-agent systems. https://dellenny.com/multi-agent-systems-with-semantic-kernel-beyond-single-copilots/25 Copilot Studio Agent Ideas You Can Build This Weekend
Artificial intelligence is no longer something only big technology companies can experiment with. Today, anyone can build smart AI assistants that solve real problems, automate repetitive work, and improve daily productivity. With tools like Microsoft Copilot Studio, creating your own AI agent has become easier than ever. https://dellenny.com/25-copilot-studio-agent-ideas-you-can-build-this-weekend/
