Browse GitHub Copilot Community (25)

bharadwajr explains how the Azure team built GEM (Great Experiences Matter), an AI-enabled “feedback-to-fix” loop that aggregates customer feedback at scale, turns it into prioritized insights, and creates actionable Azure DevOps work items with an auditable evidence trail and human review.

Christine Caggiano, Joshua Bradley, Steven Truitt, and William Chappell describe how Microsoft Discovery’s Discovery Engine adds CLIO (a self-adaptive “cognitive loop”) to improve agentic scientific problem-solving, and share benchmark results from Agent’s Last Exam showing higher scores and better consistency through multi-model, evidence-driven exploration.

Mohammed Barqawi introduces Logic App Storage Inspector, a read-only Kudu site extension that helps troubleshoot Azure Logic Apps Standard by inspecting the underlying storage for workflow history, versions, and queue health, with guidance on installation, configuration settings, and managed identity permissions.

reshmarahim announces the public preview of Radius Canvas for the GitHub Copilot app, a Canvas extension that builds a versioned application model from a repository so developers can visualize architecture, review PR impact via an application-level diff, and deploy across environments using generated GitHub Actions workflows with OIDC-based credentials.

demiajayi announces new Azure Cost Management capabilities in the Azure Resource Manager (ARM) MCP server, including default cost and pricing tools plus an optional CostManagement toolset. The post shows how to enable the toolset for GitHub Copilot Chat in VS Code and Copilot CLI, and what scenarios the tools support.

WSilveira explains how the MCP Connectors canvas extension for the GitHub Copilot app lets agents use hosted MCP servers from Azure Connector Namespace without manually wiring endpoints, headers, or local proxies. It also covers how the user-scoped MCP config works across the Copilot app and Copilot CLI, plus key security details.

Chris Noring explains how to govern GitHub Copilot spend in an enterprise by separating seat assignment, cost-center attribution, included AI credit boundaries, paid-usage budgets, and per-user limits. The article walks through a practical end-to-end model with two cost centers (Business and Developers) and shows how to avoid common misunderstandings.

Pamela Fox walks through practical MCP server designs for giving coding agents safe access to a PostgreSQL database, from free-form SQL to fully templated tools. She highlights where flexibility creates risk, and shows concrete guardrails like schema discovery patterns, read-only enforcement, and confirmation flows for destructive actions.

Lee Stott continues the FibreOps series with a hands-on look at building role-specialised autonomous agents using the Microsoft Agent Framework, including tool design patterns, an orchestrator pipeline, and a GitHub Copilot SDK adapter for conversational control of the system.

Lee Stott introduces Microsoft’s end-to-end agent platform (now GA) using the FibreOps reference implementation: build agents with the Microsoft Agent Framework and GitHub Copilot SDK, run them as hosted agents in Microsoft Foundry Agent Service, and distribute them to Teams and Microsoft 365 Copilot, with evaluation, routines, memory, and observability.

demiajayi introduces new Cost Management and Pricing toolsets for the Azure Resource Manager MCP server, enabling AI agents (including GitHub Copilot clients) to query costs, forecast spend, manage budgets and alerts, retrieve pricing, and analyze AKS costs directly through Azure’s control plane.

gauravbhardwaj explains how GitHub Copilot’s pooled included credits and metered overages work, and how to use user-level budgets and enterprise budgets to cap spend without unexpectedly blocking developers.

Chris Noring explains how to investigate unexpected GitHub Copilot Enterprise AI-credit spend and then control it using GitHub Billing guardrails. The post walks through identifying the SKU driving costs, attributing usage to organizations and cost centers, and applying budgets, alerts, and per-user limits without breaking productive workflows.

tonimontez explains how to govern GitHub Copilot’s usage-based spend using GitHub’s native budget controls first, then adds an Azure API Management gateway in front of an Azure AI Foundry (Azure OpenAI) deployment for real-time, token-granular quotas and per-developer telemetry.

junjieli introduces the Foundry Agent Canvas (public preview), a GitHub Copilot App extension that lets developers discover resources, scaffold and configure a Foundry hosted agent, test it locally with an embedded Agent Inspector, and deploy it to Foundry Agent Service using azd-driven workflows.

VimalVerma outlines Hypervelocity Engineering (HVE) as an operating model for building and continuously evolving Azure AI Landing Zones, with a focus on platform engineering, Infrastructure as Code, Policy as Code, and security-by-design so enterprise AI platforms can scale without losing governance.

Pamela Fox shows how to build an MCP server that returns more than plain text: image thumbnails as binary tool results and an interactive MCP app (a carousel) rendered inside VS Code, so GitHub Copilot can search, inspect, and present curated image results.

jisunchoi explains how to replace “multi-model chaos” with a governed AI gateway on Azure using Azure API Management, covering cost controls (token quotas and budget-based model downgrades), security hardening (managed identity + private endpoints), observability with Application Insights, and a Terraform-based deployment you can integrate with GitHub Copilot.

Dustin Ellis outlines practical ways to keep GitHub Copilot usage predictable under usage-based billing, focusing on token drivers like model choice, context size, and tool/agent usage. The post includes concrete prompting patterns, team guardrails, and lightweight policy ideas to reduce waste without losing the benefits of AI-assisted development.

kinfey explains why “token economics” has become a core architecture concern for agentic AI systems, using GitHub Copilot’s shift to usage-based billing as a concrete framing. The post breaks down practical engineering techniques—compression, caching, routing, and short-term memory—and shows how to evaluate cost, quality, and reliability together.

Lee Stott walks through a full “Multi‑Agent Dev Canvas” scenario for GitHub Copilot Canvas, showing how to decompose work, execute agent flows, validate with in-surface tests, inject failures, and evolve the design live (including GDPR/PII redaction) until the system meets its acceptance criteria.

Lee Stott explains what GitHub Copilot App Canvas is actually for: a development-time runtime where humans and AI agents can observe, steer, test, and evolve a running multi-agent system. The post walks through a real Canvas extension and the core building blocks needed to implement one.

Lee Stott explains the Model Context Protocol (MCP) and why it’s becoming a practical standard for connecting LLM apps to tools and data. The post highlights recent updates to Microsoft’s MCP for Beginners curriculum, including spec alignment, validated SDK samples, and a security-focused refresh with concrete fixes and audits.

Nikita Bajaj explains how Azure Migrate integrates with GitHub Copilot Modernize (public preview) to generate portfolio-level code insights across multiple applications and repositories, helping teams assess Azure readiness, review remediation guidance, and plan modernization work using a shared workflow between migration admins and developers.

Anavi Nahar rounds up Azure Databricks announcements and sessions from Databricks Data + AI Summit 2026, focusing on tighter interoperability with Microsoft’s data stack (OneLake, ADLS) and governed access via Unity Catalog, plus new integrations like the Excel add-in, SharePoint ingestion, and OneLake catalog federation.