Browse GitHub Copilot Community (30)

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.

kinfey breaks down a cost- and security-aware blueprint for running a multi-agent SDLC “tower” on AKS, using AI Runway for in-cluster model serving, Kata MicroVM isolation for each agent pod, and MCP so GitHub Copilot Chat can orchestrate tools while keeping token spend predictable.

Priyanka Nanda summarizes the Build 2026 updates for Azure Monitor, including new agent observability features, the Azure Copilot Observability agent, expanded OpenTelemetry/OTLP ingestion, and improvements to alerts, metrics querying, and SLI/SLO tracking across services like AKS and Application Insights.

DivSwa introduces Azure Logic Apps Automation (public preview), a new SaaS-style SKU for building and running workflow automations on Azure with built-in governance and production controls. The post highlights AI-assisted authoring, agent integration options (including Foundry agents and GitHub Copilot harnesses), and enterprise features like VNet/private endpoints, RBAC, and audit logging.

Mike Hulme introduces Microsoft’s “agentic modernization” approach, combining Azure Copilot migration agent and the GitHub Copilot modernization agent to help teams plan and execute large-scale application modernization, from estate discovery and dependency mapping through code transformation, PR-based execution, and governed rollout on Azure.

KayodePrince explains how to monitor AI coding agents by exporting OpenTelemetry (OTLP) signals and ingesting them into Azure Monitor, then using Application Insights agent views and Grafana dashboards to troubleshoot performance, understand usage, and track token-related cost signals.

Jason Pereira introduces two Azure Databricks public preview capabilities that connect Microsoft Copilot Studio and GitHub Copilot to Databricks: a workspace-wide Genie MCP endpoint for building workspace-aware agents, and Lakebase branching for debugging agent issues against real data without touching production.

TulikaC introduces a new Azure CLI switch for az webapp deploy that surfaces richer, more actionable diagnostics when Azure App Service for Linux deployments fail, including error codes, deployment context, suggested fixes, and a Copilot-ready prompt you can paste into GitHub Copilot for follow-up guidance.

jometzg shows how to build a GitHub Copilot agent usage dashboard by exporting VS Code Copilot telemetry via OpenTelemetry to an OTLP collector running on Azure Container Apps, sending it into Application Insights/Azure Monitor, and visualizing it in Azure Managed Grafana with IaC-friendly deployment and troubleshooting steps.

kinfey lays out a practical two-layer architecture for building and operating AI agents using Microsoft Agent Framework and Microsoft Foundry, with GitHub Copilot acting as a “coding agent” guided by versioned SKILL files. The post uses the ZavaShop workshop to show tools, MCP, workflows, evals, and deployment/ops guardrails across Python and .NET.

paggarwal introduces Engineering Squad, an open-source multi-agent framework that turns plain-text requirements into user stories, technical design, production code, and automated tests using Azure OpenAI and Foundry Local, with a self-correcting review loop and traceable run artifacts.

dbandaru explains how to connect Azure SRE Agent tools to the Azure MCP Server so developers can operate SRE Agents from MCP-compatible clients like GitHub Copilot CLI and VS Code Copilot. It covers setup, RBAC requirements, control-plane vs data-plane behavior, safety guardrails, and common troubleshooting steps.

nelsontam introduces the Microsoft Planetary Computer Pro MCP Tools VS Code extension, which integrates with GitHub Copilot to run geospatial workflows via natural-language prompts. It highlights STAC-based discovery, GeoCatalog management, and ingestion/monitoring features aimed at reducing the need for custom scripts and fragmented tooling.