Every engineering leader running GitHub Copilot in Visual Studio has asked some version of the same question: "Is this actually making us faster, or just making us feel faster?" Lines of code went up. Commit counts went up. But so did code churn, review backlog, and — in some teams — production incidents. The old productivity dashboard doesn't tell you which story you're living. Here's a practical, no-nonsense way to measure it on a Microsoft stack: Visual Studio, GitHub Copilot, Azure DevOps, and Power BI.
Why the Old Metrics Lie to You Now
Three assumptions quietly broke when AI started writing real production code:
Lines of code (LOC) is now infinitely gameable — Copilot can generate hundreds of lines of boilerplate in seconds with zero functional value.
Deployment frequency and lead time can rise just because typing got faster, not because more real value shipped.
Code churn — code reverted or rewritten within two weeks — is trending upward industry-wide as AI-generated code increases. It's the quiet tax nobody puts on the dashboard.
The fix isn't to throw out delivery metrics. It's to add an AI-attribution layer on top of them, so you can tell real acceleration from acceleration built on hidden debt.
The Four-Layer Model
Instead of one productivity number, track four short layers — 8 to 10 metrics total, no more:
LayerQuestion it answersExample metrics
Delivery
Is the pipeline healthy?
Deployment frequency, lead time, change failure rate, recovery time
AI attribution
How much is AI really contributing, and does it hold up?
AI code share, Copilot acceptance rate, code durability (30-day survival)
Quality guardrail
Is speed being bought with debt?
Code churn, rework rate
Human experience
Are developers actually better off?
Satisfaction pulse survey, PR review cycle time
Golden rule: every speed metric gets a quality counter-metric, and every quantitative number gets a qualitative check. Never use any of this to rank individual developers — these are system-level signals, not performance reviews.
Step 1 — Turn On Copilot Telemetry in Visual Studio
Make sure everyone has a Copilot Business or Enterprise seat — personal seats don't report usage back to the organization.
In Visual Studio, update the GitHub Copilot and GitHub Copilot Chat extensions and sign in with the org's GitHub identity.
Go to Tools > Options > GitHub > Copilot and confirm telemetry and completions are enabled — this is what feeds acceptance-rate data.
If you use Copilot's agent mode against Azure resources or Azure DevOps work items, enable the Azure MCP Server tools from the Copilot Chat tools picker so agent activity gets counted too, not just inline suggestions.
Step 2 — Pull Org-Wide Copilot Metrics
GitHub's Copilot Usage Metrics API is your source of truth for acceptance rate, active users, and code-generation breakdowns.
Fastest path — deploy Microsoft's free solution accelerator:
azd init -t microsoft/copilot-metrics-dashboard azd up
This one command provisions Azure App Service, Azure Functions, Cosmos DB, and Key Vault, then wires them together for you. You'll be prompted for your GitHub org/enterprise name, a token, and whether metrics should scope to organization or enterprise.
Already on Grafana? Use the community copilot-metrics-viewer project instead — same data, different UI.
Note: GitHub retired the old Copilot Metrics API in April 2026. Point any new integration at the current Copilot Usage Metrics API.
Step 3 — Connect Azure DevOps Analytics
This is where your delivery-side DORA data already lives:
Turn on Analytics Views in Azure Boards for cycle time, lead time, and velocity per team.
Link your GitHub repos to Azure Boards work items so PRs and commits roll up against the same items Copilot is helping with — this lets you split PR cycle time into AI-assisted vs. human-authored.
Use the Pipelines > Analytics tab for deployment frequency and success/failure trends.
Step 4 — Unify Everything in Power BI
Bring three sources into one model:
Azure DevOps Analytics → connect via the built-in OData feed connector.
Copilot metrics → connect to the Cosmos DB store behind the solution accelerator (or the raw NDJSON export).
Application Insights / Azure Monitor → for production incidents and deployment markers, which feed Change Failure Rate and Recovery Time.
Build one dashboard page per layer — Delivery, AI Attribution, Quality Guardrail, Human Experience — and schedule a daily refresh. Copilot's API only exposes a rolling 28-day window, so if you want longer trend lines, store the data yourself (Cosmos DB or a Fabric/Azure SQL warehouse).
Step 5 — Don't Forget the Human Layer
Numbers alone don't tell you why. Run a short quarterly pulse survey (8–12 questions, Microsoft Forms is enough) covering satisfaction and flow. Whenever a metric spikes or dips, check the human context before reacting — a cycle-time increase during a planned refactor sprint isn't a regression.
A Starter Dashboard (Copy This)
LayerMetricSource
Delivery
Deployment Frequency
Azure Pipelines Analytics
Delivery
Change Failure Rate
Application Insights
Delivery
Recovery Time
Application Insights
AI Attribution
AI Code Share
Copilot Usage Metrics API
AI Attribution
Suggestion Acceptance Rate
Copilot Usage Metrics API
Quality
Code Churn
Azure Repos
Quality
Rework Rate
Azure Boards + Pipelines
Human Experience
Satisfaction (pulse survey)
Quarterly survey
Human Experience
PR Review Cycle Time
Azure Repos Analytics
That's it. Nine metrics, four layers, no vanity numbers.
Three Traps to Avoid
Don't reward raw AI token/suggestion volume. "Tokenmaxxing" measures spend, not value.
Don't use any of this to rank individuals. DORA and SPACE were built to measure systems, not people.
Don't let the dashboard grow past 10 metrics. If a number wouldn't change a decision, stop tracking it.
Bottom Line
AI genuinely removes toil and can speed up real delivery — but only if you're watching quality and durability alongside speed. On the Microsoft stack, the pieces to do this properly already exist: Copilot's usage API, Azure DevOps Analytics, Application Insights, and Power BI. The work isn't building new tools — it's wiring the ones you already have into one honest picture.
· · ·Have you set up something similar on your Azure DevOps + Copilot stack? I'd be curious to compare dashboard structures — reach out via ridilabs.net.
Having trouble with your database server in production workload? or you need to optimize query? GitHub Copilot is now available inside SQL Server Management Studio, bringing AI‑powered assistance directly into your SQL workflow. For developers and DBAs, this means faster query writing, smarter optimization, and a more intuitive way to explore databases. Below is a compact guide on how Copilot enhances your daily SQL tasks.
Why Copilot Matters in SSMS
Copilot isn’t just autocomplete. It understands:
your database schema,
your active connection,
your query context,
and your natural‑language instructions.
This makes it a powerful companion for writing, explaining, and optimizing T‑SQL.
How to Use Copilot in SSMS
1. Inline Suggestions
As you type, Copilot offers context‑aware completions.
Example: Type:
sql
-- get total sales by customerSELECT
Copilot may suggest a full query with joins, grouping, and ordering based on your schema.
2. Chat-Based Assistance
Use the Copilot chat panel to:
generate queries from natural language
explore database objects
troubleshoot errors
optimize slow SQL
Example prompt: “Explain this query and suggest improvements.”
AI-Assisted Query Optimization (Demo)
Let’s say you have a slow query:
sql
SELECT*FROMOrders o JOINCustomers c ONo.CustomerID =c.CustomerID WHEREc.Country ='USA'ORDERBYo.OrderDate;
Ask Copilot: “Optimize this query.”
Copilot may respond with:
remove SELECT *
add indexes
rewrite joins
reduce unnecessary sorting
Optimized version:
sql
SELECTo.OrderID, o.OrderDate, o.TotalAmount, c.CustomerName FROMOrders o JOINCustomers c ONo.CustomerID =c.CustomerID WHEREc.Country ='USA'ORDERBYo.OrderDate; CREATEINDEX IX_Customers_Country ONCustomers(Country); CREATEINDEX IX_Orders_OrderDate ONOrders(OrderDate);
This is exactly the kind of practical tuning Copilot excels at.
Natural Language → SQL (Demo)
Prompt: “Show me the top 10 customers by total sales in the last 12 months.”
Copilot generates:
sql
SELECTTOP 10c.CustomerName, SUM(o.TotalAmount) ASTotalSales FROMCustomers c JOINOrders o ONc.CustomerID =o.CustomerID WHEREo.OrderDate >=DATEADD(month, -12, GETDATE()) GROUPBYc.CustomerName ORDERBYTotalSales DESC;
No memorizing syntax. No hunting for column names. Just results.
Key Features at a Glance
Natural language to SQL
Smart code completion
Query explanation & optimization
Schema exploration
Script generation (tables, SPs, jobs, backups)
Multi-turn chat for refining queries
Final Thoughts
GitHub Copilot turns SSMS into a more intelligent, more productive SQL environment. Whether you're optimizing legacy queries, onboarding to a new database, or generating scripts on the fly, Copilot helps you move faster and write better SQL.
Hi All, Ensuring high‑quality code is one of the most important responsibilities in modern software development. Clean, maintainable, and secure code reduces long‑term technical debt, minimizes bugs in production, and improves team productivity. Traditionally, developers rely on manual reviews, static analysis tools, and extensive debugging sessions. Today, GitHub Copilot brings a new level of intelligence to this process.
More than just an AI code generator, GitHub Copilot acts as a real‑time reviewer, debugger, and quality assistant. This article explores how you can use GitHub Copilot to evaluate code quality, detect potential bugs, and improve your development workflow. On this article i use Visual Studio 2026, you will try on Visual Studio Codes it will work better
1. Why Code Quality Matters
High‑quality code leads to:
Fewer bugs and production incidents
Easier maintenance and refactoring
Better performance and security
Faster onboarding for new developers
More predictable development cycles
However, maintaining quality manually is time‑consuming. GitHub Copilot helps automate and accelerate this process.
2. How GitHub Copilot Helps Improve Code Quality
GitHub Copilot analyzes your code as you write and provides intelligent suggestions based on patterns learned from billions of lines of open‑source code.
a. Real‑Time Suggestions for Cleaner Code
Copilot continuously evaluates your code and offers improvements such as:
Simplifying complex logic
Suggesting clearer variable or function names
Recommending more efficient algorithms
Removing unused or redundant code
For example, if you write a deeply nested loop, Copilot may propose a more readable or optimized version.
b. Detecting Potential Bugs Automatically
Copilot can identify common pitfalls and risky patterns, including:
Null reference risks
Incorrect API usage
Missing error handling
Potential infinite loops
Security vulnerabilities such as SQL injection
If you write an API endpoint without validating input, Copilot often warns you and suggests adding validation logic.
c. Suggesting More Secure and Efficient Implementations
Copilot frequently recommends best‑practice alternatives, such as:
Using secure libraries for password hashing
Avoiding unsafe operations
Replacing manual parsing with built‑in framework utilities
Improving memory or CPU efficiency
This helps ensure your code follows modern standards.
3. Using GitHub Copilot Chat for Code Review and Debugging
The Copilot Chat feature is one of the most powerful tools for improving code quality.
a. Ask Copilot to Review Your Code
You can highlight a block of code and ask:
/review
Copilot will provide:
A list of potential bugs
Readability improvements
Security warnings
Refactoring suggestions
b. Ask Copilot to Explain Errors
When you encounter an exception or failing test, you can ask:
Explain why this code fails
Copilot will analyze the stack trace, identify the root cause, and propose a fix.
c. Ask Copilot to Improve Performance
For performance‑critical functions, you can request:
Improve performance of this function
Copilot may suggest:
Algorithmic improvements
Better data structures
Reduced allocations
Parallelization opportunities
4. Using GitHub Copilot to Generate Unit Tests
Unit tests are essential for maintaining code quality. Copilot can:
Generate unit tests automatically
Suggest edge cases you may have missed
Create consistent test structures
Example prompt:
Generate unit tests for this function using xUnit. Include edge cases.
This accelerates test coverage and reduces human error.
5. Recommended Workflow for Checking Code Quality with Copilot
A practical workflow might look like this:
1. Write your code normally
Copilot provides real‑time suggestions.
2. Use Copilot Chat for review
Ask for improvements, bug detection, or readability enhancements.
3. Generate unit tests
Ensure critical functions are covered.
4. Apply refactoring suggestions
Let Copilot help rewrite complex or inefficient sections.
5. Debug with Copilot
When errors occur, ask Copilot to analyze and propose fixes.
6. Case Study: Detecting Bugs in an ASP.NET Core API
Consider the following login endpoint:
[HttpPost("login")] public async Task<IActionResult> Login(UserLoginRequest request) { var user = await _userService.GetUser(request.Username); if (user.Password == request.Password) return Ok("Success"); return Unauthorized(); }
If you run /review on this code, Copilot will typically identify issues such as:
Plain‑text password comparison
Missing input validation
Potential null reference on user
Lack of rate limiting (risk of brute force attacks)
Copilot may then propose a more secure and robust implementation.
7. Conclusion
GitHub Copilot is more than an AI assistant—it is a powerful tool for improving code quality and detecting bugs early. By integrating Copilot into your workflow, you can:
Reduce debugging time
Improve security and maintainability
Write cleaner, more consistent code
Boost overall development productivity
AI becomes agentic it means can be automated by your command. On this post, we want to create:
Individual agents — coding agents that perform tasks (fix bugs, write tests, refactor, analyze logs, generate docs, etc.).
An orchestrating agent — a higher‑level controller that coordinates multiple agents, assigns tasks, monitors progress, and handles dependencies.
Below is a practical, step‑by‑step guide grounded in the latest GitHub Copilot agentic‑AI documentation and mission‑control orchestration features.
Core Idea (the short version)
You create a team of AI agents by:
Defining clear roles (e.g., “Test Engineer Agent”, “Refactor Agent”, “Bug Triage Agent”).
Using Copilot Chat, Copilot CLI, and Copilot Spaces to give each agent context.
Using Mission Control to orchestrate multiple agents in parallel, monitor drift, and review outputs.
Using Agentic Workflows (Markdown‑based automation in GitHub Actions) for unattended, automated tasks.
This mirrors how a real engineering team works: planners, implementers, reviewers, and automation bots.
Create Individual Agents (the “team members”)
GitHub Copilot supports custom agents and agent skills inside VS Code or GitHub.com.
What an agent is (per GitHub Docs)
AI agents behave like peer programmers who can:
Run asynchronous tasks
Fix issues in your backlog
Perform analysis or optimization
Contribute to ideation and planning
How to define an agent
You define an agent by giving it:
A persona (e.g., “You are a senior backend engineer specializing in Go microservices.”)
A scope (files, repo, or context from Copilot Spaces)
A task template (prompt file or Copilot Space instructions)
Typical agent roles
Agent RoleResponsibilities
Bug Triage Agent
Analyze issues, reproduce bugs, propose fixes
Refactor Agent
Improve code quality, modularize, remove duplication
Test Engineer Agent
Generate unit tests, integration tests
Security Agent
Run static analysis, identify vulnerabilities
Documentation Agent
Generate README updates, API docs
Performance Agent
Profile code, suggest optimizations
Each agent is just a prompt + context + task.
Orchestrate Agents Using Mission Control
GitHub’s Mission Control (Agent HQ) lets you run multiple agents from one place.
What Mission Control does
Assign tasks to multiple agents across repos
Watch real‑time logs
Pause, refine, or restart runs
Review resulting pull requests
Why orchestration matters
Instead of waiting for one agent to finish, you:
Kick off parallel tasks
Monitor for drift (when the agent goes off‑scope)
Step in when tests fail or scope creeps
Partition work to avoid merge conflicts
When to use parallel vs sequential
Parallel (recommended for):
Research
Log analysis
Documentation
Security reviews
Work in different modules
Sequential (recommended for):
Tasks with dependencies
Complex explorations
Changes touching the same files
Add Automation with Agentic Workflows (GitHub Actions)
Agentic Workflows let you write Markdown instructions that run as autonomous agents inside GitHub Actions.
What Agentic Workflows are
Natural‑language automation instead of YAML scripts
AI‑driven decision making
Safe Outputs (AI cannot directly write to repo; changes are validated)
Multi‑engine support (Copilot, Claude, Codex)
Example uses
Daily status reports
CI failure analysis
Automatic triage
Auto‑fixing simple issues
Event‑driven workflows (push, PR, schedule)
Example workflow (simplified)
markdown
# agentic-workflow.md When CI fails: - Analyze logs - Identify root cause - Suggest a fix - Prepare a pull request draft
This runs automatically inside GitHub Actions.
Architecture: “AI Software Development Team”
Here’s how to structure your agentic team:
1. Planning Layer
Product Manager uses Copilot Chat to break down features
Copilot creates GitHub Issues
Copilot Spaces store diagrams, mockups, and context
2. Execution Layer
Developers use Copilot CLI to explore code, generate patches
Agents perform tasks asynchronously
3. Orchestration Layer
Mission Control coordinates multiple agents
You monitor logs, refine prompts, and approve PRs
4. Automation Layer
Agentic Workflows handle unattended tasks
Step‑by‑Step: Build Your First Agentic Team
Step 1 — Create a Copilot Space
Upload architecture diagrams, requirements, mockups
Add prompt templates for each agent role
Share with your team
Step 2 — Define Agent Personas
Example:
Code
You are the Test Engineer Agent. You write comprehensive unit tests using Jest. You never modify business logic.
Step 3 — Assign Tasks in Mission Control
Open Mission Control
Create tasks like:
“Generate tests for /src/utils/date.ts”
“Refactor /src/api/user.ts for readability”
“Analyze performance bottlenecks in /services/payment”
Step 4 — Run Agents in Parallel
Kick off multiple tasks
Watch logs
Intervene when needed
Step 5 — Review PRs
Mission Control shows all PRs created by agents
You approve, request changes, or merge
Step 6 — Add Agentic Workflows
Automate repetitive tasks
Add CI‑driven or schedule‑driven agents
Best Practices for Agentic Teams
Write extremely clear prompts
Specificity = better results
Partition work to avoid merge conflicts
Assign agents to:
Different modules
Different layers (API vs UI vs tests)
Always provide context
Use:
Copilot Spaces
Repo links
Code excerpts
Monitor for drift
If logs show the agent misunderstanding:
Pause
Refine prompt
Restart