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Are You Tracking the Right Metrics with Your Co-Sourcing Teams?

“How do I stay in control if part of the engineering is handled externally?”

We often hear this question from CTOs and VPs of Engineering who are scaling aggressively and considering co-sourcing models. Not outsourcing—true co-sourcing: external engineering teams embedded into your product squads, owning delivery alongside your internal devs.

The challenge? You’re still responsible. For delivery, for code quality, for velocity — and for everything that goes wrong.

And while co-sourcing can be a force multiplier, it introduces new risks:

👀 blind spots in delivery,

⏱ delayed productivity,

⚠️ hidden quality issues.

The only way to stay in control is with the right metrics — ones that reflect actual engineering performance, not vanity KPIs.

✅ In this article:

• Why traditional agile metrics fail in co-sourcing scenarios

• The 3 most actionable metrics every CTO should track

• How to implement them using Azure DevOps, GitHub, and Microsoft observability tools

• ⚠️ Bonus: How to eliminate visibility gaps using CI/CD telemetry

Why Velocity and Burndown Aren’t Enough

Co-sourcing is not staff augmentation. You’re not just renting devs — you’re integrating external teams into your architecture, tools, and delivery pipelines. That makes tracking harder.

Here’s why traditional agile metrics like velocity, burndown, or story point completion fall short:

• They’re easily inflated or gamed

• They don’t reflect autonomy or code quality

• They say nothing about engineering maturity

What you need instead are engineering-centric, behavior-driven metrics — ones that show:

1. How fast the external team becomes productive

2. How independently they operate

3. Whether they’re producing sustainable, high-quality code

Let’s break these down.

1. Lead Time to First PR (LTTPR)

Metric type: Early productivity

Goal: <10 business days from onboarding

This metric tracks how long it takes a co-sourced engineer to go from granted access to first meaningful pull request merged.

Why it matters:

• It shows how smooth your onboarding and domain handoff really are

• It reflects how fast the team builds context and contributes

• It’s a leading indicator of future delivery velocity

What to watch: If LTTPR exceeds 2 sprints, it’s a signal of deeper issues: unclear documentation, fragmented environments, or misaligned expectations.

💡 How to track in Azure DevOps / GitHub:

• Use Azure DevOps Auditing logs to detect User added to project events

• Correlate with Pull Requests > Completed PRs filtered by author

• For GitHub, use GitHub Audit Logs + API

• Build a Power BI dashboard via Azure DevOps Analytics View to visualize average LTTPR across teams

2. % of Tickets Resolved Without Escalation

Metric type: Team autonomy

Goal: >80% of backlog items closed without internal escalation

This is about operational maturity. A strong co-sourced team should handle most work items independently, with minimal “help me unblock this” escalations to internal leads.

How to define escalation:

• PR rework requested due to architectural or business misunderstanding

• Ticket reassigned to an internal engineer

• Manual intervention from internal tech leads or product owners

💡 How to track in Azure:

• Use custom work item tags in Azure Boards (e.g., #escalated)

• Encourage leads to tag tickets manually when escalation is required

• Create an Analytics View KPI:


(Total closed tickets – Tickets with #escalated tag) / Total closed tickets


Bonus: Correlate this with team Slack/Teams activity (volume of internal help requests) via Microsoft Graph APIs.

3. Defect Density in Co-Owned Modules

Metric type: Code quality

Goal: Defect rate not higher than baseline across internal teams

Even high-velocity teams can be unsustainable if they generate bugs or regressions downstream. This metric tracks how many critical/major bugs are found in modules where the co-sourced team made recent changes.

Why this matters:

• Quality issues typically surface weeks after delivery

• Defect clustering around one team is a red flag for rushed or low-context coding

• You need to catch this before it hits production

💡 How to implement in Azure ecosystem:

• Enable Application Insights and Azure Monitor to capture exception rates by service/module

• Use Git blame + GitHub/Azure DevOps commit data to link defects to contributing teams

• Correlate bug reports (from Azure Boards or external bug tracking tools) with recent deployment history

Advanced: Use Azure DevOps Test Plans or integrate with SonarQube for real-time code quality scoring per repo.

Bonus Tactic: Embed Telemetry into the Dev Workflow

You can’t measure what you can’t see. And most co-sourcing efforts fail because teams operate in silos — outside of your CI/CD, observability, or incident tracking stack.

Here’s how to fix it:

1. Require CI/CD integration from day one

• All PRs go through your pipelines (Azure Pipelines, GitHub Actions)

• All builds, tests, and deploys are observable in your environment

2. Instrument feature flags and exceptions using Azure App Configuration + Application Insights

• Know which team deployed what

• Track exceptions and performance regressions by feature/team

3. Make code ownership explicit

• Use CODEOWNERS in GitHub or set branch policies in Azure Repos

• Automate alerts if “unowned” PRs are pushed

Final Thoughts

Co-sourcing isn’t just about scaling headcount. It’s about scaling engineering capacity without compromising visibility, control, or code quality.

By tracking:

• LTTPR → Onboarding effectiveness

• % of work resolved without escalation → Team autonomy

• Defect density → Long-term sustainability

…you shift from reactive firefighting to proactive delivery management.

These metrics give you the signals you need to stay in control — even when the engineering ownership is shared.