GitHub Copilot Fusion: choosing effort instead of models

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My early impressions of GitHub Copilot Fusion, how its model routing works, and what GitHub’s benchmarks show about quality and cost.

Manage skills and plugins across Claude Code, Codex and GitHub Copilot with APM

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I have been building and using agent skills across Claude Code, Codex and GitHub Copilot, alongside plugins such as Ponytail and Superpowers. I am using Microsoft Agent Package Manager (APM) to manage that collection through a shared manifest. My agent-setup repository holds the configuration and a copy of the global lockfile. The manifest declares what … Read more

Send GitHub Copilot CLI telemetry to Azure Application Insights

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Send GitHub Copilot CLI telemetry to Azure Application Insights using an authenticated OpenTelemetry Collector on Azure Container Apps, then query it with KQL.

How to Find Azure Log Analytics Workspace Keys

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Retrieve Azure Log Analytics workspace keys with Azure CLI

Making AI reviews useful in pull requests

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AI code review becomes noisy quickly on busy pull requests. These are the patterns I would use for managed comments, finding identity, severity, evidence and merge gates.

How to update a pull request comment from GitHub Actions instead of creating duplicates

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Update one pull request comment from GitHub Actions instead of creating duplicates after every push, using a stable marker, ownership checks and a simple upsert pattern.

APIM Policy Patterns for AI Governance: Part 2 – Content Safety & Model Control

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Part 1 covered the consumption side of an AI gateway: rate limits, token quotas, usage attribution and the telemetry needed to understand them. The next runtime decision is whether a request should be allowed through at all, followed by whether that consumer is entitled to use the model it asked for. Those are separate controls, … Read more

APIM Policy Patterns for AI Governance: Part 1 – Rate Limits, Token Quotas & Observability

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How I would use Azure API Management to control AI request rates and token consumption, attribute usage, emit useful telemetry and handle backend throttling.

From Azure Policy to APIM: Implementing Azure AI Guardrails

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Azure Policy and API Management solve different parts of AI governance in Azure. This post goes into the actual policies and APIM XML behind that two-layer model, covering network controls, approved models, token quotas, content safety and observability.

Building an Azure AI Landing Zone Without Slowing Teams Down

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An Azure AI Landing Zone should make the governed route the easiest route for delivery teams. This post covers how Azure API Management, Azure Policy, identity, networking, quotas, telemetry and clear ownership boundaries work together to control AI consumption without turning the platform team into an approval bottleneck.