Blog
Mert Özen's blog posts on software development, backend, frontend, and DevOps.
The Copilot Coding Agent: Assigning Issues, the GitHub Actions Environment, Setup Steps, the Firewall and Reviewing the Resulting PR
No red check on a pull request from the Copilot coding agent does not mean the tests passed: by default workflows do not run on Copilot's pushes and wait for Approve and run workflows. Writing and assigning the issue, a .NET, Node and SQL Server environment with copilot-setup-steps.yml, rules actionlint misses, Agents secrets, the firewall, reviewing the PR and Agent Merge.
A Feature Workflow with AI: Explore → Plan → Implement → Verify and Spec-Driven Development
The safe way to build a feature with an agent is to split the work into explore, plan, implement and verify, and keep the plan written down and approved, so you read every diff against it. We add a wishlist to Bookstore with real model output, review the plan, go test-first, catch code that deviates from the plan, and cover Spec Kit's flow, context management and parallel agents with git worktree.
Giving Agents the Right Context: AGENTS.md, copilot-instructions.md, CLAUDE.md and Path-Specific Instructions
The tool-neutral way to describe a repository to a coding agent is a short AGENTS.md at the root: build and test commands, folder layout, team rules and topics to ask about first. We cover which tool reads which file, nested and path-specific instructions, a real AGENTS.md for Bookstore, the results of the same task with three different contexts, and a check that catches stale instruction files.
How Much Do AI Coding Assistants Really Help? Tool Types, Evidence and the Right Expectations
AI coding tools do not help equally with every task: they speed up isolated, easy-to-verify work, while on work that needs repository knowledge the gain depends on context and review. We cover the tool types from completion to cloud agents, METR's 2025 and 2026 measurements, DORA 2025's amplifier finding and two real experiments with a local model.