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Snowy Mountains

Agentic AI in our development workflow

Claude Code · GitHub Copilot · Ecommerce Platform

$ claude "implement checkout flow with payment hooks"

What is Agentic AI? Not just autocomplete. AI that can plan, use tools, and execute multi-step tasks autonomously.

Reads & understands

Navigates the full codebase, reads files, searches for patterns, and understands architecture

Plans approach

Breaks tasks into steps, identifies files to modify, and creates an implementation strategy

Executes with tools

Writes code, runs terminal commands, type-checks, lints, and verifies its own output

Our AI toolkit

Claude Code: Anthropic

GitHub Copilot: GitHub/OpenAI

Terminal-based agentic AI

IDE-integrated (VS Code, JetBrains)

Plans and executes entire features end-to-end

Inline code completions as you type

Reads, writes, and creates files across the monorepo

Chat panel for questions and explanations

Runs commands: build, test, lint, type-check

Quick edits, boilerplate, and test generation

Spawns parallel sub-agents for complex tasks

Works best for focused, single-file tasks

The agentic loop: How Claude Code approaches a feature request, from intent to verified code

01 - Explore

Reads files, searches patterns, maps the architecture

02 - Plan

Creates a step-by-step implementation strategy

03 - Execute

Writes code across packages, spawns sub-agents in parallel

04 - Verify

Type-checks, lints, tests, and confirms correctness

Copilot in the IDE

Inline completions

Real-time suggestions as you type. Completes functions, patterns, and boilerplate based on context. Accept with Tab.

Chat panel

Ask questions about the codebase, request explanations, or get help with specific problems. Context-aware within the project.

Code actions

Generate tests, add documentation, fix errors, and refactor selections. Best for focused, single-file tasks.

A real feature build

  • Developer describes the requirement: "Add breadcrumb navigation with SEO-friendly structured data"

  • AI explores the monorepo: Reads 15+ packages, maps shared types, identifies the UI proxy layer and route patterns

  • AI generates an implementation plan: Proposes file changes across lib-contract, lib-ui-core, lib-ui-mui, and web-app

  • Parallel sub-agents implement: One agent handles types, another builds UI components, a third wires up the page

  • Automated verification: Type-check, ESLint, and tests run. Issues found are fixed automatically before review.

What this means for us

Our ecommerce platform is a complex monorepo with shared contracts, multiple UI implementations, and backend adapters. AI handles the cross-cutting complexity that slows humans down.

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Packages in the monorepo that AI navigates simultaneously

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Architecture layers AI works across (contracts, UI, BFF, adapters)

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Human needed to define intent, review, and approve

Key takeaways

01 Force multiplier, not replacement
AI amplifies developer capabilities. It handles execution while humans define intent and ensure quality.

02 Two tools, complementary strengths
Claude Code handles complex multi-file features. Copilot excels at quick, focused edits in the IDE.

03 Architecture matters
Well-structured codebases with clear contracts and patterns let AI work most effectively.

04 Human-in-the-loop always
Every plan is reviewed, every output is verified. AI proposes, the developer decides.