Exploring VS Code AI features alongside Claude Code and Codex

VS Code has moved past autocomplete-plus-chat into an agent-first workspace. Instead of the model filling in lines while you drive every step, you hand off project-wide tasks and the agent inspects files, edits across the repo, runs terminal commands, and iterates — all without leaving the editor.

That shift from AI as sidekick to AI as doer is what this post is about, with a sober comparison to Claude Code and Codex alongside it.

VS Code documents this agentic loop explicitly: the agent works over a language model plus project context plus tools, making multi-file edits, running terminal commands, and operating in parallel or background modes distinct from inline chat (VS Code — agentic coding).

What the agent-first workspace changes

The older pattern is familiar: Copilot completes lines, chat explains a fragment, you copy the result back into the project. Useful, but the integration burden stays with you.

The agent pattern inverts that. You describe a higher-level task — build a CRUD form, fix TypeScript errors across the project, add dark mode — and the agent takes a working pass: reading the workspace, changing several files, running the build or tests, and fixing what breaks.

Key differences worth naming:

• Scope is the project, not the cursor. The agent reads and writes across files rather than completing the current block.
• Tools include the terminal. Running commands, tests, and builds inside the editor is part of the loop, not a separate manual step.
• Threads can run in parallel. Separate tasks live in separate threads instead of one linear chat transcript.
• Progress is reviewable. You inspect diffs and test output rather than reconstructing what the model did.

That is a genuine workflow change for anyone who has spent half their time ferrying output between a chat panel and the actual code. The honest caveat from early use still applies: no usage stats were shown in the original review pass, and editor UI moves fast — verify current view names and click paths in your installed VS Code before treating any walkthrough as literal.

For agent-readable repo habits that make this work better, see our agent-readable repository structure and Cursor prompts for agent sessions.

Claude Code side of the comparison

Claude Code follows the same doer logic from the terminal and IDE outward: it reads the codebase, edits files, and runs commands across terminal, IDE, desktop, and web surfaces (Claude Code overview).

In practice, teams mix the two shapes:

• Use VS Code agents when the work benefits from staying inside the editor — multi-file refactors, test-driven fixes, and parallel task threads with immediate diff review.
• Use Claude Code when the work starts from the terminal or spans repo-wide operations you already drive from the command line.
• Use both when a task has a research phase and an implementation phase: one thread explores and proposes the plan, another executes file by file with checks after each step.

Both reward the same preparation: a small, legible repo layout, explicit agent configs, and a test command the agent can run without asking you. Our Cursor rules for Next.js covers the config side of that setup even if your runner is VS Code or Claude Code rather than Cursor.

The practical lesson from teams running both: keep a single source of repo truth. When CLAUDE.md or equivalent project configs describe scripts, conventions, and boundaries once, every runner benefits — fewer hallucinated commands, fewer wrong-directory edits, and faster first passes.

Codex side of the comparison

OpenAI's Codex is the third reference point. Start from the current Codex docs rather than older platform guides — the older platform.openai.com/docs/guides/codex path no longer resolves, and the maintained entry point is the developer Codex page (OpenAI Codex docs).

Treat Codex as a single-agent coding surface for prompt-driven help and code generation, compared against VS Code's multi-threaded, project-resident workspace. The right choice depends on where you want state to live: inside the editor with parallel threads, or inside a focused Codex session you drive per task.

Do not over-read feature matrices here. Capabilities across Copilot, Codex, Claude Code, and VS Code agents change monthly; verify the current matrix against the three primaries linked in Sources before making a team-wide bet. Anything that pins exact menu labels, token limits, or model names without a dated source will rot fast.

A practical way to try it this week

Pick one contained, multi-file task rather than a greenfield build:

# Example session shape — adapt names to your repo.
# 1. Create a fresh agent thread for the task.
# 2. Prompt: "Add dark mode using existing tokens; update all screens; run typecheck and tests."
# 3. Review diffs file by file; ask the agent to fix failures and re-run checks.

What to watch:

• Give the agent the project first. Point it at the repo root, the package manager, and the test commands before assigning work.
• Keep threads scoped. One task per thread beats one mega-thread with five interleaved goals.
• Demand verification. Diffs plus passing checks are the done criteria — not a confident summary.
• Expect UI drift. If a walkthrough names a specific icon position or view label, confirm it in your build; VS Code renames and relocates surfaces regularly.
• Time-box the first attempt. If the agent stalls on environment setup, hand it the exact install and env commands rather than letting it guess.

This pairs well with the checklist discipline in our ship an AI MVP to production checklist: environments, secrets, and review gates still apply when agents move faster.

Limits and honest trade-offs

Agent workspaces do not remove judgment; they relocate it to review. Three limits come up repeatedly.

First, large refactors still need a human to define the boundary. The agent will happily rename across fifty files, but deciding what should not change — public API shapes, migration paths, data contracts — remains your call.

Second, environment failures waste the most agent time. Missing secrets, wrong Node versions, or uninstalled system dependencies send the agent into guess-and-retry loops. A reproducible setup script pays for itself immediately.

Third, parallel threads multiply review load. Two agents producing diffs at once is only faster if you actually review each diff. Merge without reading and you inherit subtle regressions with confident commit messages.

The teams getting the most from these tools treat the agent like a fast junior contributor: clear brief, small scope, explicit done criteria, and mandatory checks before merge.

Where this leaves Copilot-style autocomplete

Autocomplete and inline chat are not dead — they remain the fastest loop for single-line and single-function work. The agent workspace simply adds the missing upper register: parallel, project-wide execution with tool use.

The reframe that matters: stop asking whether AI sits beside your code and start deciding which threads of the workflow you are comfortable handing off. For repetitive multi-step work — bug triage, dependency updates, mass refactors, dark-mode rollouts — the agent-first workspace is already the practical next step past Copilot alone.

If you are building agent-powered apps on a shipped foundation, start from kits your agent can extend rather than regenerate. Explore production-ready starting points at OTF templates.

Sources

• VS Code — agentic coding — agent loop over model plus context plus tools; multi-file edits, terminal commands, parallel and background agents, chat versus agent distinction.
• Claude Code overview — reads codebases, edits files, runs commands across terminal, IDE, desktop, and web surfaces.
• OpenAI Codex docs — current Codex entry point; replaces the retired platform guide path.