Generative AI Coding 2026: A Feature-by-Feature Comparison of Cursor, VS Code, Kiro, and Antigravity—and How to Choose the Right One
To get real value from generative AI—not just autocomplete, but end-to-end across design, implementation, verification, and operations—tool choice matters a lot. What’s becoming the new mainstream is a competition over how well a tool can integrate four things into one flow:
(1) smart in-editor completion and editing, (2) changes spanning multiple files, (3) “agents” that can run terminals and tests to push work close to done, and (4) mechanisms that leave outputs in a form that’s easy to review.
In this article, we’ll carefully compare Cursor / VS Code / Kiro / Antigravity by capability, and organize which one “feels smooth” depending on team size and development style. Rather than vague impressions, we’ll anchor the discussion in practical points teams often struggle with (permissions, autonomy, verification, governance).
Bottom line first: who is each tool best for?
- Cursor: For people who want to maximize “fast progress” inside the editor. Great autocomplete feel, multi-line edits, and task delegation via an agent—ideal for individuals to small teams who want a faster tempo.
- VS Code + Copilot: For teams who want to keep their existing VS Code operations and deepen AI usage step by step. Agent mode and edit mode make it easier to adopt AI with control.
- Kiro: For individuals/teams who struggle to carry prototypes into “production-grade” quality. It starts from specs, flows from requirements → design → tasks, and uses hooks to automate quality checks—well-suited to real-world engineering orgs.
- Antigravity: For teams who want to run multiple agents in parallel and “delegate + supervise” long tasks and maintenance. It keeps work reviewable via Artifacts and is designed for asynchronous workflows.
The framework: the “4 layers” of generative AI coding
In practice, AI-assisted development roughly breaks into these four layers. Tools feel radically different depending on which layers they strengthen.
- Completion layer: predicts the next line or next edit (tab completion, smart rewrite)
- Editing layer: edits selections through multiple files via natural language (better if diff review is easy)
- Execution & verification layer: runs terminal commands, tests, linters, builds, and iterates fixes
- Operations layer: preserves specs/history/rules/outputs so teams can reproduce results (governance)
We’ll compare the four tools along this axis.
Feature summary (quick look)
| Feature category | Cursor | VS Code + Copilot | Kiro | Antigravity |
|---|---|---|---|---|
| Center of experience | High-speed in-editor development | Extends existing VS Code | Spec-driven path to production | Agent operations (command center) |
| Completion (Tab) | Strong (dedicated completion model) | Strong (Copilot completion) | Standard IDE completion + AI | Completion via Editor View |
| Multi-line / editing | Strong (multi-line editing, etc.) | Easy to control via Edit/Edits | Stepwise via specs → tasks | Often delegated to agents |
| Autonomous agents | Yes (Agent delegation) | Yes (Agent mode) | Yes (agentic chat / hooks) | Core feature (multiple agents) |
| Terminal / test integration | Yes (command suggestion/execution) | Yes (suggestions, approvals, fix loop) | Automated via hooks + task execution history | Unified across editor/terminal/browser |
| Specs / documentation | More rules/context-driven | Instruction/context operations | Specs are central (req → design → tasks) | Review via Artifacts |
| Team ops / governance | Depends on rules | Easy for enterprise rollout | Governance via steering rules/hooks | Designed for supervision & verification |
Next, we’ll dig into each area in more concrete terms.
1) Completion (Tab completion / smart rewrite): how much it speeds up your hands
Cursor
Cursor puts the completion experience front and center. A key characteristic is that Tab completion aims not only for “the next line,” but for “the next action.” As you start typing naturally, rewrites and suggestions appear smoothly, increasing development tempo. The more short fixes and repetitive work you do, the more you feel the benefit.
Good fit for
- Iterating UI tweaks (copy, CSS, props adjustments)
- Producing small functions in volume (validation, conversion, utilities)
Mini tip (how to write to “invite” better completion)
- Start with: purpose (in Japanese/English) → function name → argument names → return value comment
Example:// Normalize a phone number into E.164-like format→function normalizePhone(...)
VS Code + Copilot
VS Code’s strength isn’t only completion performance. It’s also being able to add AI without breaking your existing developer experience. If your team already optimized keybindings, extensions, and settings around VS Code, “just getting more completion” often delivers plenty of value.
Good fit for
- You don’t want to disrupt existing VS Code operations
- You rely heavily on settings, extensions, Dev Containers, etc.
Kiro
Kiro includes completion as an “AI IDE,” but its center of gravity is the spec-driven workflow described later. Rather than “winning on completion alone,” it treats completion as support to keep implementation aligned with specs.
Antigravity
Antigravity offers completion and inline commands via Editor View, but its real value tends to sit in the Manager Surface (command center) described later. You typically don’t choose it just for completion; it’s more like an editor as an entry point to asynchronous agent operations.
2) Chat and “edit mode”: separating questions from changes prevents accidents
AI chat is convenient, but in real work, mixing “discussion” and “modification” increases mistakes. That’s why tools are trending toward modes like Ask (questions) / Edit (changes) / Agent (autonomy).
Cursor: fast edits while preserving intent
Cursor shines at slipping “fix this” naturally into the editor flow. It’s especially strong for small-range edits and multi-line fixes that you want to apply quickly.
For team usage, it helps to predefine rules like “how far AI is allowed to touch,” which makes diff review easier.
VS Code + Copilot: easier to control edits (which files can be touched)
A Copilot advantage is operational control: in edit mode, it’s easier to choose the target files, receive proposals step-by-step, and approve each change. This is valuable early in adoption when teams are still uneasy about AI edits.
It also keeps chat and edits in one environment while preserving existing extensions and workflows.
Mini template (a practical edit instruction format)
- Goal: what must be satisfied
- Scope: file names / function names / component names
- Acceptance criteria: tests, types, lint, compatibility
Passing just these three points reduces “misses” dramatically.
Kiro: make a spec before you talk
Kiro doesn’t just do “fix this.” It strongly supports a flow where you create a spec (requirements → design → tasks) before implementation. It can feel slower for prototypes, but becomes more valuable as complexity increases, stakeholders multiply, and maintainability matters.
Its key move is solving the “decisions disappear in conversation” problem by preserving them as living artifacts.
Antigravity: validate via “Artifacts” rather than long conversations
Antigravity is designed so you don’t have to reread long chat logs. Instead, it shows “what was done” and “how it was verified” via Artifacts such as task lists, implementation plans, screenshots, and browser records.
As teams get larger, this helps a lot: the longer a conversation gets, the less humans can realistically follow it.
3) Multi-file edits and diff review: how to accept AI changes safely
When AI starts editing multiple files, value increases sharply—but so does risk. What matters is: diff traceability, explainability of changes, and easy rollback.
Cursor: great at multi-line edits and scoped changes
Cursor emphasizes multi-line edits and scoped fixes (repairs limited to the necessary area). It’s well-suited for iterating small-to-medium changes quickly.
For large projects, you’ll get higher-quality diffs by tightening the context AI uses (rules/guidelines).
VS Code + Copilot: undo + approvals are practical
Copilot’s agent mode supports multi-step edits, and includes practical safeguards like terminal command approval and Undo.
If your team wants AI help but doesn’t want it to “run wild,” VS Code’s controlled loop builds trust early.
Kiro: task decomposition creates “staged review”
Kiro generates tasks from the spec and advances work task-by-task, making it easier to track:
- which requirement a change belongs to
- what each task changed
The larger the change, the more “granularity design” directly impacts quality.
Antigravity: parallel work + verification via artifacts
Antigravity assumes multiple agents run in parallel and leave evidence as Artifacts.
It supports multi-file changes and also includes UI validation (e.g., screenshots) so you can review correctness as outputs, not just chat. This is especially helpful because UI correctness is hard to judge from code diffs alone.
4) Autonomous agents: what do you delegate, and where do you stop?
Agents are powerful—but delegation without controls is scary. Evaluate the scope of autonomy and the intervention points.
Cursor: delegate tasks and focus on direction
Cursor promotes delegating tasks so you can focus on higher-level guidance. It fits a workflow like:
- research
- refactoring
- small feature additions
…then you review the diff and fine-tune.
VS Code + Copilot: an approval-based loop for terminal + iterative fixes
Copilot agent mode can read a codebase, edit multiple files, run terminal/tests, read errors, and iterate fixes. The key is terminal actions are approval-based, with transparency and Undo.
It aims for autonomy without “going off to break things.”
Kiro: hooks for event-driven automation
Kiro’s distinctive feature isn’t only one-off agent work; it’s hooks that trigger agents on events like save/create/delete.
Examples: update tests when a React component is saved; update README when an API changes; scan for secrets before commit.
This automates “annoying but important” work, raising team-wide quality—not just individual productivity.
Antigravity: supervise multiple agents via the Manager Surface
Antigravity centers on a Manager Surface where you start, observe, and steer multiple agents. It’s designed for asynchronous loops across long maintenance tasks, bug reproduction → test creation → fixes, and iterative UI changes.
To get the most out of it, teams benefit from shared “supervision skills” (review templates and how to read Artifacts).
5) Spec-driven development and Artifacts: mechanisms to reach production quality
The biggest pain point with AI is: “It got built, but we don’t know what assumptions it used.”
How each tool handles this becomes the defining difference between Kiro and Antigravity.
Kiro: grow a spec in 3 stages—requirements → design → tasks
Kiro strongly supports generating requirements from a single prompt, producing design documents (data flows, interfaces, DB schemas, APIs, etc.), and then breaking it down into tasks.
It also aims to keep specs synced with code as implementation progresses, reducing the “docs got abandoned” problem.
This shines in reviews, handoffs, and audit-friendly environments.
Mini example (the level of granularity Kiro-style specs aim for)
- Requirements: user stories + acceptance criteria (including edge cases)
- Design: data structures, APIs, screen flows, responsibility boundaries
- Tasks: implementation steps including tests, loading states, responsive, accessibility
Antigravity: keep work reviewable via Artifacts
Antigravity stores work as Artifacts (task lists, plans, screenshots, browser records), and allows feedback to drive revisions.
This “look and know” format makes it easier for product owners, designers, and QA to participate.
For UI changes in particular, screenshots and records alongside diffs are very practical.
Cursor / VS Code: specs and artifacts are built by operations, not the tool
Cursor and VS Code tend not to push spec-driven or artifact-first workflows as the core concept; instead, you supplement with prompt design, rules, and documentation practices.
That makes them lightweight to start, but in teams you need “standard patterns” to avoid person-dependence.
6) Context management: what you show the AI determines quality
As codebases grow, AI mistakes often come from “it didn’t see it” or “it guessed.” So evaluate how context is provided.
- VS Code + Copilot: explicit context passing via workspace summaries,
#file, custom instructions, etc. - Kiro: keeps intent through specs, steering rules, and context providers (files/URLs/docs)
- Cursor: accelerates in-editor actions assuming codebase understanding (embeddings, etc.); stability improves with good rules
- Antigravity: spans editor/terminal/browser and leaves highlights as Artifacts so humans can verify efficiently
7) Integrations (MCP, etc.): connecting internal and specialized tools
A recent key is “can the AI use external tools?” Real-world dev stacks are full of internal APIs, lint/tests, design review, doc search, and more—connecting these increases leverage.
- Kiro: explicitly supports MCP and assumes specialized tool connections
- VS Code + Copilot: agent mode is moving toward handling MCP servers
- Cursor: depends on operations, but its core is editor-centric speed
- Antigravity: philosophically aligns with integration, since agents cross tools and preserve results as Artifacts
8) Team operations: how do you guarantee rules, quality, and security?
The difference between personal and team use shows up here.
For small teams (1–5) who want “build fast, fix fast”
- Use Cursor to raise tempo and keep reviews light
- Or use VS Code + Copilot to layer AI onto existing extensions
Either can feel smooth.
For mid-sized teams (6–30) who want to reduce person-dependence
- Use VS Code + Copilot to establish controls (approval/Undo/mode separation)
- Use Kiro to introduce spec-driven alignment and stronger consensus per feature
This is often the most realistic path.
For large teams (31+) that need async + supervision
- Use Antigravity to run multiple agents in parallel and review via Artifacts
- Use Kiro to automate quality with specs + hooks
The “delegate and supervise” model becomes more valuable as teams scale, especially for long maintenance work.
9) Concrete usage scenarios (short examples)
Scenario A: daily small changes on an existing project (frontend-heavy)
- Recommended: Cursor
- Why: faster completion and edits increase iteration speed
- Tip: include short acceptance criteria for UI changes (visual, accessibility, responsive)
Scenario B: introduce AI without disrupting enterprise VS Code operations
- Recommended: VS Code + Copilot
- Why: low adoption friction and strong control over edits/autonomy
- Tip: start with edit mode → move to agent mode after the team is comfortable
Scenario C: move from prototype to production while preserving requirements and design
- Recommended: Kiro
- Why: spec-driven workflow preserves “what we’re building” as a deliverable and makes task management easier
- Tip: don’t try to perfect the spec upfront—grow it in stages (req → design → tasks)
Scenario D: run multiple maintenance tasks in parallel and supervise via reviews
- Recommended: Antigravity
- Why: run multiple agents via Manager Surface and validate via Artifacts
- Tip: template your Artifact review criteria (“what must be proven?”)
Summary: choose based on “repeatability,” not raw speed
The most important thing in AI coding tool selection isn’t “it worked once,” but “we can run it at the same quality again and again.”
- If personal speed is top priority, Cursor’s in-editor acceleration hits just right.
- If you want gradual adoption while preserving existing assets, VS Code + Copilot adapts well to real teams.
- If you want to reach production quality via specs and automation, Kiro’s specs and hooks shine.
- If you want to supervise multiple agents asynchronously and validate via deliverables, Antigravity is strong.
Finally, small tips that work across tools:
- Requests should be short and include the “goal, scope, acceptance criteria” trio
- Make changes in small increments and assume diff review
- The more you delegate, the more you should prepare rules upfront (naming, tests, forbidden actions)
That alone makes AI much easier to “work with.”