Google Antigravity vs Cursor

If you’re evaluating Google Antigravity vs Cursor, you’re deciding how AI will participate in your team’s coding loop. Cursor’s momentum, including $500M ARR, shows how quickly these tools are moving from pilot to production.

Antigravity is Google’s new agent-first IDE built around Gemini 3. It’s designed to let agents plan work, execute tasks, and leave verifiable Artifacts like task lists, plans, screenshots, and browser recordings, with a Manager view that functions like mission control for orchestrating multiple agents. Cursor, by contrast, is a widely adopted AI code editor and agent platform; at NVIDIA, more than 30,000 engineers are reported to rely on the setup with a claimed 3x code output.

That makes this a comparison of philosophies as much as features: Antigravity emphasizes verifiability and multi-agent orchestration, while Cursor demonstrates what mature, AI-augmented workflows can deliver at scale. Depending on whether you prioritize transparent agent outputs or proven velocity gains, the “better” fit can look different.

To help you decide, we’ll compare agentic capabilities and reviewability, developer experience and ergonomics, integrations and collaboration surfaces, and enterprise governance and rollout complexity. We’ll ground the analysis in how each approach supports day-to-day coding, code review, testing, and cross-team coordination.

First, a quick at-a-glance overview to set the stage for the deeper dive.

Quick Overview and At a Glance Comparison Table

Before you trial either tool, it helps to see how they differ on approach, verification, integrations, and real-world outcomes—so you can match fit to your team’s goals rather than just features.

Antigravity puts autonomous agents at the center of the IDE and emphasizes verifiable outputs. As agents work, they leave a reviewable trail of Artifacts such as task lists, plans, screenshots, and browser recordings, which makes audits and handoffs clearer. If your priority is traceability across multi-step runs and confidence in what the agent actually did, Antigravity’s artifact-first model will stand out.

Cursor blends a familiar editor workflow with background automation and lightweight collaboration hooks. Teams can trigger and manage work via Slack agents by mentioning @Cursor, keeping activity in the tools engineers already use. Cursor’s commercial traction—$500M ARR—signals maturity, and in production environments like NVIDIA, reports cite 3x code output, making it attractive for teams seeking immediate velocity.

If you value transparent, step-by-step verification and multi-agent oversight above all, Antigravity is the better fit. If your priority is proven speed with minimal workflow change and strong chat-based collaboration, Cursor gets the nod.

Key Takeaways:

• Antigravity wins on traceability: its agent runs produce verifiable Artifacts you can audit end-to-end.
• Cursor leads on adoption and outcomes: Slack-native triggers plus $500M ARR and reported 3x code output indicate scale and ROI.
• Choose by priority: verification-first multi-agent workflows (Antigravity) vs. near-term velocity in existing tooling with Slack agents (Cursor).

Features, Agentic Capabilities, and Development Workflows

Agentic design determines how confidently you can offload work to AI while keeping humans in the loop. The right workflow should combine autonomy with transparent review so teams can scale output without sacrificing control.

Antigravity centers the IDE around agents, not just prompts, positioning itself as an agent-first development platform. As agents plan and execute tasks, they leave verifiable Artifacts like task lists, implementation plans, screenshots, and browser recordings, making each step auditable and easy to hand off across teammates; see Google’s description of Artifacts. For larger efforts, the Manager view works like mission control to spawn, orchestrate, and observe multiple agents operating in parallel across workspaces, which is especially useful when you need coordinated sub-tasks with clear provenance.

Cursor emphasizes controllable automation inside a familiar editor, exposing the agent loop to teams through enterprise-grade controls such as Hooks to observe and extend runs, and Sandbox Mode to execute terminal commands safely. These guardrails support day-to-day tasks like code generation, refactoring, and review without requiring teams to adopt a brand-new mental model. In production, NVIDIA reports its internal code commits tripled after rolling out AI assistance, and teams use Cursor for code reviews, test generation, and QA—evidence that this workflow scales across the software lifecycle.

If your priority is transparent, multi-agent execution with a rich audit trail, Antigravity’s artifact-first orchestration gives you deeper oversight. If you want immediately operational agents with strong safety and control inside an editor your team already knows, Cursor’s enterprise controls and field-tested flow are the faster on-ramp.

Key Takeaways:

• Antigravity: best when you need multi-agent orchestration plus a reviewable trail through audit-friendly Artifacts.
• Cursor: best when you want controlled automation via editor-native guardrails like Hooks and sandboxed execution.
• Choose by workflow fit: verifiable, mission-controlled runs (Antigravity) vs. fast, operational agent loops inside today’s coding habits (Cursor).

Developer Experience, Integrations, and Enterprise Readiness

Developer experience, integrations, and enterprise readiness determine whether AI tools actually stick after the pilot. Smooth ergonomics paired with credible security and governance speed adoption and reduce rollout risk.

Antigravity approaches DX with an agent‑first flow: Google positions it as an agentic development platform that offloads multi‑step work to autonomous agents. Oversight happens in a mission control view that lets you create, orchestrate, and monitor multiple agents in parallel. For teams, this keeps planning, execution, and the resulting artifacts inside one interface that favors reviewability over context‑switching.

Cursor optimizes for familiarity, keeping the core workflow editor‑centric while adding automation behind the scenes. For security and procurement, it brings SOC 2 Type II certification along with zero‑data‑retention options that many enterprises require. Its commercial maturity—$500M ARR and adoption by over half of the Fortune 500—signals ready playbooks for rollout, training, and integration with existing stacks.

If you prize deep, in‑IDE oversight and a clear audit trail for multi‑agent work, Antigravity’s mission‑control model fits that preference. If you need a lower‑friction path through security reviews and company‑wide enablement today, Cursor’s compliance posture and broad enterprise footprint make it the safer, faster bet.

Key Takeaways:

• Antigravity emphasizes supervision: a mission‑control view and in‑IDE review loop support high‑trust, multi‑agent development.
• Cursor emphasizes readiness: compliance, data controls, and proven enterprise adoption ease procurement and scale‑out.
• Choose by rollout risk: hands‑on oversight and artifact‑heavy reviews (Antigravity) vs. quicker enterprise clearance and familiar workflows (Cursor).

Key Differences, Use Cases, and Final Recommendation

At this stage, the choice hinges on what you optimize for: transparent autonomy you can audit, or speed that’s already proven in production. Framing it as reviewability versus throughput clarifies each tool’s trade-offs for your roadmap and risk tolerance.

Antigravity’s defining edge is verifiability. As agents work, they produce reviewable Artifacts such as task lists, plans, screenshots, and browser recordings, so every step is inspectable and easy to hand off. Its Manager view functions like mission control to orchestrate multiple agents across workspaces in parallel, which suits coordinated changes and long-running refactors. Teams exploring higher‑autonomy pipelines can lean on autonomous agents that plan, code, run tools, and explain their own work, supporting complex, multi‑stage tasks with a clear paper trail.

Cursor’s strength is production proof. The company reports $500M ARR, a signal of broad adoption and sustained day‑to‑day use. In a large-scale deployment at NVIDIA, internal code commits have tripled after rolling out AI‑assisted programming, pointing to real throughput gains. Practically, Cursor fits into the familiar editor-driven loop for tasks like code generation, refactors, and reviews, minimizing change management while raising output.

If you are running multi-agent programs where provenance and oversight matter most, Antigravity is the better fit. If your priority is measurable speed with minimal process change, especially at enterprise scale, Cursor is the safer near‑term choice.

Key Takeaways:

• Antigravity: best for inspectable multi‑agent runs and artifact‑backed handoffs in complex, coordinated work.
• Cursor: best for rapid throughput and enterprise‑proven deployment with minimal workflow disruption.
• Choose by goal: verifiable autonomy and orchestration (Antigravity) vs immediate delivery gains in familiar loops (Cursor).

From Comparison to Capability with DeepStation

You’ve weighed Google Antigravity’s verifiable, agent‑first model against Cursor’s proven throughput and enterprise readiness. DeepStation accelerates AI education and innovation through the power of community—hands‑on labs, live workshops, and peer reviews—so your team can pilot both tools side by side, master agentic patterns and governance, and turn insights into rollout playbooks that actually stick.

Ready to move from trial to production? Cohorts are filling fast—Join the AI Developer Community today! Close the loop on your Antigravity vs Cursor decision with DeepStation’s practical curricula, code labs, and ongoing support, and start shipping with confidence.