Google has significantly expanded Antigravity, its AI-powered integrated development environment (IDE), with new genomics research capabilities, multi-tier agent reasoning, and visual artifact generation, positioning it as a tool for both software development and scientific research. However, a hands-on comparison of three leading AI coding tools reveals that while Antigravity introduces compelling features, developer experience remains fragmented across competing platforms.

What Makes Antigravity's New Genomics Features Significant?

Antigravity now integrates AlphaGenome and AlphaGenome Atlas Skills, connecting developers and researchers to a one-petabyte database containing precomputed predictions for 9 billion single-nucleotide variants, which are single-letter changes in human DNA. This integration allows researchers to query genetic predictions without running underlying machine-learning models for every variant, dramatically accelerating genomics workflows.

The new genomics capabilities support several research-focused tasks. Researchers can prioritize large lists of uncharacterized variants using the AlphaGenome Variant Impact (AVI) score, describe why specific variants rank highly and identify predicted disruptions such as splicing defects, create reference-versus-alternate allele plots directly inside the chat interface, and generate parameterized links that open relevant tracks on the Atlas website. Google emphasizes that AlphaGenome remains research-only and lacks validation and regulatory approval for clinical diagnosis.

How Does Antigravity's New Multi-Agent Reasoning Compare to Competitors?

Antigravity now offers three distinct levels of agent execution designed for different project scopes. The default mode executes a single prompt immediately, suited for feature work and interactive pair programming. The new /boost command applies deeper multi-agent reasoning without requiring a scoping interview, targeting concurrency bugs, algorithms, and substantial refactoring. The /teamwork-preview mode runs a two-phase scoping interview before coordinating a larger agent team, designed for large, open-ended, or multi-day projects.

When invoked, /boost creates a three-tier hierarchy consisting of a top-level Orchestrator, DeepCoder or DeepInvestigator coordinators, and isolated execution workers, with independent verification loops checking the resulting work. Sessions can last from seconds to hours, though the mode consumes more tokens than standard execution. Notably, /teamwork-preview, previously restricted to the $200-per-month Ultra plan, is now available on all paid plans.

Despite these enhancements, a developer who spent a month testing Antigravity alongside VS Code and Cursor found the experience fragmented. Antigravity 2.0 splits the interface into an agent-centric command center and a separate traditional IDE, requiring developers to jump between two environments when they want to intervene manually. While Antigravity supports message queues and scheduled tasks, allowing developers to delegate work and automate repetitive checks, the disconnect between agent and code editor remains a friction point.

Why Did One Developer Prefer Cursor Over Antigravity and VS Code?

After a month of daily use, the developer found that Cursor 3 achieved the most balanced approach to AI-assisted coding. Unlike Antigravity, Cursor hasn't forced a choice between working with agents and working inside a traditional IDE. The new Agents Window serves as the main interface, allowing developers to run multiple agents in parallel across different repositories and environments while keeping the ability to jump straight back into the IDE whenever manual control is needed.

Cursor's architecture allows developers to keep both the agent interface and the traditional IDE open simultaneously, which the developer described as "exactly how I think an AI coding tool should work". The Customize section consolidates plugins, Model Context Protocol (MCP) servers, rules, commands, hooks, subagents, and Skills in one location. Skills are particularly useful because they allow developers to teach the agent a repeatable workflow, such as how to test or deploy a project, rather than explaining the same process repeatedly.

Automations emerged as a standout feature, enabling agents to run on a schedule or react to events from services like GitHub and Slack, all accessible directly within the Agents window. The developer also noted preferring Cursor's Grok 4.6 model to the Gemini experience in Antigravity and Copilot in VS Code, citing better performance on complicated instructions and longer jobs.

How to Evaluate AI Coding Tools for Your Workflow

• Agent-IDE Integration: Assess whether the tool allows seamless switching between automated agent work and manual code editing without losing session context or requiring navigation between separate interfaces.
• Parallel Execution Capability: Check if the platform supports running multiple agents simultaneously across different projects or repositories, enabling efficient delegation of concurrent tasks.
• Customization and Extensibility: Evaluate whether the tool provides centralized management of plugins, model integrations, custom rules, and reusable workflows that match your team's specific processes.
• Reasoning Depth Options: Determine if the platform offers tiered reasoning modes for different problem complexities, from quick feature work to deep multi-agent reasoning for complex bugs and refactoring.
• Automation and Scheduling: Look for built-in support for scheduled tasks, event-driven triggers, and message queues that reduce manual intervention and enable background work delegation.

VS Code remains the most familiar and dependable option for developers already invested in the Microsoft ecosystem, offering an Agents Window that manages sessions across multiple projects and allows parallel agent tasks. However, its gradual accumulation of AI features, including agents, Chat, extensions, models, and sessions, can make the interface feel cluttered. The developer noted that VS Code sometimes feels like "a traditional editor that has gradually received AI features rather than something designed around AI from day one".

Antigravity's strengths include smart queue-based prompt management and Actions for automating repetitive checks, but the separation between the agent command center and the traditional IDE creates friction when developers need to intervene manually. The new genomics integration and expanded multi-agent reasoning capabilities position Antigravity as a specialized tool for research and complex multi-day projects, but the fragmented interface remains a limitation for everyday coding workflows.

The broader takeaway from this comparison is that AI coding tools are converging on agent-first architectures, but execution matters significantly. Developers increasingly expect to delegate work to AI agents while maintaining the ability to take manual control without context loss or interface switching. Antigravity's expansion into genomics research and deeper multi-agent reasoning demonstrates Google's ambition to serve both software development and scientific domains, yet the developer experience still lags behind platforms that have unified agent and traditional IDE workflows from the ground up.