What is agentic coding?

A coding agent is an advanced software program powered by a large language model (LLM) that performs software development tasks autonomously. These agents don’t just generate text; they use a process often called a "reason and act" loop. When given a goal, the agent breaks the request into smaller, manageable sub-tasks. It then uses specific tools to accomplish these tasks, such as accessing the file system, running bash commands, or interacting with version control systems.

The defining feature of a coding agent is its iterative feedback loop. It doesn’t simply output code and stop. Instead, it performs automated operations to verify its work. For example, an agent might write a test case, run the code, observe a failure, and then rewrite the code to pass the test. This ability to self-correct allows coding agents to help handle complex instructions that could otherwise confuse standard text-based AI models. The underlying software that controls this reasoning loop is known as an agentic harness.

"Vibe coding" is a term that describes a coding experience characterized by a highly fluid, intuitive, and distraction-free state of flow. It refers to a method of coding where you focus entirely on the logic and creativity—the "vibe" of the application—without getting bogged down by syntax errors or boilerplate code.

Agentic coding, by contrast, is the technological methodology that often enables this state. It’s the structured, autonomous process where the AI handles the execution. While vibe coding is the goal or the feeling, agentic coding is the engine. "Agentic vibe coding" implies using these autonomous agents to help handle the heavy lifting, allowing you to remain in that creative flow state without interruption.

Google Cloud offers tools that support the agentic coding workflow, designed to bring autonomy to your development environment safely and securely. The core of this offering is Google Antigravity in Gemini Enterprise (as the AI Developer Tools toolkit). This suite transforms how developers interact with their codebase by treating AI agents as primary, multi-step workers.

Google Antigravity (available as a desktop Hub or CLI) changes the dynamic from asking for help to assigning work. Here is how it manages agentic tasks:

• Tool use and autonomy: The agent can autonomously run terminal commands, inspect logs, and write directly to files. Instead of waiting for you to run a test, Antigravity executes tasks natively and reads verification logs. The agent supports different modes of operation, depending on how closely you want to monitor each action.
• Verifiable artifacts: Solves the "trust gap." Before writing code, the agent generates a structured implementation plan and task list. Once completed, it delivers a walkthrough artifact proving its work.
• Progressive disclosure (skills): Uses dormant, directory-based instruction packages (SKILL.md). Instead of filling context windows with all rules at once, specialized instructions (like PR review checklist or DB migration protocols) load dynamically only when the specific task requires it.
• Extensibility (MCP): Supports the Model Context Protocol (MCP) to connect agents to external data sources like PostgreSQL, GitHub, or Cloud Run.

Antigravity adapts to the stage of your development lifecycle, offering distinct advantages whether you are building from scratch or maintaining an established codebase.

Google Antigravity represents a shift from standard AI assistants embedded in an IDE to a dedicated, standalone agentic development platform. Available within Gemini Enterprise as part of its AI Developer tools, it treats AI agents as primary, multi-step workers rather than passive helpers. Enterprise engineering teams can use Antigravity autonomous capabilities through two primary interfaces: Antigravity 2.0 (Hub) and the Antigravity CLI.

• Manager view and orchestration (Antigravity 2.0 Hub): Developers can use a centralized, dedicated dashboard to spawn, orchestrate, and observe multiple local agents working in parallel across different multi-folder contexts. You can assign one agent to research documentation or dependencies while another refactors a backend module.
• Verifiable artifacts for auditing: Instead of opaque runtime logs, Antigravity agents generate structured "Artifacts", which are verifiable records of their implementation plans, code changes, and test results. This provides full transparency, allowing developers to audit the agent's reasoning and "proof of work" quickly to meet enterprise compliance standards.
• Browser and terminal autonomy: Antigravity agents can possess the ability to control local browsers for visual verification, install packages, and execute complex terminal commands, enabling end-to-end task completion that spans from coding and debugging all the way to testing.
• Secure CLI automation: With native Antigravity CLI support, these agentic workflows can be plugged directly into developer terminals or automated CI/CD pipelines, streamlining brownfield modernization or boilerplate generation while remaining governed under your organizations Gemini Enterprise billing and quota management.

To understand the power of agentic coding, compare a standard AI chat experience with an autonomous agentic pipeline (Antigravity CLI).

1. You: "How do I add a rate limiter to my Express app?".
2. AI: "Here is a code snippet using express-rate-limit...".
3. You: Copy the code, open your editor, paste the code, run the server.
4. You: See a crash. Copy the error message.
5. You: Paste the error back to the AI.

You (in terminal): agy "Add a rate limiter to the server using express-rate-limit and ensure it passes tests." 

Antigravity CLI:

1. Reads package.json to check dependencies.
2. Runs package installation.
3. Edits server.js to configure middleware.
4. Runs automated testing suites.
5. Detects failures, corrects code, and regenerates tests automatically. Final Output: Delivers completion notification alongside verification artifacts and logs.