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.

"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. The core of this offering revolves around Gemini CLI, Google Antigravity, and Gemini Enterprise, which can transform how developers interact with their codebase.

Gemini CLI changes the dynamic from asking for help to assigning work. Here is how it manages agentic tasks:

• Tool use: The agent can autonomously run commands like ls, grep, and cat. It can also write directly to files. Instead of suggesting you run a test, it runs npm test itself and reads the logs.
• Memory and context: It supports a GEMINI.md file in your project root. This file acts as long-term memory or a system prompt where you define coding standards, architecture rules, or specific "do not do this" instructions that the agent follows for every task.
• Self-correction: If the agent writes code that fails a build, it sees the error message in the terminal. It then reasons about why the failure occurred and attempts a different solution automatically.
• Extensibility (MCP): It supports the Model Context Protocol (MCP). This allows you to connect the agent to external data sources like PostgreSQL, GitHub, or Slack, so it can fetch context from outside your local file system.

Gemini CLI 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 an IDE with an assistant to a dedicated agentic development platform. Powered by Gemini 3, it treats AI agents as primary workers rather than simple helpers.

• Manager view (mission control): Developers can use a centralized dashboard to spawn, orchestrate, and observe multiple agents working in parallel across different workspaces. You can assign one agent to research documentation while another refactors a codebase.
• Verifiable artifacts: Instead of opaque logs, Antigravity agents generate structured "Artifacts"—verifiable records of their plans, code changes, and test results. This can provide transparency, allowing developers to audit the agent's reasoning and "proof of work" quickly.
• Browser and terminal autonomy: Antigravity agents can possess the ability to control the browser for visual verification and execute complex terminal commands, enabling end-to-end task completion that includes deployment and testing.

For organizations deploying agents at scale, Gemini Enterprise helps provide the necessary layer of governance and integration.

• Centralized guardrails: Administrators can define policy-as-code that agents must adhere to, ensuring that no agentic workflow violates company compliance or security standards.
• Private knowledge grounding: Agents can be grounded in your organization's private codebase and documentation, allowing them to write code that respects internal libraries and architectural patterns without leaking data.
• Indemnification and compliance: Gemini Enterprise can offer intellectual property indemnification and meets rigorous compliance certifications, making it safe for large enterprises to adopt agentic coding in production environments.

To understand the power of agentic coding, compare a standard AI chat experience with Gemini CLI agent workflow.

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): gemini "Add a rate limiter to the server using express-rate-limit and ensure it passes tests."

Gemini CLI:

• Reads package.json to check dependencies.
• Runs npm install express-rate-limit.
• Edits server.js to import and configure the middleware.
• Runs npm test.
• (If test fails) Reads the error log, edits the code again, and runs the test again.

Final Output: "Task complete. I installed the package and updated server.js. Tests are passing."

Gemini CLI is an open-source tool that is separate from the standard Google Cloud SDK.

Installation: You need Node.js (version 18 or higher) installed.

Authentication: Run the command once to sign in with your Google account. No API key is needed for the free tier.