The Rise of Agentic AI Coding Tools

Google just dropped Gemini CLI. Anthropic launched Claude Code.
And they’re not just helping developers write code—they’re writing, running, testing, and shipping software on their own.

A quiet revolution is unfolding in software development: AI coding tools are no longer assistants. They’re becoming autonomous agents—capable of reading entire codebases, fixing bugs, running shell commands, and even submitting PRs.

If that sounds wild, you’re not alone.

In 2023, GitHub Copilot helped devs autocomplete lines of code.
In 2025, Gemini CLI and Claude Code let you say:

This isn’t just a developer trend. It’s the rise of a new AI-powered stack—one that engineering leaders, product owners, and business strategists need to understand to stay competitive.

In this post, I’ll break it all down for you:

• What these agentic AI tools actually do

• Where they live (terminal, IDE, PR reviews)

• Which ones matter for your team

• And how to start using them—safely and strategically

Let’s dive in.

🛠️ What Are “Agentic” AI Tools?

When OpenAI released Codex in 2021, it introduced the idea that LLMs could translate natural language to code. GitHub Copilot soon followed, offering real-time suggestions in your IDE. But these were passive — autocomplete with a brain.

In contrast, agentic tools go further and can:

🔍 Understand context (like your code, files, tasks)
⚙️ Take actions (like editing files or running commands)
📋 Make decisions (like whether a change needs a test)
🧠 Chain together tasks and workflows

AI-native developer tools are evolving into powerful assistants that act more like team members than tools.

These agents come in 3 main flavors:

1️⃣ Terminal-Based AI Agents (For developers who live in the command line)

These tools work inside the terminal (where devs run code or commands) and are perfect for automation and large-scale tasks.

• 🔧 OpenAI Codex CLI:
  It reads your code and responds to instructions like:
  “Add a login system” → AI writes code, runs it, tests it—all inside your repo.

  You can set it to “ask first” or let it run automatically. Sandbox security ensures it doesn’t break things.

• 🧠 Claude Code CLI (Anthropic):
  Can fix bugs, search git history, auto-create PRs, and summarize architecture. Ideal for larger projects. Comes with a developer SDK and integrates into custom tools. It can even browse the web for help when needed.

• 🔍 Gemini CLI (Google):
  Connects to Google’s AI with a huge memory window (can “read” your entire repo). Free to use and very powerful—great for dynamic debugging, creating files, or automating workflows like “fix all TODOs.”

• 🌱 Aider (Open-source):
  Lightweight and simple. Perfect for privacy-focused teams. Works with GPT-4 or local models. Less powerful than the others, but fast and transparent.

👥 Why it matters for teams:
These tools can take manual tasks (upgrading dependencies, applying bulk changes, generating configs) and make them fully or semi-automated. Even if your team isn’t using them yet, early adopters are seeing 2x–5x speed-ups for repetitive engineering work.

2️⃣ IDE-Native Agents (For coding directly in the editor)

These live inside VS Code, JetBrains, or custom editors and help developers stay “in flow” as they code.

• 🖊️ Cursor:
  A modern AI-native code editor. Lets you talk to your code: “Refactor this to use async functions” or “Explain this bug.” Supports Claude, GPT-4, and its own model. Used by devs at Shopify and Midjourney. Pro version adds more power, but the free version is great for everyday use.

• 🌊 Windsurf (ex-Codeium):
  Comes with its own coding AI agent (Cascade) that plans steps, executes code, fixes errors, and auto-iterates. Think of it like an AI workflow assistant: “Build me a dashboard” → it writes code, tests it, and shows a live preview. Has memory and rules for your team’s coding style.

• 💬 Gemini Code Assist:
  Google’s IDE assistant that goes beyond autocomplete. In “Agent Mode,” it builds multi-step plans and executes across your codebase. Bonus: unlimited free use for individuals.

• 👨‍💻 GitHub Copilot:
  The OG AI assistant. Still mainly suggests code, but adding features like PR summaries and inline explanations. Soon will be able to edit more autonomously.

👥 Why it matters:
Think of these tools as smart copilots that help devs write better code, faster. They reduce context-switching, explain code to juniors, and can even reduce time spent on code reviews.

3️⃣ AI Code Reviewers (For speeding up PRs and improving quality)

These tools act like robotic reviewers who analyze code, highlight bugs, and sometimes even push fixes.

• 🧪 CodeRabbit:
  Hooks into GitHub/GitLab. When a developer opens a pull request, it runs tests, linters, and security checks in a sandbox—and comments on potential issues. Some teams report 50% faster reviews and fewer bugs post-merge.

• 🔁 Ellipsis:
  Another PR bot that can catch issues and auto-push fixes with maintainer approval. Focuses on logic bugs and documentation mismatches.

👥 For teams:
These tools are productivity multipliers. They don’t replace human reviews—but they pre-check everything so engineers can focus on big-picture feedback instead of pointing out missing semicolons.

🧩 Build-Your-Own AI Dev Stack

For teams who want to go deeper:

• Claude SDK + Model Context Protocol (MCP):
  Build custom agents. Example: Create an internal agent that automatically documents new APIs in your preferred format.

• OpenAI Function Calling:
  Define actions like run_tests() or read_file() and let GPT-4 perform multi-step tasks programmatically.

• LangChain / AutoGPT setups:
  Use multi-agent flows (e.g., one agent writes specs, another codes, another reviews).

🚀 5 Strategic Takeaways

1. You Don’t Need to Go All-In to Start

Use tools like Gemini CLI or Cursor in “suggest-only” mode. No risk. Let your team play with it on side projects or boilerplate work.

2. Mix & Match for Maximum ROI

Use different tools for different parts of the dev lifecycle:
Copilot to write → Claude CLI to refactor → CodeRabbit to review.

3. Don’t Let AI Write Code Blindly

Have processes in place. Review all AI code. Use tests. Think of AI like a junior dev—it’s fast, but needs oversight.

4. Customize the Agent to Your Company

Add files like CLAUDE.md or GEMINI.md in your repo that include context, preferences, and coding rules. These agents will actually read and follow them.

5. Empower Your Team—Don’t Replace Them

AI assistants are force multipliers. They let your best engineers move faster and help your juniors learn by example. Let the AI handle the grunt work, while your team handles strategy, design, and complex problem solving.

📊 Where This is All Headed

• Agent Teams: You might soon have multiple AI agents per dev—one for writing code, one for testing, one for infrastructure.

• IDE Memory: Tools will remember decisions across sessions. (“Why did we do this?” — answered.)

• Unified AI Dev Platforms: From code to deployment, AI may cover everything. GitHub and Google are already building this future.

• Open Source Disruption: Local models like Code Llama and StarCoder are improving fast. A GPT-4-level open model could change the game.

📥 Closing Thoughts

The tools are here. The shift is happening. Whether you’re a hands-on engineer or a VP of Product, understanding agentic AI tools is no longer optional.

🧠 They’re not replacing your team. They’re augmenting them.
🏗️ They’re not a gimmick. They’re becoming infrastructure.

If you want to keep your team competitive in 2025 and beyond, this new AI dev stack deserves a seat at the table.