AI in Coding, Part 2: AI Tools Every Engineer Should Know in 2025

AI is no longer just a research curiosity or a cool side experiment. In 2025, it is a daily part of many engineers’ workflows. More than half of developers already use AI coding assistants every day, and adoption keeps growing.

But not all tools are equal. Some focus on generating code, others on testing or debugging, and some on productivity beyond coding. Understanding the landscape helps us pick the right tool for the right stage of software development.

Four Categories, but Lots of Overlap

In 2025, AI touches almost every part of the development lifecycle. To make sense of the landscape, it helps to think of the tools in four categories:

1. Code generation
2. Testing & review
3. Debugging & deployment
4. Documentation & productivity

These categories aren’t hard boundaries. Many tools overlap and can be used across stages. For example, you can write infrastructure-as-code directly in Cursor, or use Copilot to generate unit tests. The division here is meant to give structure, not to box tools in.

It is also worth noting that your AI stack does not, and probably should not, be vast. It is better to pick a handful of tools that cover most of your workflow and learn how to make the best out of them, rather than lose focus by trying to master 20 different tools. Most rely on the same models under the hood anyway. The real opportunity lies not in chasing tools, but in learning how to maximize what the models can do for you through the tools you choose.

Alongside this, it is important to be careful about which tools you let into your workflow. The AI market is full of wrappers — tools that simply sit on top of an existing model or an existing product, add a basic interface, and market themselves as something new. Many of them are buggy, unreliable, or offer little beyond what you could do with the base model itself.

If you’ve seen “disappear for 28 days and become the most dangerous person in the room” type ads, you know the type: they usually teach you to use regular productivity tools like Canva or Miro boards with an AI interface bolted on. There’s nothing wrong with those tools, but they are not breakthroughs. They won’t turn you into a 10x engineer, and they often distract from the real advances happening in AI for coding.

The focus should remain on high-quality tools that integrate well into your development lifecycle, not on hype-driven add-ons.

Code Generation: Writing Faster, Smarter

These tools focus on writing code for you, from simple autocomplete to full function and module generation.

GitHub Copilot – The most widely adopted AI pair programmer, powered by OpenAI Codex.

ChatGPT (GPT-4/5 with code capabilities) – Generates snippets, explanations, and even entire applications.

Cursor IDE – An AI-native editor where code completion, refactoring, and context understanding are first-class citizens.

Replit Ghostwriter – Integrated directly into Replit for rapid prototyping.

Tabnine – Predictive autocomplete with enterprise features.

Amazon CodeWhisperer – AWS’s coding assistant, great for cloud-native development.

Sourcegraph Cody – Combines AI with powerful code search.

Use case: You type “write a Python function to parse a CSV and return JSON” and the AI writes it in seconds.

Testing and Code Review: Catching Problems Early

AI is also learning how to test and review code, automating some of the most repetitive but important parts of the process.

Snyk Code AI (formerly DeepCode) – Security-focused static analysis with AI driven suggestions.

CodiumAI – Generates meaningful unit and integration tests.

Diffblue Cover – Auto-generates unit tests for Java.

Testim (by Tricentis) – AI for web and API testing.

Mabl – Cloud based, AI powered end to end testing.

Use case: Instead of manually writing unit tests for every function, AI proposes them automatically, cutting hours of repetitive work.

Debugging and Deployment: Smarter Operations

Once code is written, AI can also help us deploy, monitor, and fix issues.

Amazon CodeGuru – Finds performance bottlenecks and recommends fixes.

Azure Copilot for DevOps – Integrates into pipelines for smarter automation.

Harness.io – CI/CD with anomaly detection and rollback suggestions.

Komodor – Helps debug Kubernetes clusters with AI powered insights.

Dynatrace Davis AI – Monitors systems and finds root causes.

Use case: Instead of staring at logs for hours, AI highlights which service failed, why, and even proposes a fix.

Documentation and Productivity Assistants

AI isn’t only about writing code — it also makes it easier to keep projects understandable and teams aligned.

Mintlify – Generates developer documentation from code.

Swimm – Keeps documentation in sync with the codebase.

Documatic – Lets you query your codebase in natural language.

JetBrains AI Assistant – Inline explanations and docs inside popular JetBrains IDEs.

Notion AI – Assists with project planning, documentation, and collaboration.

Use case: Instead of manually writing API documentation, AI auto-generates clean, usable docs based on code annotations.

Why This Matters

AI now touches every part of the software lifecycle:

• Code generation reduces boilerplate.
• Testing & review cuts down bugs before they reach production.
• Debugging & deployment makes operations more efficient.
• Documentation & productivity tools ensure projects are understandable and maintainable.

But remember, these categories overlap. The real power of these tools is not in fitting them into neat boxes, but in knowing how to mix and match them for your workflow. And even more important — you don’t need them all. Focus on a small set of tools that cover your needs, then get really good at leveraging them. That is where the real productivity boost lies.

✅ Key Takeaway

AI is no longer just autocomplete. It has matured into a broad ecosystem that covers almost every stage of software engineering. Knowing which tools exist, how they overlap, and how to focus on a handful of the right ones for your workflow can multiply your productivity.

This article is part of my series “AI in Coding – From Past to Future.”

🎥 Watch the Full Lecture

For a deeper dive into this topic, watch my complete presentation on AI in software development:

Note: The introduction is in Bosnian, but the main lecture is in English and starts around the 3-minute mark.

This lecture covers all the topics in this series and provides additional insights and Q&A.

Full Series:

• Part 1: From Lisp to Copilot: The Evolution of AI in Software Development
• Part 2: AI Tools Every Engineer Should Know in 2025 (this post)
• Part 3: Coding with AI: Best Practices (Coming October 6)
• Part 4: I Let AI Build a Web App Live (Coming October 9)
• Part 5: AI in Coding: How Far We’ve Come (Coming October 13)
• Part 6: The Next 10 Years of Coding (Coming October 16)
• Part 7: Will AI Replace Programmers? (Coming October 20)
• Part 8: Future-Proofing Your Career (Coming October 23)

Which AI tools are you currently using in your development workflow? Have you found the sweet spot between too few and too many tools? I’d love to hear about your AI toolstack and what’s working best for you.