---
title:

How to Enter AI Development as a Developer in 2026

date: 2026-01-30
tags: [#ai, #programming, #tools, #agents, #vibe-coding, #cursor, #mcp, #best-practices, #editors, #development ]
draft: false
---

Overview

At the beginning of 2025, the baseline was chatbots working on individual files and smart autocomplete. By early 2026, the baseline is agents that work with the entire project and can perform complex tasks by independently gathering information, calling external tools (via MCP), and other agents. The market has split into advanced IDEs, cloud “Text-to-App” platforms, and powerful terminal tools. The agents skills standard is spreading for packaging specialized instructions for agents.

Two Paradigms: Vibe Coding and Assisted Coding

  • Vibe Coding: High-level intent management and orchestration of autonomous agents. Full transfer of code ownership to the agent. Focus on the result (“make it work”) rather than implementation. Vibe coding is perfect for PoCs, internal utilities, small DIY projects, and other throwaway code. Assisted Coding is for full-scale projects and complex tasks. In 2026, the line is blurring: we are moving from “Vibe Coding” to “Viable Code”—a practice where prototyping speed is combined with automated quality control. The size of projects that can be vibe-coded is increasing with improvements in models and context engineering.
  • Assisted Coding: Helping the developer write code using AI without losing control. The developer remains the chief architect and censor. This is closer to classical programming but with AI assistance. The developer sets the task, checks the plan, and controls the code generated by the agent.

Different Types of IDEs: Text-to-App, CLI/Cloud, GUI

I would recommend starting with CLI and GUI. Try, for example, OpenCode/Claude Code and Antigravity (or Cursor). OpenCode is a great OSS tool where new ideas are implemented quickly, but it’s more expensive as it works through your API key. Claude Code is similar but cheaper because Anthropic is aggressive with subscription pricing. Antigravity has the best UX (though it seems to lack subagents for now). Read the documentation, try them in parallel on small and large projects to “feel” the difference.

Project Preparation

In 2026, the success of working with AI agents depends half on how well the project is prepared for them. An agent needs accurate context and a fast feedback loop.

1. Context Engineering and Documentation

To prevent the agent from hallucinating or suggesting alien solutions, you need to create “anchor points” in the form of documentation files:

  • AGENTS.md / Project Rules: The main file describing the architecture, tech stack, and general behavior rules. This is the “constitution” of your project for AI.
  • Skills: Description of specific skills and workflows (e.g., how to write tests or deploy the app). To avoid cluttering the main agent’s context, specific skills can be moved to separate files (Cursor Skills).
  • Permanent Memory: Context files to be used by the agent. I use a separate documents folder with SRS, SDS, and other documentation. For some reason, this isn’t in IDEs yet, except for Cline.
  • External Resources: Most IDEs have built-in mechanisms for HTTP requests and even web searches. But it’s “expensive.” There are ready-made MCP servers for external resources: context7, GitMCP, mcpdoc, etc. Cursor can index external resources and perform requests to them “out of the box.”

2. Reference Implementation

To set the coding style and architectural constraints, you need a reference implementation. This could be old code that already works or a part of a new project written manually.

3. Structure and Cleanliness

  • Modularity: The clearer the separation of responsibilities in the code, the easier it is for the agent to localize changes without breaking dependencies.
  • Typing: Using TypeScript or type annotations in Python is a must. This allows the agent (and its built-in linters) to instantly verify code correctness.
  • README.md/AGENTS.md in every folder: A brief description of the directory’s purpose helps the agent navigate large monorepos faster.
  • In-code Documentation: Comments, docstrings, etc. Description of module/class/function responsibilities.

4. Quality Gates

Agents tend to “forget” and sometimes intentionally violate rules. Therefore, you need quality gates to check the code against the rules:

  • Linters and Formatters: Maximum automatic checks that can be performed quickly. Including searching for dead code (a typical agent problem), stubs, and attempts to disable linters.
  • Fast Tests: The agent must be able to run tests in the background and get feedback in seconds.

5. Interaction Protocols (MCP)

Integration of Model Context Protocol (MCP) allows agents to go beyond the text editor:

  • Connect tools for reading databases, performing HTTP requests, or interacting with the GitHub API.
  • This turns the agent from a “code writer” into a full “engineer-executor” capable of checking the system state before making edits.

Where It Still Hurts

  • Legacy without tests and types: Agents drown there. AI is more likely to add more problems than help.
  • Niche domain areas: If you write software for specific hardware or in a rare language with little training data, the benefit will be less.
  • Context problem: On long distances and in huge repositories, agents can still lose focus and “forget” initial conditions.
  • Architectural problems: Agents are poor at building architectures and cannot independently make decisions on how best to do something.

Conclusion

Agents require project preparation and new work habits. But it’s worth it.