Introduction

Over the past few years, something that was considered science fiction for decades has been happening in IT. AI has stopped being a toy for research labs and has become a real working tool that is changing the approach to writing code, designing architectures, and the very way we think about development.

We are on the threshold of cognitive automation — an era where routine and template tasks are delegated to machines, freeing us to solve truly complex and creative problems. This is not about replacement, but about superposition: AI in the role of a junior developer, reviewer, QA engineer, and even architect, working 24/7.

If previously a programmer "communicated" with a computer through documentation, StackOverflow, and IT chats in messengers, today they communicate with the computer itself, which can analyze context, continue thoughts, suggest solutions, and even write code.

[I] History of AI: From Idea to LLM

Development Timeline

1940–1950s — Birth of the Artificial Intelligence Idea
During this period, the first mathematical foundations of computing appeared — Turing formulated the idea of a "universal machine." Scientists began discussing the possibility that a machine could "think" — the very concept of artificial intelligence emerged for the first time.

1956–1970s — Algorithmic Era and First AI Programs
Researchers focused on creating systems that could solve problems by manipulating symbols and using logical rules. The first languages for AI (LISP) appeared, along with early experiments with logic, reasoning, and pathfinding.

1980s — Era of Expert Systems
AI moved from research to industry: companies created expert systems (MYCIN, XCON). Huge budgets were spent on creating "digital experts," but the systems proved expensive to maintain and poorly scalable.

1990s — Revival Through Machine Learning
Attention shifted from "rules" to models that could be trained on data. Neural networks got a second life thanks to new algorithms and more powerful computers.

2000–2010s — Deep Learning and the Data Revolution
The emergence of powerful GPUs made training deep neural networks a reality. Google, Facebook, and other companies began massively applying AI: recommendations, search, advertising, translation.

2018–2022 — Era of Large Language Models (LLM)
The Transformer architecture appeared — it changed everything. GPT-2, GPT-3, BERT, T5 — AI began generating text, writing code, translating, summarizing. LLMs became a universal interface to knowledge and information.

2023–2024 — Multimodality and Automated Workflows
Models began working with text, images, video, voice, and files in one interface (GPT-4o, Gemini 1.5, Claude 3). AI-powered IDEs appeared: GitHub Copilot, Cursor, Windsurf. The first attempts to create agents that understand tasks themselves, break them into steps, and perform actions began.

2024–2025 — MCP and AI Agent Architecture
MCP (Model Context Protocol) — a new standard for AI interaction with tools, APIs, and services. Unlike old plugins, MCP allows creating full-fledged ecosystems of agents capable of performing actions in the real world. IDEs received built-in "technical agents" that can write code, modify files, run CLI, and communicate with APIs. AI transformed from a "chat" into an operating system for automation.

[II] AI Chats: Universal Assistants

Chat interfaces have become the standard for rapid prototyping, research, and problem-solving. Each model has its strengths and can process the same prompt differently.

Main Chat Platforms

ChatGPT (OpenAI)

The flagship model from OpenAI. GPT-5 is a multimodal version that works with text, images, audio, and video. It stands out for its broad erudition, good context understanding, and ability to generate structured code. Ideal for general programming tasks, explaining concepts, and brainstorming.

Claude (Anthropic)

A model from Anthropic focused on safety and long context (up to 200K tokens). Claude 4.5 Opus is the best choice for working with large codebases and documentation. Distinguished by precision in following instructions and quality of reasoning.

Gemini (Google)

Google's multimodal model with native integration into the Google ecosystem (Docs, Sheets, Gmail). Gemini 3 Pro works well with data analysis and Google services integration.

DeepSeek

A Chinese open-source model. DeepSeek-V3 and DeepSeek-Coder show impressive results in programming at significantly lower costs. An excellent choice for code-specific tasks.

Aggregators and Specialized Chats

OpenRouter

A unified API aggregator for accessing multiple models (GPT, Claude, Llama, Gemini, etc.) through one interface. Allows comparing models and choosing the optimal one for a specific task. Convenient for developers who need access to different providers.

Perplexity

An AI search engine with source citations. Ideal for research tasks when you need up-to-date information with references. Combines LLM capabilities with real-time internet search.

NotebookLM (Google)

An AI assistant for working with documents. You upload your materials (PDF, Docs, text) — and the model answers only based on them, with citations. Excellent for analyzing technical documentation, specifications, and research papers.

LLM Council

Andrej Karpathy's project for comparing responses from different LLMs to the same prompt. Useful for understanding differences between models and choosing the optimal one for specific tasks.

[III] AI Integration in IDE

JetBrains AI Assistant is built right into the IDE and feels like a natural extension of the workflow: it helps with autocompletion, explains code fragments, suggests refactoring options, and even generates tests using the context of the entire project.

GitHub Copilot is also a popular AI assistant for programmers that offers inline suggestions, understanding the structure of the file and project, and provides Copilot Chat for communication directly inside the IDE. It is also supported in JetBrains IDE through a separate plugin.

Cline is a powerful local AI agent that works as an extension in VS Code and other editors. It can analyze your entire project, suggest improvements, perform complex refactorings, and even run terminal commands at your request. The main idea is agent-oriented work: you don't just get hints, but communicate with a "smart assistant" that can sequentially perform tasks and control the result.

Cascade is an AI assistant from Codeium that works as a multi-step task "orchestrator." It allows creating action chains (pipelines) where AI performs tasks step by step: analyzes code, writes tests, fixes errors, updates documentation, creates pull requests — all automatically. The main feature of Cascade is a visual task graph where each node is an agent or action. This allows developers to build complex AI workflows without manual routine, and then reuse or combine them as modules.

Codeium is a free AI tool for code autocompletion and project search (semantic search). It stands out for its high speed and no limits. Works in almost all popular IDEs, including JetBrains. Includes Chat mode, code generation, and automatic refactoring.

Tabnine is one of the first AI completers, focused on the enterprise sector. It focuses on privacy: you can deploy your own local model within the company. Provides autocompletion, function generation, and code analysis.

Cody is an AI assistant from Sourcegraph, focused on working with huge monorepos. Its strength is advanced global code search + semantic understanding of project structure. Cody can answer questions about code, find dependencies, suggest improvements, and generate changes at the repository level.

CodeGeeX is a powerful open-source model for autocompletion and code generation. There are plugins for VS Code and JetBrains. Suitable for those who want a completely local solution without the cloud.

AI in VCS Integration

A game-changing feature in modern IDEs (especially JetBrains AI Assistant) is the deep integration of AI into Version Control Systems. It turns the routine of "committing" into a smart process:

• Generate Commit Messages: AI analyzes the diff and writes a clear, structured message describing the changes. You can even customize the prompt (e.g., "start with an emoji" or "follow Conventional Commits").
• AI Self-Review: Before pushing code, you can run an AI check. It acts as a pre-commit hook, analyzing your changes for errors, smells, or security issues right in the "Commit" window.
• Explain Commits: If you're looking at history and don't understand what a specific commit did, AI can summarize it in plain language.
• Resolve Conflicts: The most painful part of Git — merge conflicts — becomes easier. AI analyzes both versions of the code and suggests a smart merge that preserves the logic of both branches.

[IV] AI Agents: Autonomous Developers

The next step was code agents — AI that doesn't just write code, but independently plans and performs actions to achieve a goal. Unlike a chat, an agent can:

• Analyze the codebase
• Create and edit files
• Run terminal commands
• Fix errors based on logs
• Iteratively improve the solution

Windsurf, Cursor, Cline — these are no longer assistants, but semi-autonomous developers who work at the mid-level and close 70–80% of routine tasks.

CLI Agents

When AI came to the terminal, it became clear — a new stage had begun. The terminal is a natural environment for automation. AI agents in CLI can execute shell commands, work with files, and integrate into existing scripts. Claude Code, Gemini CLI, Cursor CLI, Codex CLI.

Model Context Protocol (MCP)

MCP is an open standard from Anthropic for connecting AI models to external tools and data. It's "USB for AI" — a unified interface for integration.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   AI Model  │────▶│  MCP Host   │────▶│ MCP Server  │
│  (Claude)   │◀────│  (IDE)      │◀────│ (Tools)     │
└─────────────┘     └─────────────┘     └─────────────┘

If regular AI agents can read code and execute commands, MCP goes a step further. It's not just an assistant in the IDE or CLI — it's an agent that can plan multiple steps simultaneously and work with different project contexts.

Imagine you need to:

• Create a new feature in the project
• Write database migrations
• Update documentation and tests

A regular agent can perform these steps one by one, reacting to each command. MCP understands the entire flow, builds a plan, and executes tasks in the right sequence, minimizing the risk of errors.

[V] Agents in CI/CD

AI tools for code review have become a natural part of the modern development process. They help find errors, vulnerabilities, style inconsistencies, architectural principle violations, and even provide refactoring recommendations. Unlike static analyzers, AI can consider the context of the entire project, understand the developer's intentions, and suggest more "human" improvements.

Main tasks of AI Code Review:

• Automatic analysis of Pull/Merge Requests
• Detection of errors, potential bugs, and edge cases
• Security analysis
• Code smell detection
• Evaluation of architectural violations
• Refactoring and improvement suggestions
• Style and convention consistency in the project
• Speeding up review and offloading Senior/Lead developers

CodeRabbit AI

One of the most popular startups in AI code review. Works with GitHub and GitLab. Particularly strong in explanations and improving readability. It's almost a "virtual team lead" who reads PRs like a human. Great for teams where there are few reviewers but many PRs.

Capabilities:

• Conducts full AI code review at senior engineer level
• Detailed comments on lines, functions, files
• Builds Sequence Diagrams
• Analyzes architectural decisions
• Provides refactoring and code cleanliness recommendations
• Answers questions directly in PR

GitHub Copilot (Copilot Reviews / Copilot for PRs)

Copilot has long gone beyond autocompletion and become a full-fledged review assistant. The best option if the project is hosted on GitHub. Deep integration, fast operation, supports large context.

Capabilities:

• Analyzes the entire Pull Request
• Summarizes changes and explains what the PR does
• Finds bugs, logical errors, SOLID/clean architecture violations
• Writes review comments automatically
• Can suggest fixes and even generate patches

Snyk Code (AI Security Review)

Snyk is a leader in security, and their AI code analysis focuses specifically on vulnerabilities. If security review quality is important (and for senior devs it's critical), Snyk Code provides deeper analysis than Copilot or static analyzers.

Capabilities:

• Finds security holes: SQL injections, XSS, unsafe deserialization, insecure config
• Suggests secure fixes
• Analyzes dependencies and libraries
• Checks infrastructure files (Docker, K8s)
• Supports many languages, including PHP

[VI] Agents !== Silver Bullet

When the first AI agents for development appeared, many perceived them as a revolution. "Now you can just describe a task, and the agent will do everything itself!" — such headlines flashed in tech blogs and Twitter. Reality turned out to be more complex: AI agents are a powerful tool, but not a magic wand. They don't replace engineering thinking, don't eliminate the need to understand code, and don't make code review obsolete. Rather the opposite — they make these skills even more important.

Code Review Becomes More Critical

Paradox: the more code AI generates, the more important human review becomes.

An agent can write code that works, passes tests, but at the same time:

• Violates the project's architectural principles
• Duplicates existing functionality (the agent simply didn't know about it)
• Creates non-obvious technical debt
• Uses outdated patterns or libraries
• Solves the problem "head-on", ignoring edge cases

Example 1: "The agent wrote me an authorization service in 5 minutes. Everything worked. During review, I discovered it didn't use our existing AuthService, created duplicate logic, and hardcoded several values. The fix took longer than if I had written it myself."

Example 2: "The agent wrote me an authorization service in 5 minutes. The remaining 7 hours and 55 minutes I spent rewriting it."

This doesn't mean the agent is useless. It means its code requires the same (and sometimes more thorough) review as code from a junior developer.

Prompt Quality === Result Quality

Garbage in — garbage out — this principle works with AI agents too. A vague task description leads to a vague result.

Bad: "Add email validation"

Good: "Add email validation to the registration form. Use our existing EmailValidator from namespace App\Shared\Validator. Validation should trigger on blur and on submit. Display errors below the input field in the format already used for other fields."

The second prompt gives the agent context: where to look for existing code, what behavior is expected, how it should look visually. The result will be fundamentally different.

A good task description for an agent is a skill that needs to be developed. It's not "just write what you want" — it's engineering communication.

Project Context — Your Responsibility

The agent doesn't know your project's history. It wasn't present at meetings where you decided to use a certain pattern. It didn't read the RFC where the architectural decision was justified. It doesn't know that "this hack" is a temporary workaround for a bug in a third-party library. Without context, the agent will make decisions based on general best practices. Sometimes this matches your agreements, sometimes it doesn't.

Solution: create project rules. Files like .cursorrules, .windsurfrules, memory systems in AI tools — these are ways to pass context to the agent that it cannot learn from code.

Architectural principles, code style, patterns used, forbidden approaches — all this should be documented not only for people but also for AI.

These files and mechanisms are ways to control AI assistant behavior at the project level, not individual requests. You're essentially creating a layer of instructions that AI automatically considers in any interaction with code.

[VII] Vibecoding !== AI Agent Development

A term "vibecoding" has appeared in the community — a work style where a developer just "vibes" with AI, generating code without deep understanding of the result. Prompt → code → works? → commit.

It's like programming by copy-paste from StackOverflow, only faster and on a larger scale.

Vibecoding can work for prototypes, hackathons, or pet projects. But for production code that will be maintained for years, it's a path to disaster.

AI Agent Development is a fundamentally different approach. It's not a rejection of AI tools, but conscious work with them.

Vibecoding: When you ask an LLM "make it pretty", paste the code, it works, but you don't fully understand why. This is "Hello World" level or a quick MVP. Risks: security holes, unoptimized queries (in PHP this is the classic N+1), spaghetti code.

Agent Orchestration: An approach where you're the client and tech lead. You provide context, require SOLID compliance, PSR-12, design patterns. You use agents for specific tasks (one writes tests, another refactors, a third writes documentation).

"Vibecoding is when AI leads you by the hand. Orchestration is when you lead AI on a leash."

You're the Team Lead, AI is Your Team

Every developer becomes a tech lead! Imagine you stopped being an individual contributor and became a tech lead. Now you don't write all the code yourself — you have a team. Your role has changed:

• Task Setting. You decompose requirements, formulate clear tasks, define acceptance criteria.
• Architectural Decisions. You decide how components should interact, which patterns to use, where to draw module boundaries.
• Code Review. You check the result, find problems, suggest improvements.
• Responsibility. If the code breaks production — you're responsible, not your team.

Working with AI agents is the same model. Agents are your team. They're fast, don't get tired, can work in parallel. But they don't make final decisions. You're responsible for code quality, architecture, and consequences.

This doesn't mean you should micromanage every line. A good team lead trusts the team but checks the result. Same with agents: give them autonomy within the task, but always review the result.

AI Onboarding or the "New Employee" Principle

There's a useful mental model for working with AI agents. Imagine a new developer joined your team. They're talented — passed a complex technical interview, have experience with your stack, write clean code. But they know absolutely nothing about your project. They don't know why the authorization service is named that way. They don't know about technical debt in the payments module. They don't know that half the code in legacy/ is what "we've been planning to rewrite for two years."

What do you do with such an employee?

1. Onboarding. You don't throw them at complex tasks right away. You explain the architecture, show key modules, tell them about agreements and rules. "This is how we name services. Business logic lives here. Don't touch this folder — there be dragons."
2. Context for Tasks. You don't say "add feature X". You explain: "We need feature X. It should integrate with module Y, use existing service Z, and note that clients sometimes send invalid data — here's a ticket with examples."
3. Code Review. You check their pull requests. Not because you don't trust them, but because they might not know about non-obvious requirements or agreements.
4. Feedback. If something is done wrong, you explain why. "It's better to use pattern X here because..." This helps them understand context and do better in the future.

An AI agent is exactly such an employee. It's smart, fast, but doesn't know your project. The difference is that the agent doesn't learn between sessions (yet). Every time you work with a "new employee" who needs context explained again.

That's why project rules, good documentation, and structured prompts are so important. This is your "onboarding" for the agent.

Understanding Over Speed

The main rule of AI Agent Development: you must understand what the agent did and why. This doesn't mean you should be able to write this code yourself in the same time. It means you should:

• Understand the solution logic
• See why this particular approach was chosen
• Be able to explain the code to a colleague
• Know how to debug it if it breaks

If the agent generated 500 lines of code and you don't understand how they work — don't commit. Figure it out. Ask the agent to explain. Simplify the solution. Break it into parts. Because code you don't understand is a ticking time bomb.

Code generation speed means nothing if you lose control over the codebase. AI Agent Development is about smart use of tools, not maximum generation speed.

[VIII] Practical Tips: How to Work Effectively with AI

• Choose the right tool. If you have a quick question — use chat. If you need to write code — use IDE + AI. If you need to solve a complex task — use an Agent. Need automation — MCP + CLI.
• Review the code. AI can hallucinate — always review generated code.
• Don't try to get perfect results on the first try. Clarify, correct, guide, try different models.
• Watch the context. The more relevant context you provide, the better the result.
• Create project rules.
• Traps and Hallucinations: Don't keep a conversation going too long in one context window.
• Always observe the process. The model can get stuck and loop the same request, adding a line then deleting it.

[IX] My Experience After 1 Year Working with AI Agent

One of the main wishes that has now become reality is the ability to create universal "Fixer" tools.

"We have CsFixer for CodeSniffer — it would be great, I once thought, if there was such a tool for phpstan, psalm, deptrac..."

And such tools have appeared. An AI agent can generate or adapt code that automatically fixes errors detected by static analysis tools (e.g., phpstan or psalm), significantly reducing technical debt and accelerating the implementation of strict quality standards.

Accelerating Development and Debugging

Writing tests became faster: The agent takes on the routine of creating template tests, mocks, and edge cases, allowing the developer to focus on logic rather than syntax.

Debugging became faster: Through quick analysis of logs, stack traces, and error context, the agent instantly suggests probable causes and solutions, reducing problem-finding time from hours to minutes.

Working with Logs

Accelerated root cause analysis: Instead of manually reviewing terabytes of data, the agent can quickly identify patterns, highlight anomalies, or group related events, allowing instant localization of the root cause of failure.

Translating logs to natural language: The agent can explain complex or cryptic system messages in plain language, turning technical jargon (e.g., error codes or stack traces) into an understandable problem description and suggest specific actions to fix it.

Working with Legacy and Documentation

Explaining legacy code: The agent can decompose and explain how complex, poorly documented, or outdated code works, providing instant context and lowering the entry barrier for new developers or when working with old projects.

Creating UML diagrams: Automatic creation of structural or behavioral UML diagrams based on source code significantly improves understanding of project architecture, which is critical for refactoring planning and documentation.

Expanding Team Competencies

Filling the frontend developer position: The agent writes CSS, JS, and HTML code excellently. It can quickly implement layouts, create responsive styles, and write small JS scripts, allowing backend developers not to be distracted by frontend routine and thus optimize the staffing structure.

Vulnerability Scanning

The agent can quickly analyze code fragments for common vulnerabilities (e.g., SQL injections, XSS, unsafe serialization) and suggest patches or safer implementation alternatives.

Performance and Optimization

Refactoring for performance: The agent not only fixes errors but also suggests structural changes (refactoring) aimed at improving code execution speed (e.g., loop optimization, database query optimization).

Localization and internationalization (i18n): Automatic detection of strings requiring localization and assistance in creating translation files (e.g., JSON, PO files) to support multiple languages.

Infrastructure and DevOps

Configuration generation: Accelerated writing or editing of complex configuration files for CI/CD (e.g., GitLab CI, GitHub Actions, Jenkins), Dockerfiles, or web server configurations (Nginx, Apache).

Data migration and schema conversion: Assistance in creating scripts for database migration or schema conversion (e.g., from MySQL to PostgreSQL or vice versa), significantly reducing manual labor when transitioning to new systems.

Improving Communication Efficiency

Writing/Editing corporate emails: From composing official responses to editing internal documentation — the agent helps maintain a professional, literate, and unified communication style, saving time on formulating thoughts.

[X] Working with AI — Essential "Hard Skill" of 2026

If in 2024 the ability to use Copilot and ChatGPT was a competitive advantage, by 2026 effective interaction with AI agents will become a basic requirement in the job market, comparable to knowing Git or SQL.

Companies expect employees to spend time on high-level, non-automatable tasks: architectural planning, solving complex business problems, mentoring, and strategic vision. AI agents free up time for this. Therefore, a developer not using AI agents won't be able to compete in task completion speed with a developer who knows how to effectively delegate routine to machines. The productivity gap will be too large.

Automation of routine coding will lead to the disappearance of positions requiring only "writing code according to specs." The market will be left with either expert architects (Team Leads) or AI system operators with deep domain knowledge. The skill of working with agents is your ticket to the first category.

Thus, AI agents don't relieve you of the need for deep engineering understanding. On the contrary, they elevate you to a level where you stop being an executor and become a highly qualified architect and quality manager, whose main weapon is critical thinking and precise task setting. This is the new Team Leadership.

Conclusion

AI tools for development have come a long way from experimental chatbots to full-fledged agents capable of independently solving tasks. Key trends:

This is not a replacement for developers, but their enhancement. AI takes on the routine, freeing up time for architectural decisions, creative problem-solving, and what machines still can't do — understanding business context and making responsible decisions.