AI coding agents: what they are, how they work, and how to build one

AI coding agents are no longer limited to autocomplete. Today’s tools can plan, write, test, and debug code with a surprising degree of autonomy—often performing tasks at the level of a junior developer, but faster and at scale.

This isn’t a glimpse of some distant future. It’s already happening. And if you build software for a living, it’s essential to understand how these agents work, what they’re capable of today, and where they’re headed next.

AI agents vs. AI assistants: understanding their roles in software development

AI agents in software development range from simple code helpers to full-fledged autonomous systems. Understanding the difference is key. First, there are AI assistants—tools like GitHub Copilot, Cursor, or Bolt. These rely heavily on large language models trained on vast codebases. They process natural language prompts or partial code snippets and generate code completions or suggestions relevant to the prompts. 

The interaction is synchronous and demand-driven: the developer triggers the action, and the AI model responds. Integration happens mainly through IDE plugins or APIs that deliver context-aware code predictions. However, these don’t execute code, verify results, or handle workflows independently; they only help automate repetitive tasks.

Then come autonomous AI agents like Devin AI. These agents operate with a higher degree of independence and solve more complex tasks. They ingest task descriptions, break them into subtasks, generate code, execute test suites, analyze failures, and iterate to fix errors—all in a loop. 

Autonomy here means they can push commits, open pull requests, or even merge code with minimal human oversight. Their architecture combines natural language understanding, program synthesis, execution monitoring, and decision-making components. Memory modules allow them to maintain context over long sessions.

Real-world use cases illustrate these differences clearly:

• AI assistants speed up routine coding—autocomplete, boilerplate generation, or small utility functions.

• Autonomous agents can handle more complex workflows: triaging and fixing low-risk bugs, generating comprehensive test coverage, or scaffolding new features based on product specifications.

From a technical standpoint, the core difference lies in agency. Assistants respond; autonomous agents initiate, plan, and execute.

Leading AI coding tools 

GitHub Copilot

GitHub Copilot is one of the most widely used AI coding assistants today. Developed by GitHub in collaboration with OpenAI, it’s designed to help developers write code faster by suggesting entire lines or blocks of code directly inside the editor.

Copilot analyzes what the developer is typing in real time, predicting what will come. It can autocomplete functions, generate boilerplate code, suggest solutions to everyday tasks, and even help write tests. It supports dozens of programming languages but performs best in JavaScript, TypeScript, and Go.

Cursor

Cursor is an AI-powered code editor built on top of Visual Studio Code but redesigned specifically for working with AI agents. It is a standalone editor where the AI isn’t just an assistant—it’s part of the core workflow.

The key feature of Cursor is its deep integration with code context. It reads the current file and can pull references from the entire codebase to provide more accurate and relevant suggestions. This makes it helpful in navigating large projects, refactoring code, and even understanding unfamiliar codebases.

Cursor supports natural language queries, allowing developers to ask questions like “What does this function do?” or “Refactor this method to be more efficient.” The editor then responds directly in-line, often rewriting or explaining code with references to related project parts.

Bolt

Bolt is an AI-powered web development platform by StackBlitz, integrating Anthropic's Claude AI to assist users in building, running, and deploying applications directly from their browsers using natural language prompts. 

Bolt takes prompts written in everyday language and turns them into working code. |It allows users to run and test code within the browser, facilitating immediate feedback during development

Thanks to StackBlitz’s WebContainers, everything runs in-browser. There's no need to install Node.js, dependencies, or editors locally. Users can preview the app live as it is built.

Devin AI

Devin, developed by Cognition, represents a significant leap forward in autonomous AI coding tools. Unlike assistants suggesting snippets, Devin operates independently, closer to a junior engineer capable of tackling complex tasks from start to finish without constant human input.

Devin can function within a persistent development environment, writing, editing, and debugging code across files, though it remains in the demonstration phase. It interacts with the terminal, runs commands, sets up servers, installs dependencies, and manages version control, allowing seamless integration into the development process.

Mellum by JetBrains

Mellum is JetBrains' large language model (LLM), which is designed to assist software developers. It is now open-sourced and available on Hugging Face. Integrated within JetBrains IDEs, Mellum offers functionalities like advanced code completion, real-time error detection, and context-aware code generation. It uses machine learning to understand the developer’s intentions and provides relevant suggestions, thus streamlining the coding process.

What makes Mellum notable is its seamless integration with JetBrains’ ecosystem—tools like IntelliJ IDEA, PyCharm, and WebStorm. Unlike general-purpose LLMs, Mellum is a "focal model," meaning it is built to excel at a specific task—in this case, code completion. This specialization allows Mellum to provide faster and more accurate code suggestions, reducing boilerplate code and improving overall code quality.

Mellum supports multiple programming languages, including Java, Kotlin, Python, Go, and PHP, and it is making ongoing efforts to expand its language support. Mellum offers code suggestions and refactoring assistance, with some capabilities to suggest performance improvements, though its ability to detect security vulnerabilities is limited. 

Cody

Cody is an AI coding tool developed by Sourcegraph, focused on helping developers understand, navigate, and improve large codebases. Unlike many assistants that operate in isolation, Cody is built with deep context awareness—it doesn’t just look at a single file but understands entire repositories.

Its main strength lies in providing context-aware support, answering questions about code, explaining functions, and helping navigate through unfamiliar parts of complex projects. This makes Cody especially useful for teams managing legacy systems or onboarding new developers.

Cody integrates with selected large language models to provide context-aware support, aiding developers in understanding and navigating large codebases. This flexibility helps improve the accuracy and relevance of its suggestions.

It integrates with Sourcegraph’s platform to offer powerful code search and navigation, helping developers find relevant information quickly. Cody supports tasks like debugging, code review, and documentation generation, making the development process smoother and faster.

Core components of coding AI agents

Under the hood, coding AI agents are built from tightly connected systems that let them understand instructions, write working code, test it, and adapt to different circumstances.

1. Input/output interfaces

At the most basic level, agents receive inputs and produce outputs. But unlike traditional software, their inputs aren’t strictly formatted.

• Inputs can include natural language instructions, structured data, or environmental signals.

• Outputs aren’t just text—they include actual code files, CLI commands, commit actions, or feedback summaries.

These agents often operate in a command-line or IDE-based environment, which allows for more than passive code suggestion—they can take actions that directly affect the codebase or infrastructure.

2. Integration with development environments

To do anything meaningful, an agent must plug into the tools developers already use:

• IDEs (Integrated development environments): Tools like Visual Studio Code or JetBrains IntelliJ, where code is written and tested. Agents hook into these via APIs or extensions, allowing them to read files, edit code, or trigger builds.

• CI/CD Pipelines (Continuous integration/continuous deployment): Systems like GitHub Actions or Jenkins automate building, testing, and deploying code. Agents can interact with these pipelines by automatically reading logs, detecting failed jobs, or fixing configuration issues.

• Source control repositories: Most agents need access to Git. They clone repositories, create branches, commit changes, push code, and open pull requests. This allows them to operate on real codebases, not isolated toy examples.

3. Natural language processing + code generation + execution monitoring

This is the loop that makes the agent feel intelligent.

• Natural language processing (NLP): Using language models, the agent parses human-written instructions or documentation. For example, turning “write a Python function that checks if a string is a palindrome” into a set of steps to follow.

• Code generation: The agent produces syntactically correct code using large language models like GPT-4 or Code LLaMA. This part is probabilistic, based on training data and pattern matching, not hard-coded rules.

• Execution monitoring: After generating code, the agent often executes it in a sandboxed environment. It watches for runtime errors, stack traces, test failures, or unexpected behavior. This feedback is used to decide what to fix or retry. In some cases, execution monitoring includes running unit tests or reading log files to validate that the agent’s output works.

4. LLMs as reasoning engines

Large Language Models don’t just autocomplete. When structured well, they can reason through a problem. In the context of an agent, the LLM might:

• Break a big task into subtasks

• Choose which tools to use

• Decide whether to retry a failed operation or abandon it

• This is possible because modern LLMs can simulate planning, memory, and decision-making by structuring their output across multiple thought steps, sometimes called chain-of-thought prompting.

5. Memory and tool usage

Two essential features make agents feel consistent and context-aware:

• Memory: AI agents don’t have memory like humans, but can simulate it by saving and reusing context. This context helps them avoid repetition, maintain consistency, and reason more effectively across steps. Agents can store state across steps or sessions, for example, remembering what files were edited recently or what errors were encountered during a previous run. Some systems use short-term memory (just for the current task), while more advanced ones store long-term memory (cross-session knowledge).

• Tool Usage: An agent relying only on natural language output is limited. To move beyond that, it needs tool use—functions it can call to get accurate, real-world data or perform verified actions.

Coding standards and best practices for building AI agents

Designing robust AI agents for coding tasks requires more than fine-tuning a language model. It demands disciplined engineering practices, sharp constraints, and a feedback loop that keeps the agent useful. Below are core principles and proven methods for building dependable, high-performance AI coding agents.

Give the agent the proper context

AI agents operate on partial visibility. Feeding them precise, structured input boosts reliability. Context should include:

• Code snippets and documentation relevant to the task

• File paths, function names, and variable states

• Clear goals (e.g., “add error handling”, “refactor for readability”)

AI agents don’t fully understand the entire codebase unless it’s explicitly provided. That’s why it’s critical to supply a focused slice of information. Developers should feed the agent enough context to perform the task. That could mean including a specific function, a related error message, and a clear description of the intended change. Too much data overwhelms the agent and leads to off-target suggestions; too little forces it to make wild guesses. Balancing context makes the agent faster and more accurate.

Use memory for continuity

In this case, memory doesn’t mean long-term storage like a database. It refers to how the agent tracks ongoing tasks within a session. A capable AI agent should remember what it just did, what it was asked to do, and the key files or variables it has interacted with. This type of short-term memory allows it to maintain consistency in naming, logic, and formatting.

There are three pillars of memory in AI Agents. The first is state, which is the agent’s awareness of what’s happening now—tracking current tasks and context to keep actions consistent. The second is persistence, which means storing useful information across sessions, like project details or user preferences, to improve long-term accuracy. 

And the third is selection. The agent can choose what information to remember and what to discard, avoiding overload and focusing on relevance. Together, these enable continuity. This means AI agents can follow complex tasks over time without losing track or repeating work.

Define clear roles and responsibilities

Establishing distinct roles for both the AI agent and the human developer is crucial. The AI agent should handle repetitive tasks such as code generation, formatting, and documentation, while the developer focuses on higher-level problem-solving, design decisions, and code reviews. This division ensures efficient collaboration and maintains the quality of the codebase.

Communicate clearly with AI

How developers communicate with AI affects how helpful the AI’s code suggestions are.

• Clear Prompts: Giving detailed instructions helps the AI understand exactly what to do. For example, instead of “write a function,” specify what the function should do, what inputs it takes, and what output it should give;

• Iterative Feedback: If the AI’s suggestion isn’t quite right, giving specific feedback like “Make this faster” or “Use this tool instead” helps the AI improve. This back-and-forth helps both sides get better results.

Review and validate AI code

Even though AI speeds up coding, humans still need to check its work carefully.

• Check for context: AI might give code that works technically, but doesn’t fit the overall system. Developers need to spot and fix these issues;

• Learn from AI: Reviewing AI code is also a chance to pick up new coding tricks or tools;

• Test thoroughly: AI-generated code should go through automated tests and manual checks, especially for critical parts;

• Team reviews: When working in teams, combining AI tools with human reviews helps keep the codebase solid. AI can find issues, but humans make the final call.

Challenges in coding AI agents

One of the biggest challenges when building AI agents for coding is their heavy reliance on training data. These agents learn patterns, syntax, and best practices from vast amounts of existing code. But this dependency creates a few problems.

First, if the training data contains errors, outdated methods, or bad practices, the AI can reproduce those mistakes. It might suggest code that works but isn’t optimal, secure, or up to current standards.

Second, AI agents often struggle with understanding the full context of a project. Since they only see patterns from the data they’ve been trained on, they might miss unique project requirements or subtle architectural decisions, leading to suggestions that don’t fit well.

Lastly, there’s the problem of data bias. The AI's outputs can be narrow or less flexible if the training data is heavily skewed toward certain programming styles, languages, or frameworks.

In short, the quality and diversity of training data directly affect how valuable and reliable coding AI agents can be. Developers must stay vigilant, review AI outputs carefully, and keep improving these models with better data and feedback loops.

Should developers worry about their jobs?

Let’s be honest—this question’s on everyone’s mind. And the short answer is “no”, but you’d be smart to pay attention. AI is changing how we code. That’s real. But it’s not replacing developers anytime soon. It’s not building products on its own. It’s not running teams, handling edge cases, or making judgment calls when things get tough.

It is taking over the boring stuff—boilerplate, small fixes, repetitive tasks. That means developers who know how to use these tools won’t be replaced—they’ll just get more done faster. They'll have more time to focus on complex problems, better architecture, or just shipping something meaningful instead of fiddling with syntax.

The role’s shifting. If you ignore that shift, you’ll fall behind. But if you lean in and learn how to work with AI instead of against it, you’ll be more valuable than ever. 

Future directions for AI coding agents

The field of AI-powered coding agents is evolving fast and will only get more sophisticated. We're seeing promising developments that will push these tools far beyond autocomplete and basic code generation.

Collaborative multi-agent frameworks

Instead of one monolithic AI assistant, future systems may coordinate multiple specialized agents that work together. One might focus on architecture, another on documentation, and a third on test writing. These agents can pass context between each other, allowing for parallelized, modular problem solving—more like a team of engineers than a single assistant.

Domain-specific coding agents

More refined, domain-aware agents are emerging as general-purpose tools that plateau in specific tasks. These agents are trained on narrower corpora, like embedded systems, web development, or scientific computing—the result: fewer hallucinations and more context-aware outputs.

Voice and real-time interfaces

Interfaces will become more natural and responsive. Integration with voice-based interfaces will allow speaking code aloud during a sprint, getting instant refactoring suggestions while typing, or even debugging collaboratively through a conversation with an agent. The move toward voice, multimodal input, and live suggestions aims to dissolve the friction between human intent and code execution.

Self-learning and feedback loops

Agents are starting to retain history, observe user corrections, and iterate based on feedback, without needing manual fine-tuning each time. This gives rise to semi-autonomous systems that improve over time by watching how developers edit, accept, or ignore their output. It's a long way from perfect memory, but it’s the first step toward agents that learn the team’s style, not just the syntax.

These directions suggest a future where AI agents aren’t just tools—they’re dynamic, evolving collaborators woven into the fabric of software development.