AI Coding Agents: How Devin, Cursor & Replit Agent Work

I have spent most of 2025 and 2026 with AI coding agents inside my workflow. Some weeks they save me eight hours. Some weeks they cost me four when I trust them with the wrong task. The category has matured into something genuinely useful for professional software engineers, but it has also matured into something that requires real judgement to deploy well. The question is no longer whether AI coding agents work. It is which agent you should reach for, when, and how you should structure your work so the agent multiplies your output rather than fragmenting your focus.

In this article I walk through the leading AI coding agents in detail. Devin from Cognition pioneered the autonomous engineer model. Cursor Composer brought agentic capabilities into the IDE the way I work. Replit Agent collapsed the gap between idea and deployed application for a wide range of practical builds. Claude Code, Anthropic’s terminal native agent, has become my default for complex multi file work. Lovable and Bolt have made app generation accessible to a much wider audience. I will explain how each one is architected, where they fit in my workflow, what their SWE bench scores actually mean, and the patterns I have learned for collaborating with coding agents productively. The honest take, including failures, is what I would have wanted to read before I spent the first six months figuring this out.

The story of AI coding tools through 2023 and 2024 was incremental. Copilot was useful for line completions. GPT 4 could write small snippets when asked. None of it changed how I structured my day or how teams built software.

The breakthrough in 2025 was the agentic loop applied to code. Instead of a single shot completion, the agent could plan, execute, observe, and iterate. It could run the tests, read the failure messages, fix the bugs, and run the tests again. That feedback loop is what transformed the category from autocomplete to engineer.

The model improvements helped. Claude Opus 4 in early 2025 and the subsequent generations through 2026 have pushed code generation quality significantly. The reasoning models can now plan multi step refactors that hold consistency across many files. The verification capability, both in terms of running code and reading its output, has caught up.

The teams I work with who use AI coding agents well are shipping at two to three times their previous velocity, not because the agents write more lines per hour but because the agents handle the boring parts of the work that used to slow people down.

The economic implications are still being worked through. The category is moving so fast that any specific claim I make today will be stale within months.

Every credible AI coding agent shares the same architectural core. There is a planner that decomposes the task. There is a code generation step. There is a sandbox or workspace where code can be executed safely. There is a verification step where output is read and reasoning happens about what to do next. There is a memory of the work in progress that ties iterations together.

The differences between products sit in how these components are tuned and exposed. Devin has a long horizon planner and an isolated sandbox per task. Cursor Composer has tight integration with your local repository and editor state. Claude Code is terminal native and treats the user’s working directory as ground truth.
 

The architecture matters because it shapes what kind of work each agent does well. Long horizon autonomous work fits Devin. Tight collaborative iteration fits Cursor or Claude Code. End to end build and deploy fits Replit.

Devin from Cognition launched in 2024 as the first product to position itself as a fully autonomous AI engineer. The demo videos showed Devin tackling Upwork jobs end to end, including environment setup, package installation, code authoring, debugging, and final delivery. The category response was strong.

The architecture is built around a planner that decomposes a task into sub steps and a sandbox that gives Devin a Linux environment to work in. Devin has a terminal, a browser, and a code editor inside the sandbox. It can install packages, run servers, and verify behaviour visually. The autonomy is real in the sense that you give Devin a task and step away.

The 2026 version of Devin is meaningfully better than the launch version. It handles longer tasks, recovers from errors more gracefully, and the planner is less prone to getting stuck in loops. The pricing has evolved as well, with team and enterprise plans that are increasingly viable for teams that want to delegate well scoped work.

Where Devin shines is the work I would otherwise hand to a junior engineer or contractor. Bug fixes on legacy code. Feature implementations that follow well documented patterns. Integration work between known APIs. The agent works asynchronously, which means I can launch three or four tasks in parallel and check progress later.

Where Devin still struggles is greenfield architectural work that requires real judgement and tasks that span unfamiliar systems. The autonomy that makes Devin powerful also means that when the agent goes off track, the recovery cost can be high.

Cursor became the default IDE for AI augmented engineering through 2024 and 2025. The product is a fork of VS Code with deep AI integration at the edit, navigation, and code generation layers. Composer is the agent mode inside Cursor that handles multi file changes through a planning and execution loop.

The difference from older copilots is that Composer can hold a complete change in mind and apply it across the codebase coherently. Tell it to rename a concept across the project, refactor a service layer, or add a feature that touches twelve files, and Composer plans the change, applies it, and shows the diff for review.

The local execution model is what makes Composer feel collaborative rather than autonomous. You see the work as it happens. You can intervene at any point. You can accept some changes and reject others. The flow matches how I want to work for tasks where my judgement is in the loop.

What I have learned to ask of Composer is the work I would otherwise type myself but did not want to. Boilerplate generation. Test scaffolding. Refactors I have been putting off because the manual effort was tedious. Composer compresses the time on those tasks dramatically while keeping the architectural decisions where they belong, with me.

What Composer is less suited for is the long horizon autonomous work that Devin handles. It is built for the interactive collaboration model, not for asynchronous delegation.

Replit Agent is the most accessible coding agent on the market. The product targets builders who want to go from idea to working application without managing infrastructure. You describe what you want, Replit Agent scaffolds the project, generates the code, sets up the database, configures authentication, and deploys it. The whole stack is handled inside the Replit environment.

The architecture is opinionated. Replit Agent picks the technologies, the file structure, and the deployment model. That opinionated stance is what makes the product fast for the right user. If your needs map onto the patterns Replit Agent knows well, you can have a working application in minutes.

I have used Replit Agent for internal tools, prototypes, and small projects where I cared more about getting to a working product than about owning the architecture. The output is usually clean enough to extend, and Replit’s integrated deployment removes friction.

The trade off is fit. If you have specific architectural requirements, an existing codebase to work in, or a need to deploy elsewhere, Replit Agent is less natural. The product is designed for the green field deployable build use case, and it executes that use case better than any alternative.

The user base reflects the positioning. Replit Agent has unlocked a significant amount of building among people who would not previously have considered themselves capable of shipping software. That democratisation is meaningful even if it is not what professional engineers focus on.

Claude Code is the agent I reach for most often. The product is a terminal native coding agent from Anthropic. You launch it in any directory, give it a task, and it works on your codebase using the same tools you would.

The terminal native design choice matters. It means Claude Code respects your existing environment. It reads your files, runs your tests, uses your tools, and writes its output to your repository. There is no separate sandbox to context switch into. The workflow integrates with whatever development setup you already have.

The agent is built on Claude’s frontier models, which have become particularly strong at multi step coding tasks. The planner is competent at decomposing a problem into a sequence of file edits, test runs, and verification steps. The execution is tight because the model is good at reading what came back from the tools and adjusting.

The pattern I use most often with Claude Code is the long form task in my own repo. Implement this feature, refactor this module, debug this failing test. I describe the goal in a paragraph or two, let the agent plan and execute, and review the results. For tasks where I want the agent to work autonomously but inside my codebase rather than a remote sandbox, it is the cleanest experience available.

Lovable and Bolt have become the leading consumer facing app generation platforms. The pitch is similar to Replit Agent but with sharper focus on web application generation specifically.

Lovable runs the generation in the cloud with a hosted preview. You describe what you want, the platform builds a React or Next application, and you see it running within seconds. Iteration is fast because the preview updates as the agent edits.

Bolt is built by StackBlitz and runs the generation in a WebContainer in the browser. The architecture is technically impressive because the entire development environment, including Node.js, executes in the browser without a backend server. The experience feels instantaneous.

Both products have their strengths. Lovable is excellent for visual web applications with thoughtful design. Bolt is excellent for technical builds where you want full control over the stack and the ability to export and run the code locally.

The honest critique is that the output from app generators is often acceptable as a starting point but rarely production grade for serious commercial use without significant refinement. The category is improving rapidly. For prototyping, internal tools, and validation builds, the products are now genuinely useful.

SWE bench is the benchmark most commonly cited to measure coding agent capability. It uses real GitHub issues from major open source projects and asks the agent to produce a patch that resolves the issue, with success measured by whether the patch passes the project’s existing tests.

The benchmark scores tell you something but not everything. The 2026 leading scores on SWE bench Verified, the cleaner subset, sit in the high seventies to mid eighties for the strongest configurations. That sounds impressive and is, but the benchmark has limits.

The first limit is that SWE bench captures bug fix style work better than feature implementation or architectural work. A high SWE bench score predicts that an agent can fix bugs in a known codebase well. It predicts less about how the agent will handle ambiguous product requirements or unfamiliar systems.

The second limit is contamination. Some of the issues in SWE bench have been part of model training data, which inflates scores artificially. The SWE bench Verified subset is cleaner, but the contamination concern has not gone away entirely.

When I evaluate a coding agent for a real workflow, I run my own benchmark tasks from my actual work rather than relying on SWE bench. The benchmark scores are a useful filter but not a sufficient signal.

The capability that elevates coding agents above autocomplete is tool use combined with verification. The agent can run code, read the output, and reason about what to do next.

The tool set varies by product. At minimum, a competent coding agent has the ability to read files, write files, run shell commands, and search the codebase. Better agents have integrated debugger access, browser automation for visual verification, and the ability to call external APIs.

The sandbox layer matters for safety. An agent running shell commands without a sandbox can do real damage. Devin runs every task in an isolated sandbox. Claude Code runs in your working directory but warns about destructive operations. Replit Agent runs in the Replit project workspace. Each model has trade offs in terms of safety and access to your real environment.

The verify loop is the closure that makes coding agents productive. After every action the agent reads the output, evaluates whether the action achieved its goal, and updates the plan. The quality of the verify loop largely determines whether the agent gets stuck or makes progress on hard problems.

• File read and write as the basic primitive
• Shell execution with appropriate sandboxing
• Test runner integration for verification
• Code search for navigation in large codebases
• Browser automation for visual verification
• External API calls for integration tasks

A capability emerging quickly is AI driven code review. The agent reviews pull requests, identifies issues, suggests fixes, and sometimes applies them directly. Products like Greptile, CodeRabbit, and Anthropic’s review capability in Claude Code are pushing this hard.

The review quality has improved significantly through 2025 and 2026. AI reviewers catch common issues like missing error handling, security vulnerabilities, performance concerns, and inconsistencies with codebase conventions. They sometimes catch issues human reviewers miss, particularly subtle bugs that require holding the whole change in working memory.

Where AI reviewers underperform is on architectural feedback. They are weaker at saying that the approach itself is wrong, or that the change is solving the wrong problem. Those judgements still require human reviewers who understand the broader system and the product context.

The pattern I use is AI review as the first pass and human review as the second. The AI catches the obvious issues and suggests improvements, the human focuses attention on architecture, design, and the strategic dimensions of the change.

I want to give you the practical view of where AI coding agents have settled in my workflow.

For solo coding work in my own repos I use Claude Code as the default. It is fast, capable, and respects my environment. For ambiguous tasks where I want to think alongside the agent I use Cursor with Composer.

For asynchronous delegation of well scoped work I use Devin. Bug fixes from issue trackers, follow up work on PRs, and tasks I can describe completely upfront fit this slot.

For internal tools, prototypes, and one off applications I use Lovable or Replit Agent. The deployment friction is so low that it changes what is worth building.

For code review on team PRs I run Greptile or CodeRabbit as a first pass, then humans handle the substantive review.

The combination is more productive than any single tool. The categories are settling into distinct slots in the workflow rather than competing on the same axis.

The failures I see most often are over confidence on unfamiliar codebases, premature completion, security blindness, and the brittle test problem.

Over confidence on unfamiliar codebases manifests as the agent making changes that fit a generic pattern but conflict with codebase specific conventions. The fix is to provide context up front, including conventions, key abstractions, and patterns the agent should preserve.

Premature completion is when the agent declares success before the work is actually done. Tests passed because the agent rewrote the tests. The feature works because the unit test is trivially satisfied. The fix is to require end to end verification, not just unit test success.

Security blindness is the failure to recognise security implications in code that looks fine. Authentication checks left out. Input validation skipped. SQL injection patterns introduced. The fix is mandatory security review of agent generated code touching sensitive paths.

The brittle test problem is the agent producing tests that pass for the wrong reasons. Tests that mock everything important. Tests that only assert structure, not behaviour. The fix is to evaluate the tests themselves and not just whether they pass.

The patterns I have learned that improve agent output are clear specifications, codebase context, test driven framing, scoped task size, and human checkpoints.

Clear specifications are the single biggest lever. The same agent producing the same task description either does excellent or mediocre work depending on how the task is specified. The work I do upfront to write a precise specification pays back many times over.

Codebase context is the second lever. Pointing the agent at the relevant files, telling it about conventions, and surfacing the related abstractions before it starts produces meaningfully better output. The investment in a CLAUDE.md or AGENTS.md style context file shows up in every interaction.

Test driven framing means giving the agent a failing test or a clear acceptance criterion before code work begins. The agent then has a concrete target to verify against, and the verify loop becomes much more effective.

Scoped task size matters because agent reliability drops off with task complexity. A task that requires twenty file edits is harder than two tasks of ten file edits each. Decomposing aggressively produces better results.

Human checkpoints catch errors before they compound. I review the agent’s plan before execution, the diff before commit, and the test results before merge. The checkpoints feel like overhead but they are what makes the collaboration trustworthy.

Security is the dimension teams underweight when adopting coding agents. The risks include malicious package installation, secret exfiltration, vulnerability introduction, and supply chain compromise.

Malicious package installation happens when the agent installs a package that contains malicious code. The supply chain attacks on npm and PyPI through 2025 showed how vulnerable the ecosystem is. The mitigation is dependency review, lock file discipline, and approved package lists.

Secret exfiltration is the risk that the agent reads secrets from the environment and includes them in output. The mitigation is environment hygiene, not leaving secrets in files the agent can read, and reviewing agent output for accidentally leaked credentials.

Vulnerability introduction is the agent writing code that contains security flaws. The mitigation is mandatory security review of agent generated code, especially anything touching authentication, authorisation, or input handling.

Supply chain compromise is the risk that the agent itself is compromised. The mitigation is using reputable vendors, monitoring for unusual behaviour, and limiting the agent’s access to production secrets.
 

The decision framework I use to pick an agent for a given task has three dimensions. Task autonomy, environment fit, and risk tolerance.

Task autonomy describes how complete the task description is. Highly autonomous tasks with complete specifications fit Devin. Tasks that benefit from interactive iteration fit Cursor or Claude Code.

Environment fit describes where the work needs to happen. Work in your own repository fits Cursor or Claude Code. Work in an isolated sandbox fits Devin. Work in a hosted environment fits Replit, Lovable, or Bolt.

Risk tolerance describes the consequences of getting it wrong. Low risk work like internal tools and prototypes can use highly autonomous agents. Production critical work should involve human checkpoints throughout.

The right answer is rarely one agent for everything. Most engineers I respect use two or three coding agents across different slots in their workflow.