A dev agent is an AI agent that works inside a software team’s actual workflow, not just suggesting code as you type, but planning a task, writing and running the code, and opening a pull request on its own. That’s the line that separates a dev agent from an autocomplete tool like the original GitHub Copilot: a dev agent takes multi-step action with a clear start and end point, the same way a human contributor would pick up a ticket and submit a PR. This guide covers Devin, GitHub Copilot, Cursor, and Shogo, the open-source agent we build, including where each one is the wrong choice.
What a dev agent does that autocomplete doesn’t
Early AI coding tools suggested the next line or function while you typed. You stayed in the driver’s seat the whole time. A dev agent works differently:
- Takes a task description (a ticket, an issue, a plain-English instruction) rather than just the current line of code
- Plans the steps needed to complete it across multiple files
- Writes and runs the code, including tests, inside its own sandboxed environment
- Opens a pull request for a human to review, rather than inserting a suggestion inline
- Reviews code itself, flagging issues in a diff the way a second engineer would
That last point matters because it’s no longer just about writing code faster. GitHub Copilot’s Pro and higher plans now include pull request reviews directly inside GitHub and file diff reviews inside code editors, agent capability applied to the reviewing side of the job, not only the writing side.
What’s actually live right now
Devin, built by Cognition, markets itself as an autonomous software engineer rather than an assistant. It plans, writes, tests, and ships code inside a team’s existing workflows, and runs on a credit system Cognition calls Agent Compute Units (ACUs). Its official pricing: a Free plan, a Pro plan at $20 per month, a Max plan at $200 per month for higher quotas, and a Teams plan at $80 per month for the team base plus $40 per month per developer seat. Enterprise pricing is quote-based.
GitHub Copilot has moved well past autocomplete. Its Pro and Pro+ tiers, confirmed directly on GitHub’s pricing page, add agent mode across major editors (VS Code, Visual Studio, JetBrains, Eclipse, Xcode), the ability to assign a task to Copilot and have it open a pull request, and code review on both pull requests and file diffs. Individual plans run Free, Pro at $10 per month, Pro+ at $39 per month, and Max at $100 per month, each with a different pool of included usage credits. Higher tiers also add the ability to delegate tasks to third-party coding agents, including Claude and OpenAI Codex, from inside GitHub itself.
Cursor, an AI-native code editor, follows a similar pattern: inline completions for quick work, and an agent mode for larger, multi-file tasks that a developer reviews before merging.
Shogo takes a different angle on the same problem: instead of an agent that writes every layer of an app, it cuts the agent out of the boilerplate. You describe the data model once as a Prisma schema, and the platform generates a type-checked Hono server, typed client, and live REST endpoints, so the agent’s work goes into the UI and business logic. Every tool call the agent makes is validated against a schema before it runs, and the build loop is type-checked and lint-gated, so errors surface and get fixed before anything ships. When the agent hands work to a sub-agent, a router picks the cheapest model that can handle that task. Shogo is open source (the SDK is MIT-licensed and the platform is AGPL) and can run on Shogo Cloud, your own VPS, or a paired desktop.
Where Shogo fits, and where it doesn’t
Where Shogo is a strong fit:
- Building and shipping a new internal tool or app end to end. Describe it, and Shogo writes the code, runs it, and puts it online in the same workspace your team chats in.
- Teams that want to own and inspect the stack. The harness is open source, so you can see exactly what the agent does and self-host it.
- Predictable billing. Paid plans are unlimited usage within fair-use 5-hour and weekly windows rather than a compute-credit pool, Pro is $20 per seat per month, with a free plan to start.
Where another tool is the better fit:
- Day-to-day work inside a large existing codebase in your own editor. An IDE-native tool like GitHub Copilot or Cursor fits that workflow more directly than moving work into a separate platform.
- Benchmark-driven decisions. Shogo states on its own technical page that it doesn’t yet publish head-to-head comparisons against Claude Code, Cursor, Codex, Devin, or Aider. If published benchmark numbers decide your shortlist, you can’t make that comparison for Shogo yet.
What to actually evaluate before adopting one
The marketing for every dev agent sounds similar: faster shipping, fewer repetitive tasks, more time for real engineering work. What actually differs between them:
- How much it runs unsupervised. Some tools are built to pause for review at every meaningful step. Others, like Devin, are built to run a task start to finish and hand you a finished pull request. Neither is wrong, but they fit different levels of trust on different kinds of work.
- How usage is billed. Flat monthly seats (GitHub Copilot) are predictable. Credit or compute-unit systems (Devin’s ACUs) scale with how much the agent actually does, which can be cheaper for light use and more expensive for heavy use. A third model, Shogo’s, is a flat seat price with unlimited usage inside rolling fair-use windows and an optional overage cap you set.
- Where it runs. Inside your existing IDE and GitHub workflow, or in a separate cloud environment the agent owns. This affects how much you need to change about how your team already works.
- What happens on a wrong change. A dev agent that opens a PR for review is a different risk profile than one that pushes directly to a branch other work depends on. Know which one you’re adopting before it ships something to production.
Where this connects to agents beyond code
Dev agents are a specific, mature case of a broader pattern: an agent that takes a defined task, plans the steps, executes them, and hands off a reviewable result instead of a single suggestion. The same pattern shows up in AI agents built without writing code, just applied to business workflows instead of pull requests, finance teams, support tickets, and operations processes that look nothing like a codebase but follow the same plan-execute-review shape.
FAQ
What is a dev agent?
An AI agent built to work inside a software team’s actual development process: planning a coding task, writing and testing the code, and opening a pull request for review, rather than just suggesting the next line as a developer types.
Is Devin the same as GitHub Copilot?
No. GitHub Copilot started as an inline autocomplete tool and has added agent capabilities (task assignment, PR creation, code review) on top. Devin, from Cognition, was built from the start as an autonomous agent that plans and executes a coding task end to end. They now overlap in capability but come from different starting points.
How much does a dev agent cost in 2026?
It varies by billing model. GitHub Copilot runs flat per-seat pricing from $10 to $100 per month depending on the individual tier. Devin uses a credit-based model: a $20 per month Pro plan, a $200 per month Max plan, and a Teams plan at $80 per month plus $40 per developer seat, with usage billed in Agent Compute Units on top.
How is Shogo different from Devin or GitHub Copilot?
Shogo is an open-source agent that generates an app’s backend (server, types, typed client) from a Prisma schema, so the agent’s work goes into the UI and business logic. Builds are type-checked and lint-gated before they ship. It’s strongest at building and running new apps and internal tools end to end. GitHub Copilot is the more direct fit for work inside an existing codebase in your own editor, and Shogo doesn’t yet publish head-to-head benchmarks against Devin or Copilot.
Do dev agents replace developers?
No current dev agent operates without a human reviewing its output before it ships to production. The realistic framing is that a dev agent takes on the first draft of a task, planning, writing, and testing, while a developer still reviews, corrects, and approves before anything merges.
Can a dev agent review code, not just write it?
Yes. GitHub Copilot’s Pro and higher tiers include pull request review and file diff review as a distinct capability from code generation, a sign that “dev agent” now covers both sides of the development loop, not just writing new code.
What’s the risk of letting a dev agent run unsupervised?
The main risk is a change shipping without the context a human reviewer would catch, a missed edge case, an unintended side effect in a different part of the codebase, or a security issue. Most dev agents mitigate this by opening a pull request rather than merging directly, but it’s worth confirming that behavior before adopting one for anything beyond low-risk tasks.
