Hand migrations and bug-fix tickets to a coding agent
Head of AI Research. Task: Head of AI Research TL;DR — Devin AI Review Devin AI is not another coding assistant — it is an autonomous software engineer that takes tickets from Jira or Slack and delivers pull requests. We gave it real migration tasks, data engineering work, and bug fixes. It handled the repetitive stuff remarkably well, but the ACU-based pricing is confusing and costs add up fast on complex tasks. This is a fundamentally different tool from Cursor or Copilot. If your team is drowning in migration backlogs or repetitive engineering work, Devin is worth serious evaluation. Table of Contents What is Devin AI? Key Features How Devin Works Pricing Plans Pros and Cons Devin vs Cursor vs Copilot Nubank Case Study Final Verdict FAQ What is Devin AI? Devin AI is an autonomous AI software engineer built by Cognition AI. Unlike Cursor, Copilot, or Windsurf — which sit inside your editor and help you write code in real time — Devin operates independently. You assign it a ticket from Linear, Jira, or Slack, and it goes off on its own to understand the codebase, write the code, run tests, create a pull request, and iterate on review feedback. Think of it less as a coding assistant and more as a junior engineer who never sleeps. You describe what needs to happen in natural language, and Devin figures out the implementation. It has its own IDE, shell, and browser — a full development environment that runs in the cloud while you work on something else. We spent three weeks testing Devin on real tasks across two production codebases: a Next.js monorepo and a Python data pipeline. We gave it migration tickets, data engineering tasks, bug fixes, and refactoring work. The results were genuinely interesting — and honestly mixed in ways that matter. The paradigm shift here is real. With Cursor, you are the developer and AI is your copilot. With Devin, you are the project manager and AI is the developer. That distinction changes everything about when and how you should use it — and who should be evaluating it in your organization. Key Features Here are the six capabilities that define what Devin can actually do — and what sets it apart from the IDE-based assistants: Devin reads tickets from Linear, Jira, or Slack, understands the requirements, navigates the codebase, writes the implementation, runs tests, and opens a PR. The entire workflow is autonomous — you review the output, not the process. Devin has its own IDE, shell, and browser running in the cloud. It can install dependencies, run build scripts, execute tests, browse documentation, and debug issues — just like a human developer at their workstation. Devin indexes your entire codebase and learns patterns, conventions, and tribal knowledge over time. You can create playbooks and knowledge docs that teach it your team's specific practices. This is where the fine-tuning story gets interesting. This is Devin's sweet spot. Code migrations, framework upgrades, API version bumps, dependency updates across hundreds of files — the repetitive, well-defined work that human engineers dread. Nubank used it on a 6M+ LOC migration. GitHub, GitLab, Linear, Jira, Slack, Teams, AWS, Azure, GCP, Snowflake, MongoDB, PostgreSQL, Stripe, Datadog, Sentry — Devin plugs into the tools your team already uses. Trigger tasks from Slack, get PRs on GitHub. Devin does not just write code — it can set up pipelines, configure deployments, optimize Docker builds, and fix failing CI runs. It understands infrastructure context through its AWS, Azure, and GCP integrations. How Devin Works: Step-by-Step The workflow is fundamentally different from IDE-based assistants. Here is what the actual process looks like when you hand Devin a task: Tag @Devin in Slack, assign a Linear/Jira ticket, or describe the task directly in Devin's web interface. Be specific — "Migrate all API routes from Express to Hono" works better than "update the backend." The more context you provide upfront, the better the output. Devin reads the ticket, explores the relevant parts of your codebase, checks documentation (using its built-in browser), and creates an implementation plan. You can see this plan in the Devin dashboard and intervene if the approach is wrong before any code is written. Devin writes the code in its own IDE, runs the test suite, checks for linting issues, and fixes problems it finds. If tests fail, it reads the error output, diagnoses the issue, and tries a different approach. This loop continues until the implementation passes.; tools: Devin. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include a clear ticket backlog and integration with tools like GitHub/Jira. Constraints: ACU pricing is unpredictable for complex tasks; Devin requires specific instructions and fails on vague requirements. Human approval is mandatory for merging PRs. Source reliability is secondary (blog review)..
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Published on Agent Work Atlas:
Overview
Head of AI Research
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Head of AI Research TL;DR — Devin AI Review Devin AI is not another coding assistant — it is an autonomous software engineer that takes tickets from Jira or Slack and delivers pull requests. We gave it real migration tasks, data engineering work, and bug fixes. It handled the repetitive stuff remarkably well, but the ACU-based pricing is confusing and costs add up fast on complex tasks. This is a fundamentally different tool from Cursor or Copilot. If your team is drowning in migration backlogs or repetitive engineering work, Devin is worth serious evaluation. Table of Contents What is Devin AI? Key Features How Devin Works Pricing Plans Pros and Cons Devin vs Cursor vs Copilot Nubank Case Study Final Verdict FAQ What is Devin AI? Devin AI is an autonomous AI software engineer built by Cognition AI. Unlike Cursor, Copilot, or Windsurf — which sit inside your editor and help you write code in real time — Devin operates independently. You assign it a ticket from Linear, Jira, or Slack, and it goes off on its own to understand the codebase, write the code, run tests, create a pull request, and iterate on review feedback. Think of it less as a coding assistant and more as a junior engineer who never sleeps. You describe what needs to happen in natural language, and Devin figures out the implementation. It has its own IDE, shell, and browser — a full development environment that runs in the cloud while you work on something else. We spent three weeks testing Devin on real tasks across two production codebases: a Next.js monorepo and a Python data pipeline. We gave it migration tickets, data engineering tasks, bug fixes, and refactoring work. The results were genuinely interesting — and honestly mixed in ways that matter. The paradigm shift here is real. With Cursor, you are the developer and AI is your copilot. With Devin, you are the project manager and AI is the developer. That distinction changes everything about when and how you should use it — and who should be evaluating it in your organization. Key Features Here are the six capabilities that define what Devin can actually do — and what sets it apart from the IDE-based assistants: Devin reads tickets from Linear, Jira, or Slack, understands the requirements, navigates the codebase, writes the implementation, runs tests, and opens a PR. The entire workflow is autonomous — you review the output, not the process. Devin has its own IDE, shell, and browser running in the cloud. It can install dependencies, run build scripts, execute tests, browse documentation, and debug issues — just like a human developer at their workstation. Devin indexes your entire codebase and learns patterns, conventions, and tribal knowledge over time. You can create playbooks and knowledge docs that teach it your team's specific practices. This is where the fine-tuning story gets interesting. This is Devin's sweet spot. Code migrations, framework upgrades, API version bumps, dependency updates across hundreds of files — the repetitive, well-defined work that human engineers dread. Nubank used it on a 6M+ LOC migration. GitHub, GitLab, Linear, Jira, Slack, Teams, AWS, Azure, GCP, Snowflake, MongoDB, PostgreSQL, Stripe, Datadog, Sentry — Devin plugs into the tools your team already uses. Trigger tasks from Slack, get PRs on GitHub. Devin does not just write code — it can set up pipelines, configure deployments, optimize Docker builds, and fix failing CI runs. It understands infrastructure context through its AWS, Azure, and GCP integrations. How Devin Works: Step-by-Step The workflow is fundamentally different from IDE-based assistants. Here is what the actual process looks like when you hand Devin a task: Tag @Devin in Slack, assign a Linear/Jira ticket, or describe the task directly in Devin's web interface. Be specific — "Migrate all API routes from Express to Hono" works better than "update the backend." The more context you provide upfront, the better the output. Devin reads the ticket, explores the relevant parts of your codebase, checks documentation (using its built-in browser), and creates an implementation plan. You can see this plan in the Devin dashboard and intervene if the approach is wrong before any code is written. Devin writes the code in its own IDE, runs the test suite, checks for linting issues, and fixes problems it finds. If tests fail, it reads the error output, diagnoses the issue, and tries a different approach. This loop continues until the implementation passes.
Reported result
The source reports mixed results: Devin handled repetitive tasks well but struggled with ambiguity. ACU-based pricing costs add up quickly on complex tasks, making budgeting difficult. No specific success metrics for the author's own tests are provided, only qualitative observations of 'mixed' outcomes.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include a clear ticket backlog and integration with tools like GitHub/Jira. Constraints: ACU pricing is unpredictable for complex tasks; Devin requires specific instructions and fails on vague requirements. Human approval is mandatory for merging PRs. Source reliability is secondary (blog review).
Try it
Act as an engineering lead evaluating Devin for a migration task. 1. Identify a specific, well-defined ticket from [replaceable: Jira/Slack link or description] such as 'Migrate API routes from Express to Hono'. 2. Assign this ticket to Devin via [replaceable: Slack tag or Devin dashboard]. 3. Monitor Devin's implementation plan in the dashboard; intervene if the approach is wrong before code is written. 4. Wait for Devin to generate a PR. 5. Review the PR diff and test results. 6. Do not merge until human verification passes. 7. Record the ACU cost and time taken. Success metric: PR passes CI tests and requires minimal manual edits. If inputs are missing, ask for the specific codebase context and ticket details.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.