# Devin AI Examples: Real-World Prompts, Tasks, and Code Workflows

Cognition reported that Devin resolved 13.86% of GitHub issues end-to-end on SWE-bench, demonstrating how autonomous engineering agents move beyond autocomplete into full development cycles. Real-world Devin AI examples show that precise context provisioning, architectural constraints, and deterministic verification turn high-level prompts into production-grade pull requests. Here is how engineering teams structure prompts, orchestrate migrations, and persist agent outputs.

Source: https://fast.io/resources/devin-ai-examples/
Last reviewed: 2026-09-04

## Devin AI Examples: What Autonomous Software Engineering Looks Like in Practice

Cognition reported that Devin resolved 13.86% of GitHub issues end-to-end on SWE-bench, establishing an autonomous baseline for navigating multi-file repositories, reproducing bugs, and running verified test suites. Devin AI examples are reproducible engineering walkthroughs showing prompt structures, context provisioning, and resulting pull requests for real-world development tasks. While standard developer assistants operate inside an IDE editor providing line-by-line autocomplete suggestions, Devin functions as an independent software engineer running inside a dedicated cloud sandbox.

When an engineer assigns a ticket to Devin, the platform spins up an isolated compute environment equipped with standard developer tooling: a Linux shell, an embedded code editor, a headless web browser, and network access. Devin plans its technical approach, inspects project directories, runs build scripts, and diagnoses errors without requiring continuous prompt steering. The primary question engineers ask when evaluating the platform is what can devin ai build across realistic development environments.

In practice, Devin handles end-to-end engineering tasks that require multi-file coordination. It implements new service routes from API specifications, refactors legacy libraries, updates outdated dependencies, investigates production errors, and drafts unit test suites. The agent does not simply generate unverified code blocks. Devin executes tests directly in its container, inspects command-line output, and iterates on syntax or logic errors until test suites pass.

Proof of implementation remains central to the Devin workflow. Every session produces an audit trail containing shell logs, diff views, and browser recordings that show the application running in real time. Rather than trusting code based on statistical probability, human developers review completed pull requests alongside concrete execution data.

The operational difference between assistive autocomplete and autonomous execution centers on state management. Assistive tools maintain context only across open editor tabs. Devin maintains state across an entire operating system instance: compiling assets, installing packages, querying databases, running background servers, and navigating local web pages.

## Five Practical Devin AI Prompt Examples and Code Workflows

Constructing high-yield instructions requires treating the agent like a junior engineer who requires precise scope boundaries, explicit reference patterns, and verifiable completion tests. The following five devin ai prompt examples demonstrate how engineering teams structure prompts for feature development, bug investigation, and migrations.

### 1. Implementing an OpenAPI REST Endpoint with Zod Validation

**Objective:** Implement a complete Express route from an existing OpenAPI definition, including request validation and integration tests matching existing repository patterns.

**Devin AI Prompt:**

```text
Implement the POST /api/v2/bookings endpoint specified in docs/openapi.yaml.

Requirements:
1. Review src/routes/users.ts to understand our standard router conventions and error handling middleware.
2. Define request and response schemas in src/schemas/booking.ts using Zod to enforce the data contract.
3. Implement the controller logic in src/controllers/bookingController.ts, including database transaction handling.
4. Mount the route in src/routes/index.ts.
5. Write integration tests in tests/routes/bookings.test.ts testing valid inputs, missing required fields, and conflicting booking times.
6. Run npm test -- tests/routes/bookings.test.ts to verify that all test cases pass.
```

**Expected Artifact:** A git branch containing the router registration, controller implementation, typed Zod schema, and integration tests passing against the test suite.

**Architectural Breakdown:** Devin reviews `src/routes/users.ts` to detect import conventions and custom middleware patterns before generating code. It constructs `src/schemas/booking.ts` with typed Zod objects matching the YAML schema, then writes `src/controllers/bookingController.ts` using the project's ORM. Finally, it executes `npm test` inside the shell, verifying HTTP response codes and error payloads directly against the running application.

### 2. End-to-End Bug Reproduction and Regression Patching

**Objective:** Isolate an intermittent race condition reported by users, reproduce the failure with an automated test, and deliver a verified patch.

**Devin AI Prompt:**

```text
Users report that invoice status updates fail with a concurrency error when two webhook events arrive within 100 milliseconds.

Reproduction steps:
1. Inspect src/services/invoiceService.ts around the updateStatus function.
2. Create a reproduction test in tests/repro/invoiceConcurrency.test.ts that fires parallel update calls against the same invoice record.
3. Confirm the test fails with the observed concurrency exception.
4. Implement optimistic locking using the version column on the invoices table to handle concurrent writes.
5. Verify that the reproduction test passes and run npm test to ensure existing billing suites remain unaffected.
```

**Expected Artifact:** A reproduction test demonstrating the initial failure, a clean code patch implementing version checks, and test logs confirming zero regressions.

**Architectural Breakdown:** Devin first creates a standalone test simulating parallel webhook delivery. It runs the test to observe the exact database lock exception. Once the failure is confirmed in the terminal, Devin refactors `invoiceService.ts` to check an integer version field before committing writes. It reruns the reproduction test to confirm resolution, followed by the complete billing test suite.

### 3. Test Coverage Expansion for Payment Processing

**Objective:** Build out unit test coverage for payment reconciliation services, addressing missing edge cases such as partial refunds and failed network webhooks.

**Devin AI Prompt:**

```text
Expand unit test coverage for the payment reconciliation service in src/billing/reconcile.ts.

Scope and constraints:
1. Inspect tests/billing/fixtures/ for existing mock structures.
2. Add test cases covering: successful charges, partial refunds, declined cards, and duplicate webhook payloads.
3. Use existing test mocks for third-party payment gateways; do not make outbound network calls during testing.
4. Run npm test -- --coverage src/billing/reconcile.ts.
5. Ensure tests cover all export functions and branch pathways in reconcile.ts.
```

**Expected Artifact:** A dedicated test suite covering four edge cases with mocked network responses and passing coverage output.

**Architectural Breakdown:** Devin inspects existing test fixtures to reuse mock token generators and response objects. It introduces parameterized tests across edge cases, avoiding network calls to live payment gateways. Running test coverage inside the shell allows Devin to identify uncovered branches and add assertions until coverage goals are satisfied.

### 4. Large-Scale Codebase Migration from REST to GraphQL

**Objective:** Execute a phased refactor converting 50 REST endpoint handler files into GraphQL schema definitions and resolver functions without breaking downstream consumers.

**Devin AI Prompt:**

```text
Begin phase one of the REST to GraphQL migration for catalog queries.

Execution instructions:
1. Inspect the GraphQL schema in src/graphql/schema.graphql and existing resolvers in src/graphql/resolvers/.
2. Convert the handlers in src/api/catalog/ (five files) into query resolvers under src/graphql/resolvers/catalog/.
3. Maintain the underlying database query functions in src/db/catalog.ts without modification.
4. Write GraphQL integration queries in tests/graphql/catalog.test.ts matching the expected response payloads from the REST endpoints.
5. Run npm test -- tests/graphql/catalog.test.ts and verify all queries return valid JSON data.
```

**Expected Artifact:** Converted resolver implementations, updated GraphQL schema types, and verified test execution logs.

**Architectural Breakdown:** Refactoring 50 files in a single pass overwhelms agent context windows. Dividing the migration into modular batches of five files allows Devin to extract types, map resolvers to existing database queries, and test each batch independently. The resulting pull request includes resolver mappings and automated queries matching legacy REST responses.

### 5. Upgrading Framework Dependencies from React 18 to React 19

**Objective:** Modernize component lifecycles, resolve deprecated hook usage, and upgrade package dependencies across an internal dashboard.

**Devin AI Prompt:**

```text
Upgrade the client dashboard package from React 18 to React 19.

Steps:
1. Review package.json and update react and react-dom to version 19.
2. Search src/components/ for deprecated patterns, including string refs and legacy context APIs.
3. Refactor any forwardRef instances to use React 19 native ref props where applicable.
4. Run npm run build to check for TypeScript compilation errors and JSX transform issues.
5. Resolve build errors and run npm test to verify component rendering.
```

**Expected Artifact:** A package manifest update, refactored component definitions, and verified compiler output showing clean TypeScript builds.

**Architectural Breakdown:** Devin updates dependency versions in `package.json`, runs the package manager install command, and immediately triggers `npm run build`. As the TypeScript compiler identifies breaking changes, Devin navigates to each offending component file, replaces legacy ref forwarding patterns, and verifies that the production bundle compiles without warnings.

## Devin AI Demo Examples: End-to-End Tasks Across the Software Lifecycle

Observing Devin AI demo examples clarifies how the agent connects isolated tools into unified workflows. During real-world demonstrations, Devin does not simply edit text files; it navigates documentation, executes shell commands, inspects browser rendering, and reports status updates directly to engineers.

One standard demo example involves building interactive web applications from scratch. When prompted to construct a simulation like Conway's Game of Life or a team dashboard, Devin creates project directories, initializes frontend frameworks, writes business logic, and starts a local development server. It opens its embedded browser to inspect the running web application, catches runtime JavaScript exceptions in the console, and refactors components until visual rendering succeeds.

Another prominent workflow is automated error remediation from monitoring tools. When wired to incident management systems or logging services, Devin receives error payloads containing stack traces and context headers. The agent searches the repository for offending functions, reproduces the crash with an automated script, patches the defect, and links the monitoring issue directly in the pull request description.

Devin also excels at customer engineering and unfamiliar integration prototyping. When handed a link to an external API documentation site, Devin uses its browser tool to read endpoint specifications and authentication flows. It constructs proof-of-concept client scripts, tests requests against live sandbox credentials, and outputs documented integration libraries.

In project management automation, Devin connects directly to issue trackers like Linear and Jira. When a ticket receives a triage label, Devin automatically pulls the issue description, reads relevant repository files, determines whether the issue is reproducible, and leaves a comment detailing the probable root cause or opens a draft pull request before an engineer begins work.

## Context Provisioning and Playbooks: How to Structure High-Yield Prompts

The primary difference between a prompt that delivers a functional pull request and one that fails in circular loops is context provisioning. Autonomous coding agents require clear environmental guardrails to avoid exploring dead ends.

An effective prompt contains four distinct components:

1. **Defined Objective:** A direct statement of the goal without ambiguous product jargon.
2. **Reference Locations:** Explicit file paths to existing code that exemplifies desired conventions, directory structures, and error patterns.
3. **Deterministic Verification:** Concrete shell commands, such as specific test file targets or linter checks, that Devin must execute before concluding the task.
4. **Hard Constraints:** Clear boundaries specifying files that must not be edited, third-party libraries that should not be added, or architectural patterns to avoid.

Teams preserve operational knowledge by using Devin Playbooks. Playbooks are structured markdown guides stored within the repository that define repeatable engineering recipes. A playbook can document how to stand up local database seeds, how to configure test mocks, or how to format pull request descriptions. When Devin starts a session, it reads these playbooks to align with team standards automatically.

When prompts lack verification criteria, autonomous agents often stop after making syntax edits without running the code. Specifying exact test suites forces the agent into a self-correcting loop: it writes code, observes test failures, parses stack traces, and refactors its implementation until all checks pass.

Engineering teams should also provide explicit rollback conditions. Instructing Devin to inspect git diffs before opening a pull request ensures that accidental file modifications, generated temporary files, or unintended dependency changes are reverted before review.

## Architectural Workspaces: Managing Storage, Artifacts, and Handoffs Around Devin AI

While Devin operates inside an ephemeral container during a development session, real-world engineering requires persistent storage for the artifacts generated across the lifecycle. Git stores source code commits, but agentic development creates intermediate assets that source control is not designed to manage.

During complex tasks, Devin produces test coverage logs, performance benchmark outputs, schema migration plans, and video recordings of browser testing. When the remote sandbox VM shuts down upon task completion, uncommitted local assets are lost. If an engineering team needs to audit a failed migration or review profiling data from yesterday's session, relying solely on container disk storage creates blind spots.

Teams often attempt to solve this by dumping logs into Amazon S3 buckets or syncing folders via Google Drive. However, static object storage lacks context awareness, semantic search, and collaborative handoff mechanics.

Intelligent workspace platforms provide a persistent coordination layer for autonomous engineering teams. A team can connect Devin sessions to shared org-owned workspaces on [Fast.io](/product/workspaces/) to retain build logs, documentation briefs, and generated deliverables. When files arrive in a workspace, Intelligence Mode indexes all content for hybrid semantic and full-text search, allowing developers and agents to retrieve past solutions using plain language queries.

Engineering teams can use [Fast.io for agents](/storage-for-agents/) via the Model Context Protocol (MCP) server, available over Streamable HTTP at `https://mcp.fast.io/mcp` or `https://mcp.fast.io/mcp/key` with Bearer authentication, to read architectural guidelines and write task outputs programmatically. For structured analysis, [Fast.io Metadata Views](/product/document-data-extraction/) turn migration reports and test metrics into sortable, typed databases without manual data extraction. Every file retains per-file version history and an append-only audit log, ensuring team leads can track agent edits and restore prior versions instantly.

When agents finish prototypes for internal teams or external clients, developers can deliver finished assets using branded share portals (Send, Receive, and Exchange) with customizable access expiration. Agent accounts can set up client workspaces and execute ownership transfer to human administrators while maintaining developer access. Every organization starts with a 14-day free trial (credit card required) across Starter, Business, and Growth tiers, giving engineering teams persistent shared workspaces to coordinate autonomous agents and human developers.

## Frequently asked questions

### What are real examples of tasks Devin AI can do?

Devin AI handles diverse engineering workflows, including implementing REST and GraphQL API routes from specifications, resolving GitHub issues, upgrading framework dependencies, writing unit and integration tests, and diagnosing production errors from log traces.

### How do you write an effective prompt for Devin AI?

An effective Devin prompt provides a clear objective, specifies reference files in the repository for style matching, sets strict architectural constraints, and includes deterministic verification commands like test suite runs or build scripts.

### Can Devin AI build full-stack web applications from scratch?

Yes. When given application requirements, Devin can initialize repository structures, configure frontend and backend frameworks, implement database schemas, and use its embedded browser to verify visual rendering and catch console errors.

### How does Devin AI verify that its code works?

Devin uses its integrated developer tools, including a Linux shell and headless browser, to execute test suites, run linters, compile builds, and visually inspect web interfaces, iterating on code until verification commands pass.

### Where does Devin AI store code and build artifacts?

Devin commits finished source code directly to Git branches. For persistent storage of intermediate build logs, migration reports, and architecture briefs across ephemeral sessions, teams use shared workspaces connected via MCP.

## About Fast.io

Fast.io provides shared workspaces where people and AI agents work on the same files, with built-in semantic search and citation-backed chat over what they hold. Agents reach it through a remote MCP server at https://mcp.fast.io/mcp, a REST API at https://api.fast.io/current/, and a command line client published on npm as @vividengine/fastio-cli.