Manus AI vs Devin AI: Choosing the Right Agent for Automated Testing

While Devin AI established the baseline for autonomous software engineering with a 13.86% resolving rate on the SWE-bench evaluation suite, the evolution of agentic architectures has split the field into specialized coding engines and general-action browser operators [Cognition Labs 2024 / SWE-bench 2026]. This 13.86% benchmark is not just a measure of code editing; it is the catalyst for a debate on how automated testing should be executed. Developers must choose between two distinct execution models. The first model is a containerized software development environment that runs shell commands and edits files. The second model is a browser-centric operator that executes actions on live web interfaces. Understanding the difference between these environments is key to designing a reliable test automation strategy.

Devin AI operates inside a virtual machine sandbox. This environment includes a complete Linux operating system, a shell, a code editor, and a web browser. When you ask Devin AI to run tests, it executes them inside this isolated container. It has the ability to install package dependencies, compile binaries, and execute local test runners like Jest or Pytest. The agent can launch a full Linux desktop to test GUI and web applications, allowing it to inspect visual layouts and run end-to-end test suites. Because Devin AI has direct command-line access within its virtual machine, it can run local compilers and debugger tools to troubleshoot failures without human intervention. This setup keeps your local development system isolated and secure, as the agent writes and executes code inside its own remote workspace.

Manus AI employs a dual-browser architecture. For script execution and general tasks, Manus AI allocates a sandboxed browser environment that runs in an isolated virtual machine container. Under the hood, this sandbox uses E2B microVMs powered by Firecracker to execute Python or Node.js test scripts securely. The agent uses this cloud container to run code, write scripts, and perform research. When a task requires logging into a website, Manus AI uses sandboxed browser operators to interact with live web apps for end-to-end testing. It can also use a local browser operator extension that runs within the user's browser session. This extension permits the agent to use existing browser cookies and active login sessions, bypassing bot detection and authentication barriers. This allows Manus AI to test web applications in authenticated states, though it requires developer oversight to monitor actions.

The workflow of executing a test suite highlights the differences between a specialized software engineer and a general-action agent. Devin AI is designed to understand code structure and repository organization. When integrated with a Git repository, Devin AI can clone the codebase, read configuration files, and determine the active testing framework. For instance, if a Node.js project uses Jest, Devin AI executes Jest commands via its internal terminal, captures stdout and stderr, and parses error logs to identify broken code. It then opens the specific file in its editor, fixes the syntax error or updates the broken assertion, and runs the test suite again. This loop continues until all tests pass. If a test fails due to a missing dependency, Devin AI will search the web, identify the correct npm package, run the installation command, and update the package.json file.

Manus AI operates primarily at the browser level. Rather than analyzing code syntax, Manus AI focuses on user actions and interface responses. To run test scripts, Manus AI executes Python or Playwright automation in its isolated E2B microVM. If a test script needs to check a login flow, Manus AI uses its browser operator to click the username input, enter credentials, and click the login button. It can capture screenshots of the browser viewport at each step to verify that the visual elements render correctly. When a test assertion fails, Manus AI relies on visual cues, browser logs, and screenshot diffs to troubleshoot the issue. It does not natively edit repository code in the same autonomous cycle as Devin AI. Instead, it reports the failure details, browser logs, and visual evidence to the developer.

How Manus AI runs test scripts depends on the environment you select. In the cloud container, Manus AI runs test scripts in a clean environment, making it suitable for verifying public web applications. For testing features behind complex firewalls or corporate login systems, the local browser operator uses your active browser session. This dual approach allows you to run end-to-end tests across different network environments, but it does not provide the repository-level code correction loop that Devin AI executes. Devin AI remains focused on the codebase, checking out branches, running local tests, and committing fixes directly to Git.

When evaluating these tools for a testing pipeline, the choice depends on where your testing occurs. If your primary need is unit testing, integration testing, and code-level bug fixing, Devin AI provides a complete software development cycle. If your primary need is verifying user interfaces, checking authenticated web app paths, and running browser-based visual regression tests, Manus AI's browser operators offer direct visual interaction.

Durable storage is a major concern when running automated tests with AI agents. Because both Devin AI and Manus AI run code in remote, sandboxed containers, their execution environments are ephemeral. Once an agent completes a test run or the session is archived, the local disk of the virtual machine is wiped. This means that test logs, Playwright trace files, JUnit XML reports, and test screenshots are deleted. If you need to audit a test run or debug a test failure that occurred hours ago, you cannot access the container's filesystem.

Developers often use basic storage solutions to preserve these artifacts. One common approach is copying files to a local hard drive using secure copy protocols or terminal commands. Another alternative is writing scripts to upload test reports directly to Amazon S3 buckets or remote database servers. Teams also use consumer file hosting services like Google Drive or Dropbox to store test logs. However, these storage systems are disconnected from the agent's runtime. You must write custom upload scripts for each agent, configure API credentials inside the sandboxes, and manually organize files into directories. These manual transfers introduce delays and make it difficult to maintain a consistent record of test results.

Fastio shared workspaces provide a persistent storage layer that integrates directly with agent workflows. Fastio is an intelligent workspace platform where developers and AI agents collaborate on the same files. When you create an organization-owned workspace in Fastio, the workspace remains persistent across all agent sessions. You can configure your testing agents to write reports, screenshots, and logs directly to Fastio folders. Because Fastio maintains a complete, per-file version history, every upload is tracked, and you can restore prior versions of test reports if an agent overwrites them. This keeps all test outputs durable and accessible to the team.

Fastio features Intelligence Mode, which automatically indexes all files in the workspace for search and retrieval. When an agent uploads a text log or PDF test report, Fastio indexes the content. Developers and agents can then search this data using hybrid search, which combines exact matching with semantic meaning. If a test fails with a specific error code, you can query the workspace to find all previous instances of that error, along with the corresponding log files and developer notes. Agents access these files via Fastio's Model Context Protocol (MCP) server, which exposes Streamable HTTP at /mcp and legacy SSE at /sse for direct reads and writes. This eliminates the need to build custom sync scripts or manage separate database connections. More details are available in the Fastio storage for agents overview.

For example, a testing agent running in a remote container can use the Fastio MCP server to write test screenshots to a specific folder path. Instead of configuring complex AWS credentials or managing local file storage syncing, the agent makes simple MCP tool calls to upload files. The MCP server handles chunked uploads automatically, ensuring that large logs or HLS video streams of test runs are transferred without network timeouts. Because Fastio is designed to be MCP-native, the agent does not need to learn a custom API; it uses standard workspace, storage, and file operations exposed by the server. This persistent connection ensures that every test run leaves a clear audit trail in the shared workspace, allowing human developers to review results using their preferred web interface or desktop application.

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Automated testing generates large volumes of unstructured data. Test runners output JUnit XML files, Playwright HTML trace folders, and JSON execution summaries. Parsing these files manually to track test trends or identify failing modules is slow. While keyword search helps you find specific errors, it does not allow you to query test metrics as a structured database.

To solve this problem, teams use Fastio Metadata Views to turn test reports into a live, queryable database. Metadata Views allow you to describe the fields you want extracted from files using natural language. Fastio's AI then designs a typed schema, matches files in the workspace, and populates a filterable spreadsheet. You do not need to write parser code or configure optical character recognition (OCR) rules. The system works with PDFs, text logs, images, and spreadsheets.

For test reports, a developer can define columns such as:

• Test Date (Date & Time)
• Suite Name (Text)
• Passed Tests (Integer)
• Failed Tests (Integer)
• Pass Rate (Decimal)
• Failure Message (Text)
• Log URL (URL)

Once the schema is defined, Fastio automatically scans new test reports uploaded by Devin AI or Manus AI, extracts the specified metrics, and populates the spreadsheet. This structured data grid runs independently of the search database, enabling developers to sort, filter, and analyze test metrics inline. If you want to find all test suites that failed on a specific date, you can filter the view by date and pass rate.

Because the Fastio MCP server exposes Metadata Views programmatically, testing agents can query these tables directly. An agent can run a search query over the Metadata View to check if a specific test suite has failed in the past, or to compare current pass rates with previous test runs. This structured extraction layer allows agents to analyze test history and make decisions without requiring database integration or manual log parsing.

For example, when a new test report is uploaded, an agent can check the Metadata View to see if the pass rate has dropped below a specific threshold. If the pass rate falls, the agent can query the history of that test suite, identify when the regression started, and locate the specific commit that introduced the failure. This structured analysis is done using standard database queries over the extracted metadata columns, making it faster than searching through thousands of lines of raw text logs.

Integrating automated testing into your team's development pipeline requires secure automation and clear ownership boundaries. Fastio includes a full workflow engine that allows you to build and drive test pipelines. The workflow engine uses a visual DAG builder to define steps, dependencies, and conditions. Workflows can be triggered in five ways: manual execution, scheduled cron jobs, events, webhooks, or AI-driven triggers.

For automated testing, you can configure a workflow that triggers whenever a testing agent uploads a new test report. The workflow can run an AI review step to analyze the failure log, extract the error message, and route an approval task to a human developer. If a critical test fails, the system can hold the publishing process until a human teammate reviews the logs and signs off. Fastio also includes Collaborative Notes, which support real-time co-editing. Both developers and agents can edit the same Note simultaneously, allowing a testing agent to update a test checklist or add debugging notes while a developer is active in the document.

When a testing project is complete, Fastio supports ownership transfer, allowing an agent to hand over the workspace to a human sponsor. The agent creates the organization, sets up the workspace, configures the testing folders, and generates an ownership transfer link. The human sponsor receives this link, registers their payment details on the Fastio pricing page to start the 14-day free trial, and takes full control of the organization. Fastio does not offer a permanent free plan or a free agent tier; doing work requires an organization on a paid subscription. Plans include Starter ($29/mo), Business ($99/mo), and Growth ($299/mo), and every subscription starts with a 14-day free trial that requires a credit card. Once the trial begins, the agent can retain developer access, allowing it to continue running test suites while the human manages administrative settings.