← All use cases AI Agent Pipelines

Give autonomous agents a full sandbox to build and test

Coding agents get a real Linux environment to clone repos, install dependencies, compile, run test suites, and debug failures. No simulated filesystem. No stubbed commands. Real tools, real output.

Illustrative workflow and output. Verify application compatibility and results in your own test environment.

ai agent pipelines
$ claude "clone the repo, fix the failing test, push"
 
> shell_exec("git clone git@github.com:acme/api.git")
Cloning into 'api'...
> shell_exec("cd api && npm install")
added 847 packages in 12s
> shell_exec("npm test")
FAIL src/auth.test.ts
Expected: 200 Received: 401
 
> shell_exec("vim src/auth.ts", interactive=true)
// ... agent edits the file ...
> shell_send_raw(":wq\n")
> shell_exec("npm test")
All 47 tests passed.
> shell_exec("git commit -am \"fix auth token validation\" && git push")

The problem

AI coding agents are powerful but constrained. When they run on your local machine, they can accidentally break your environment — install conflicting packages, modify system files, or leave orphan processes. When they run in sandboxes, they can't install real dependencies, access the network, or test against actual services.

The result: agents that can write code but can't fully test it. They generate patches but can't verify them. They suggest fixes but can't prove they work.

How agend solves it

agend gives each agent run a disposable, fully-capable Linux environment. The agent operates it like a developer at their workstation:

  • Clone and buildgit clone, npm install, pip install, cargo build — real package managers, real dependencies, real compilation
  • Run tests — execute the full test suite, read failures, understand stack traces, and iterate on fixes
  • Edit interactively — use vim or nano to make changes, not just file-write APIs
  • Debug failures — inspect logs, run debuggers, check environment variables, test network connectivity
  • Push results — commit and push directly to GitHub/GitLab when the fix is verified

Each environment is isolated. The agent can apt install anything, run services, open ports, and make a mess — without affecting your machine or other environments.

Pipeline patterns

  • Fix-and-verify loops — agent clones, tests, fixes, re-tests until green, then pushes
  • Multi-repo workflows — clone a service and its dependencies, test integration end-to-end
  • CI preview environments — spin up an agend env per PR, let the agent verify the build
  • Dependency upgrades — bump versions, run tests, fix breakages, commit the upgrade
  • Code review automation — checkout a PR branch, build it, run tests, report findings

Key MCP tools used

  • shell_exec — run commands, build projects, execute tests
  • shell_exec (interactive) — launch vim, debug REPLs
  • shell_file_put — write files directly
  • shell_file_get — read file contents
  • shell_task_output — monitor long builds

Works with

  • Claude Code / Claude Desktop
  • Cursor (GPT / Claude)
  • Gemini
  • Windsurf / Continue
  • Custom agents via MCP SDK

Pre-installed tools

  • git, curl, wget, build-essential
  • Node.js, Python 3, Go
  • Docker (optional)
  • Any tool installable via apt

Give your coding agent a real computer to work on.

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