A Readiness Checklist Predicts AI Agent Success Before You Start
Can you build from the CLI?
That single question predicts more about your AI agent results than which model you pick.
The assessment nobody runs
You onboard a new AI coding tool. You point it at your repo. You ask it to fix a bug or write a feature. It produces code that looks plausible.
Then it breaks.
Not because the model is bad. Because the model has no way to verify its own work. It cannot run your build. It cannot execute your linter. It cannot tell whether it introduced a new warning or fixed an old one.
You are now the verification layer. You paste errors back in. You re-run commands. You become the translation service between the agent and your actual development environment.
The problem is not the agent. The problem is the project was never set up for an agent to succeed.
The checklist
Teams that see immediate wins with AI coding agents share a pattern. Their projects pass a readiness assessment before the agent ever writes a line of code.
"We have a little bit of like a checklist that we assess projects against... can you build from the CLI, for example, or are you an IDE-only type of development shop... do you have linting, and can it be run by the agent from the CLI? If it's a compiled language, do you have a lot of warnings, so the agent won't be able to tell whether it added a warning or not?"
Elliot,
The checklist is boring. That is why it works.
CLI buildability: Can you run the build without opening an IDE? If the answer is no, the agent cannot verify that its changes compile.
Standalone linting: Can linters run from the command line? If linting only works inside VS Code, the agent cannot check its own style violations.
Warning baseline: In compiled languages, do you have a clean warning count? If there are 200 existing warnings, the agent cannot tell whether it added one.
Automated tests: Do you have tests that run without manual setup? If the agent cannot execute tests, it cannot iterate based on failures.
PR review history: Do your PRs carry review comments? This signals that the codebase has documented expectations the agent can learn from.
What the checklist predicts
Teams with yeses across these questions get quick wins. The agent has the tools it needs to generate code, verify it, and iterate.
"If somebody's answering yes to most of those, we know that we're going to have really quick success because the agent's going to be really effective."
Elliot,
Teams with noes still get value from AI, but the value is different. The wins are not "I asked for unit tests and got working unit tests." The wins are more like "I got a draft I could manually fix" or "I got ideas for how to approach this."
"If a lot of those are no, there's still going to be wins with AI, but the wins they're looking for are not, okay, I got unit tests, right?"
Elliot,
The checklist does not judge your codebase. It tells you what kind of results to expect and where to invest if you want different results.
Why this matters for your team
For a Series A - C team with five engineers, every hour spent debugging agent output is an hour not spent shipping features. If your project fails the readiness checklist, you already know where the friction will appear.
The fix is not "get a smarter model." The fix is "make your build runnable from the CLI." One is a vendor decision. The other is infrastructure work that pays dividends whether you use AI or not.
A team that invests a sprint in CLI buildability and standalone linting gets two returns: their AI agent starts closing the loop on its own work, and their CI pipeline becomes more reliable for human developers too.
How Roo Code closes the loop on ready projects
Roo Code is an AI coding agent that closes the loop: it proposes diffs, runs commands and tests, and iterates based on results. When your project passes the readiness checklist, Roo Code can execute your build, run your linter, and verify its own changes without requiring you to paste errors back and forth.
With BYOK (bring your own key), you connect directly to your preferred model provider and pay for tokens at cost. Roo Code does not mark up token prices or require a subscription to access agent capabilities.
The readiness checklist is not about Roo Code specifically. It is about whether any agentic tool can operate autonomously in your environment. Projects that pass the checklist let Roo Code close the loop. Projects that fail the checklist turn any agent into a suggestion engine that requires manual verification.
Old approach vs. new approach
| Dimension | Manual verification workflow | Closed-loop agent workflow |
|---|---|---|
| Build verification | Developer copies errors, pastes into chat, waits for response | Agent runs CLI build, sees errors, iterates automatically |
| Linting | Developer runs linter in IDE, reports violations manually | Agent runs linter from CLI, fixes violations in same session |
| Test execution | Developer runs tests, interprets failures, asks for fixes | Agent runs tests, reads failures, updates code until tests pass |
| Warning detection | Developer counts warnings before and after changes | Agent compares warning output against clean baseline |
| Feedback cycle time | Minutes to hours per iteration | Seconds per iteration |
The first step
Before your next AI coding session, run the checklist.
If you cannot build from the CLI, start there. If your linter only runs in the IDE, fix that next. Each "no" you flip to a "yes" removes a manual verification step from your workflow.
The agent gets more effective. You stop being the translation layer. The loop closes without you in the middle.
Frequently asked questions
Stop being the human glue between PRs
Cloud Agents review code, catch issues, and suggest fixes before you open the diff. You review the results, not the process.