We're trying to build a "brainstorm execute → verify" loop for Al coding agents. Does this workflow match your needs with ai agents?

The problem we’re trying to solve in Ripple:

\> AI agents can edit code fast, but they often start changing files before the human clearly understands the plan, risk, and boundary.

So we’re building a simple loop:

1. Brainstorm

2. Execute

3. Verify

4. Human handoff

In brainstorm mode, the agent should not blindly edit code yet.

It should first explain:

\- possible solution paths

\- which files may be involved

\- what could be risky

\- what should not be touched

\- safest first implementation path

Example:

Task:

"Improve auth retry reliability"

Brainstorm mode should return:

\- possible approaches

\- likely files involved

\- risky areas

\- suggested safest path

\- where human approval is needed

Then the human or agent chooses a stricter execution boundary:

File mode:

Only work inside src/auth.ts

Function mode:

Only work inside refreshToken()

Task mode:

Work only inside the selected task scope

After the agent edits code, Ripple checks:

\- what files changed

\- whether the agent stayed inside the original task

\- whether it crossed the allowed boundary

\- whether it touched risky areas

\- whether a human must review before continuing

The loop looks like this:

Human gives broad task

↓

Ripple brainstorms possible paths

↓

Human/agent chooses safe execution boundary

↓

AI agent edits code

↓

Ripple checks changed/staged files

↓

Ripple gives human a clear handoff:

continue / stop / needs review

We are not building another coding agent.

We are trying to build a trust/context layer around coding agents.

Questions for people using Claude Code, Codex, Cursor agents, Copilot agents, or local agents:

1. Does this brainstorm → execute → verify loop feel useful?

2. Would you want an agent to brainstorm before editing risky code?

3. Is “intent drift” and “boundary drift” a clear framing?

4. Do you already solve this with PR review/checklists?

5. What would make this actually useful for real teams?

Brutal feedback welcome. We’re trying to verify if this building workflow is practical for building your project in your way with ai agents or its looking good in theory?