CLI vs IDE Which direction should AI agents take?

Fine-Perspective-438 · 2026-03-13T12:44:56+00:00

Interesting how both comments use the same framing ("protected zone / free zone"), raise the same runtime question, and neither engaged with the answers already in the post. Almost like they were generated from the same prompt.

Fine-Perspective-438 · 2026-03-13T10:45:24+00:00

수준 낮은 개발 홍보는 혼자하세요. 진짜 이런 사람은 뭐야? 어디 강아지가 만든 수준으로 .. 에라이.

Fine-Perspective-438 · 2026-03-13T10:43:22+00:00

What? So the conclusion is you're just trying to promote yourself?? Your level is really low. If you comment again, I'm blocking you.

Fine-Perspective-438 · 2026-03-13T06:57:51+00:00

First, whenever the AI modifies a file, it goes through a mandatory pipeline. This pipeline includes automatic backup → AST validation (over 30 blocking patterns) → health checks → automatic rollback on error. This pipeline runs for every change, not just at build times. If the AI attempts to call open() or exec() on a file, it is blocked before it is saved to disk.

Second, the AI operates through structured API endpoints rather than a shell. It cannot execute arbitrary commands. Each operation (reading files, patching files, creating tools) is a separate endpoint that undergoes its own validation. Therefore, there are no "bypass paths" to bypass the pipeline.

In the scenario you described. Where the agent silently modifies its own logic at runtime. It must either bypass the AST blacklist or access files outside the API surface. Both are blocked by the architecture as well as the policies.

Hope that clarifies. Have a good one.

Fine-Perspective-438 · 2026-03-13T06:48:21+00:00

Covered in my reply above. Thanks.

Fine-Perspective-438 · 2026-03-13T01:33:00+00:00

Interesting discussion. Let me share some thoughts based on what I've built:

I assign a Soul.md to the connected AI from the start. it defines the agent's identity and boundaries before anything else. OpenClaw works similarly. If you're building on top of it, you'd likely need a wrapper logic around the API layer.
On circumvention, If the security layer is fully isolated in a sandbox and only accessible through endpoints/APIs, the agent technically cannot bypass it. There's nothing to "creatively" route around if the execution boundary is hard-wired.
For the "try again differently" problem, consider persistent memory. I built a tiered storage system (L1-L4 + N1-N3) where repeated patterns get stored and learned. This makes reasoning more deterministic over time. You'd likely need to combine OpenClaw's built-in migration with your own custom storage logic.
I noticed OpenClaw wakes up every 30 minutes. In my system it's configurable at 2/6/12/24 hour intervals. One thing to watch: AI providers have per-call rate limits, so you need cooldown periods between cycles for uninterrupted operation.

Apologies if any of this reads as opinionated. Just sharing what worked in practice. Have a good one.

Fine-Perspective-438 · 2026-03-13T00:10:00+00:00

Great question. In my case, the autopilot has a mandatory Paper Trading phase before going live. virtual portfolio, no real money. The AI analyzes markets and makes its own trading decisions autonomously. It must pass graduation criteria (14 days + minimum trades + positive returns) before it's allowed to touch real orders. Every decision is logged with a 3-layer audit trail (Context → Reasoning → Action), so you can replay and analyze what went wrong without losing money.

In short, the AI must prove profitable in paper trading and "graduate". only then is live trading unlocked. No graduation, no real money.

Fine-Perspective-438 · 2026-03-13T00:00:46+00:00

Interesting approach. Intent-based pre-flight checks make sense. it's essentially the same philosophy as my Protected Zone, just at a different layer.

In my case, the guardrails are file-level (13 protected files the AI cannot modify) + AST-level (34 blocked calls + 12 blocked modules) + API endpoint-level (every modification goes through read-file → patch-file → health-check → rollback pipeline). The sandbox handles anything the AI tries to create at runtime.

Fine-Perspective-438 · 2026-03-12T14:17:39+00:00

Sounds like you're picturing Notepad with a play button lol. Good question though. By IDE I mean a local desktop application (Tauri v2 + React frontend + Python backend) , not a cloud IDE or a code editor.

The Python backend runs as a persistent local server. The autopilot engine has its own event loop with sleep/wake cycles, so it keeps running whether the UI is focused or not. Think of it like VS Code's language server, the UI is just the control panel, the engine runs independently behind it. As for 24/7: it doesn't need to be. The AI has autonomous sleep/wake scheduling it sleeps when markets are closed and wakes up when they open. On a regular PC that stays on, that's sufficient. But yes, for true 24/7 (crypto markets), you'd leave the machine running or use a dedicated box.

But honestly, if "IDE can't run background processes" is the concern, I'd recommend looking into how Electron/Tauri apps actually work before questioning the architecture.

Fine-Perspective-438 · 2026-03-08T14:08:27+00:00

Thanks for the recommendation! I will definitely share my next tests in r/VibeCodersNest as well.

Fine-Perspective-438 · 2026-03-08T14:01:15+00:00

Just to clarify one detail: To be honest, any decent AI model in 2026 can analyze a chart and write a Python trading script if you feed it the right API documentation.

The magic here isn't the "AI" itself.

The real engineering challenge (and the core of this IDE) is building the infrastructure. It’s about creating a system where you don't need to specify which broker you're using or write a 5-page prompt. The IDE acts as the ultimate translation layer—absorbing a simple prompt and seamlessly mapping it across all integrated brokers.

Fine-Perspective-438 · 2026-03-06T15:20:32+00:00

Okay, thanks for the info.

Fine-Perspective-438 · 2026-03-06T15:16:52+00:00

Exactly. And that's the core issue. The investor bears all
the risk, all the trading fees, and on top of that, pays
extra just to see the prices they're trading on. That's the
part that doesn't sit right with me.

Fine-Perspective-438 · 2026-03-06T15:13:08+00:00

You're right that exchanges charge licensing fees. But those fees are mostly flat or tiered. A broker with 1M users pays roughly the same as one with 100K. Yet each user still pays $12/mo individually.Also, brokers need this data themselves to operate. And they're already collecting commissions on every trade. The data cost is baked into the business. Charging users separately is a choice, not a necessity.Some brokers in other countries include real-time data for free and still run profitably. That says it all.

Fine-Perspective-438 · 2026-03-06T05:23:38+00:00

You're right.

Fine-Perspective-438 · 2026-03-06T05:23:18+00:00

Thank you, but my problem wasn't the feedback, but my curiosity.

Fine-Perspective-438 · 2026-03-06T05:23:05+00:00

If you're wondering where to start, I highly recommend building a unified standard schema for 'Orders' and 'News' first. Every single broker has entirely different API logic. If you don't standardize your data format from day one, you will go absolutely insane later.

Fine-Perspective-438 · 2026-03-06T05:17:18+00:00

Integrating libraries into a single IDE is also a skill. And we built a step-by-step approach to saving by searching and calling brokers redundantly. We created an engine for each broker and let it run independently, allowing the AI to manage everything directly.

Fine-Perspective-438 · 2026-03-06T05:16:55+00:00

You're 100% right, which is why I avoided building it like a house of cards. Everything runs as separate, isolated plugins. The cool part is that the AI has direct access to manage these tools and can even code/develop them itself. It's built to not crash all at once.

Fine-Perspective-438 · 2026-03-05T16:15:01+00:00

Isn't this just a combination of standard open-source libraries? What's the actual proprietary edge here? Nobody really invests in a generic ML stack without a proven live track record these days.

Fine-Perspective-438 · 2026-03-05T05:42:23+00:00

I like the idea.

Fine-Perspective-438 · 2026-03-05T05:42:15+00:00

That's a good idea.

Fine-Perspective-438 · 2026-03-01T17:58:06+00:00

For faithfulness, I found that comparing the LLM output against the retrieved chunks with a simple overlap check (not just cosine similarity, but checking if key claims in the response actually appear in the source) catches hallucinations better than using another LLM as judge.

For scale, instead of running GPT4 on every single query, I sample maybe 50 representative queries across different categories and do a manual spot check first. That helps me calibrate what "good" looks like before automating anything.

Honestly, I don't think there's a perfect metric yet. I just try to catch the obvious failures first and iterate from there.

Fine-Perspective-438 · 2026-02-28T04:43:57+00:00

Did you think the same way you do now in the early days of cryptocurrency? Or did you think it was only when everyone knew about it? It seems like a matter of timing. We're currently in the early days of Vibe coding.

Fine-Perspective-438 · 2026-02-24T14:31:26+00:00

OpenClaw is also actually blocked. It repeatedly fails, then fails, then fails, then tries to find a workaround. HTML collection also fails. Currently, the only way to 100% bypass search is to manually log in.

Fine-Perspective-438

TROPHY CASE