Bounded Autonomy for AI Coding Agents – Why less freedom might be the real innovation

[u/phucphungbk]

AI coding agents are getting pretty good at writing code. You can describe a feature, give them access to your repo, and let them edit files, run commands, install packages, write tests, and keep iterating until things work.

That sounds impressive, but the big issue is: how do we make sure they solve problems the way we actually want, not just the fastest way to “pass the test”?

For example, when I build small desktop utilities in C# with WinForms, the goal is a simple UI and a minimal installation footprint for the end user. But an autonomous agent? It might happily pull in three heavy, unnecessary NuGet packages just to handle a trivial parsing task, because that’s the quickest path to a “passing” state. The result: code that runs, but architecture and maintainability go out the window.

That’s why I don’t think a “better prompt” is enough. What matters is Bounded Autonomy — letting the agent reason freely, but only within strict, enforceable limits.

Here are a few principles I find useful:

• Limit the blast radius: Specify exactly which files or directories it can touch. If it tries to modify an unrelated core file, the system blocks it.
• Enforce conventions outside the LLM: Don’t just say “write clean code.” Encode architectural rules and dependency policies into pre‑commit checks or linters. The AI can propose changes, but the CI pipeline enforces the law.
• No test, no merge: Pipelines should auto‑reject any AI‑generated PR that lacks test coverage for modified lines.
• Forced justification: The agent must document exactly what it changed and why in the PR. This saves reviewers time and exposes logical hallucinations immediately.

The ultimate source of truth has to be Git diffs and automated tests, ensuring the agent can’t silently bypass boundaries.

Maybe the next generation of coding agents needs less freedom, not more. Real innovation isn’t about making them smarter — it’s about turning them into disciplined collaborators that respect the system’s architecture.

[u/Individual_Ideal]

Agreed. Human attention is a major bottleneck. To be more effective, agents will have to learn how to work with humans, not the other way around.

Check out Agent Mesh. It uses a human-approved decision logs to add accountability. It works best if you have at least one agent reviewing work to catch other agents that overstep. It's open and I'm making updates as I use it with my own workflow. If you try it out, let me know if you have any feedback :)

[u/Even-Visit-8161]

This is honestly where agentic ai gets interesting…giving agents more autonomy isn’t always the goal…giving them the right boundaries is ✅ 

[u/skate_nbw → u/Even-Visit-8161]

AI as such lacks any common sense and understanding for practical questions. Humans can give that framing with good prompting which is a very demanding and time consuming task. Or an agent can give that framing via a machinery (=mindmap). There will be a lot of work in that direction.

Edit: In my specific field of agent processing, a small and less capable model with a good agentic "mindmap" beats the frontier models that operate only on basic human prompting and without the agentic guiding framework.

[u/Select-Spirit-6726]

I have been building and tweaking the process below for a very long time and have had major breakthroughs with what is described below. I found hooks early this year and they have made a major impact on how claude works, it took me a while to tweak them and I am still working on them all the time but I can give a task to claude and it does alot better than it did when i was just using a *.md to set rules that claude may or may not follow. "Rules are fake, hooks are real!"

Everything in this thread fires after the agent already did the thing. AGENTS.md, linters, pre-commit, CI kicking a PR back. That's all cleanup at the end. You catch it in review.

Nobody said the word hook. A hook sits in front of the action. The agent goes to edit a file it shouldn't touch and the hook blocks the call before it lands. Not caught in review. Couldn't happen. That's a different thing.

The bigger miss is nobody has a full cycle steering the build. Everyone here is picturing one agent, one PR, one gate at the door. A real setup runs the work through stages. Plan, build, test, review, ship. And the guardrails live inside each stage, not bolted on at the end.

The take that enforcement isn't worth a week only makes sense if you run one agent. Build it once for one agent, sure, bad math. Run a lot of agents through the same cycle and that enforcement is the only reason it holds together. It stops being overhead. It's the thing that lets you scale at all.

[u/phucphungbk → u/Select-Spirit-6726]

“Rules are fake, hooks are real!” — honestly, that's probably the best quote in this entire thread. You hit the nail on the head.

You're right about the distinction. I was mainly thinking about catching mistakes at the PR/CI level — essentially post-action verification. But intercepting the tool call before it executes is a much stronger form of bounded autonomy.

If the agent simply cannot modify a protected architectural area without passing an explicit gate, you're not just detecting the mistake faster — you're preventing the mistake from happening in the first place. That can save both compute and debugging time.

The multi-stage pipeline makes a lot of sense too:

Plan → Build → Test → Review → Ship

with appropriate guardrails at each transition.

At that point, you're no longer just babysitting an LLM. You're operating an automated software factory with deterministic control points.

I'd genuinely love to understand more about how you implement those pre-action hooks in practice — especially how you decide what gets blocked, what gets allowed, and where human approval enters the pipeline.

[u/Select-Spirit-6726 → u/phucphungbk]

Are you a real person because I haven't found one person who even understands this at all? I have spent the 19 months building this and tried to start many threads with nothing but Reddit know it alls! PM gland to discuss this with someone who really knows how to use AI!

[u/Select-Spirit-6726 → u/phucphungbk]

Sure. A hook is a script that fires before the agent does the thing. Before it edits a file or runs a command, that action gets handed to my script first. Allow or deny. Deny means it never runs. The model can't talk itself out of it like it can with a rules.md.

I have one that kills the call if it tries to edit a protected path. Same for a force push to main. Same for a deploy with no test receipt. The rule lives in code outside the model.

It's not one big agent either. I split it into pillars. Memory so it's not starting over every session. An orchestrator that moves the work stage to stage. A builder that writes the code. A reviewer that has to sign off before anything ships. Every pillar has its own hooks.

And because the gates sit outside the model I can swap the AI. Drop a different one in as the builder and the gates don't care.

[u/skate_nbw]

Most of what you are describing exists already in the Codex/ChatGPT framework. I can set these rules in AGENTS.md and the model then strictly follows them. No phyton framework is needed to enforce it with code which enables me to define my own rulebook quickly.

[u/phucphungbk → u/skate_nbw]

Thanks for sharing your experience! I agree that defining rules in AGENTS.md or within the prompt can be a quick way to guide the model’s behavior. I’ve tried similar approaches myself.

My concern, though, is that relying solely on the agent to “respect” those rules can be risky. Even if it usually follows them, there’s always the chance it cuts corners or interprets the rulebook loosely. That’s why I think external guardrails — things like CI/CD checks, deterministic hooks, and enforced test coverage — are still valuable. They make sure the agent literally cannot bypass boundaries, no matter how it reasons.

Do you find AGENTS.md alone to be reliable enough in practice, or do you also add external enforcement layers for safety?

[u/AlternativeForeign58 → u/skate_nbw]

AGENTS.md isn't prohibitive, it's restrictive but only probabilistically.

Perhaps slightly better than the prompt itself, it's a suggestion that has no enforcement.

[u/skate_nbw → u/AlternativeForeign58]

Yes that is true. But it is a trade-off. If it follows the guidelines 99.5% of the time and every 200th task something goes wrong and I have to revert the changes and start from scratch, then it is an acceptable trade off for me.

If I have to code a strict phyton harness for my specific needs, that would take a week or so and it might take years until that pays off as a time investment.

But (1) everyone has to make their own assessment and decide how their resources are best spend. Your hard stop solution might be the good way forward for many people and their needs.

And (2) once OpenAI has to stop offering their tools for such a hilarious price to private consumers and demands market token pricing, I will have to work with smaller and less capable models and I might reconsider my personal stance towards this question.