Ok so what is the correct way to tell it "I don't care what is happening, you must uphold these rules at all times"? If it's not any configuration of .md files?
Ok so what is the correct way to tell it "I don't care what is happening, you must uphold these rules at all times"? If it's not any configuration of .md files?
You need to make the rule concrete somehow. I call it a "control". So for example, instead of instructing it "always run tests before committing", you (or you have it) make a git commit hook that always runs the tests first and that refuses the commit if they don't pass.
In this case, it is an advisory control only, because the LLM can also unhook that hook. And of course, it could also just disable the failing test(s) with some bullshit reason. But it is far better than assuming it will comply every time.
Then there is the "hard control", which is the inviolable that the LLM cannot bypass.
You need to move as much as is technically possible to either hard or (failing that) advisory controls.
>In this case, it is an advisory control only, because the LLM can also unhook that hook
you can in principle make a hook in the harness itself that will run an auditor prompt that checks it adhered to the policy, correct the model and also make it known in advance.
This appears to be what the claude code-review is doing:
https://github.com/anthropics/claude-code/blob/7ef6eec9d9ba8...
I've seen pretty much every model including Claude bypass commit hooks constantly if there's even a tiny bit of friction, instead of pausing and asking me to help fix why it can't run the hooks (usually something that needs to be done outside the sandbox like `npm ci`).
That's why it's critical to have these checks run in a context that the model can't bypass, such as via github actions that block PR merges. Generally works pretty well for stuff at work (where we have all this stuff set up as part of continuous integration checks), but it means that for personal stuff I have to set up quite a bit more infrastructure to ensure that models don't just skip running tests (which they LOVE to do).
So you are essentially saying "you can't, you can only safeguard from effects of LLM eventually ignoring it"
I think things like CI, linters, hooks, etc started as defense against humans eventually ignoring it (low blood sugar, under pressure, never cared in the first place, etc).
These seem like they would naturally extend to LLMs as well.
(I'm not pro-LLM on balance, but the solutions of today feel similar for humans and LLMs.)
Just like humans, yes.
> you must uphold these rules at all times"?
You need to let go of the idea that this is something LLM's can do. They can't. At best, they can bias towards rules conformance with more or less likelihood, but coverage and conformance both go down super-linearly as you accumulate more rules, more context, and more output in a session. That's simply the nature of how these tools work and you need to engineer your workflows around it if you want to use them.
If you absolutely need some rules enforced, you need to adopt some framework for validating those rules that then rejects, reprocesses, or repeats any session that fails to satisfy them. In the best case scenario, this is some traditional deterministic validator (like a linter, compiler, analyzer, exhaustive test suite, etc in coding) but if you need to process in stochastic space because its something rich and ambiguous like natural language itself, then you want to dispatch a swarm very narrow, task-focused subagents that each validate against a very constrained subset. (And prepare yourself to have those to fail sometimes too. LLM's are noisy and cannot deliver strict rule enforcement on their own.)
Ask it to come back with a filled checklist and hand it over to a different agent with a fresh context window (three lines model). Or make it collapse the context and get back to the checklist.
Subagents whose only job is to review the actions of your other agents for rule compliance? It works reasonably well for me in complex workflows using Claude Code.
Can I ask how you set this up? Like is there some way to have that run automatically, similar to “auto mode” for approvals, or do you have to invoke it regularly?
Sure! I use an orchestrator main agent whose only job is to run subagents through the workflow process I've defined. Part of that workflow is to invoke a review subagent at particular points to check the spec, the implementation plan, and the code for rule conformance.
The review subagent has its own definition and gets invoked with a specific target, so the context is very focused on just rule enforcement and I don't have problems with it skipping rules.
The whole workflow is packaged up as a plugin, but you don't need that to get this approach to work. It should be sufficient to have the rules you want enforced written out somewhere, and to either kick off a focused review agent manually referencing them, or do something like I did and have it be a defined part of your workflow (depending on the automation level you want).
Hooks..
CI runs. Local Git hooks. Cursor also has hooks built into their agent. Other agent APIs probably have something similar.
in the plugins I'm using, it's basically _always_ adding information to the context. You're not going to do it manually; you can't go back in the context and add it because that'll break the cache. The way your programming harness works is by constantly reminding the LLM of the tools available.
A good programming harness is basically a stack. A good stack keeps building each layer. You _cannot_ pull things off the bottom of the stack because that's an expensive cache hit; but you can pull things off the top. So what your harness should be doing: <tools> <rules> <user content> onto each request _then_, when you get to the next request or result, pulling that out if there's some change.
So you can see it's the cache that's either exponentially growing or having to cache bust to keep it fresh.
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