I'm personally 100% convinced of the opposite, that it's a waste of time to steer them. we know now that agentic loops can converge given the proper framing and self-reflectiveness tools.
Converge towards what though... I think the level of testing/verification you need to have an LLM output a non-trivial feature (e.g. Paxos/anything with concurrency, business logic that isn't just "fetch value from spreadsheet, add to another number and save to the database") is pretty high.
In this new world, why stop there? It would be even better if engineers were also medical doctors and held multiple doctorate degrees in mathematics and physics and also were rockstar sales people.
It's not a waste of time, it's a responsibility. All things need steering, even humans -- there's only so much precision that can be extrapolated from prompts, and as the tasks get bigger, small deviations can turn into very large mistakes.
There's a balance to strike between micro-management and no steering at all.
Most prompts we give are severely information-deficient. The reason LLMs can still produce acceptable results is because they compensate with their prior training and background knowledge.
The same applies to verification: it's fundamentally an information problem.
You see this exact dynamic when delegating work to humans. That's why good teams rely on extremely detailed specs. It's all a game of information.
Having prompts be information deficient is the whole point of LLMs. The only complete description of a typical programming problem is the final code or an equivalent formal specification.
Does the AI agent know what your company is doing right now, what every coworker is working on, how they are doing it, and how your boss will change priorities next month without being told?
If it really knows better, then fire everyone and let the agent take charge. lol
For me, it still asks for confirmation at every decision when using plans. And when multiple unforeseen options appear, it asks again. I don’t think you’ve used Codex in a while.
A significant portion of engineering time is now spent ensuring that yes, the LLM does know about all of that. This context can be surfaced through skills, MCP, connectors, RAG over your tools, etc. Companies are also starting to reshape their entire processes to ensure this information can be properly and accurately surfaced. Most are still far from completing that transformation, but progress tends to happen slowly, then all at once.
This sounds like never. Most businesses are still shuffling paper and couldn’t give you the requirements for a CRUD app if their lives depended on it.
You’re right, in theory, but it’s like saying you could predict the future if you could just model the universe in perfect detail. But it’s not possible, even in theory.
If you can fully describe what you need to the degree ambiguity is removed, you’ve already built the thing.
If you can’t fully describe the thing, like some general “make more profit” or “lower costs”, you’re in paper clip maximizer territory.
> If you can fully describe what you need to the degree ambiguity is removed, you’ve already built the thing.
Trying to get my company to realize this right now.
Probably the most efficient way to work, would be on a video call including the product person/stakeholder, designer, and me, the one responsible for the actual code, so that we can churn through the now incredibly fast and cheap implementation step together in pure alignment.
You could probably do it async but it’s so much faster to not have to keep waiting for one another.
Maybe some day, but as a claude code user it makes enough pretty serious screw ups, even with a very clearly defined plan, that I review everything it produces.
You might be able to get away without the review step for a bit, but eventually (and not long) you will be bitten.
I use that to feed back into my spec development and prompting and CI harnesses, not steering in real time.
Every mistake is a chance to fix the system so that mistake is less likely or impossible.
I rarely fix anything in real time - you review, see issues, fix them in the spec, reset the branch back to zero and try again. Generally, the spec is the part I develop interactively, and then set it loose to go crazy.
This feels, initially, incredibly painful. You're no longer developing software, you're doing therapy for robots. But it delivers enormous compounding gains, and you can use your agent to do significant parts of it for you.
> You're no longer developing software, you're doing therapy for robots.
Or, really, hacking in "learning", building your knowhow-base.
> But it delivers enormous compounding gains, and you can use your agent to do significant parts of it for you.
Strong yes to both, so strong that it's curious Claude Code, Codex, Claude Cowork, etc., don't yet bake in an explicit knowledge evolution agent curating and evolving their markdown knowledge base:
Unlikely to help with benchmarks. Very likely to improve utility ratings (as rated by outcome improvements over time) from teams using the tools together.
For those following along at home:
This is the return of the "expert system", now running on a generalized "expert system machine".
I assumed you'd build such a massive set of rules (that claude often does not obey) that you'd eat up your context very quickly. I've actually removed all plugins / MCPs because they chewed up way too much context.
It's as much about what to remove as what to add. Curation is the key. Skills also give you some levers to get the kind of context-sensitive instruction you need, though I haven't delved too deeply into them. My current total instruction set is around ~2500 tokens at the moment
Reviewing what it produces once it thinks it has met the acceptance criteria and the test suite passes is very different from wasting time babysitting every tiny change.
True, and that's usually what I'm doing now, but to be honest I'm also giving all of it's code at least a cursory glance.
Some of the things it occasionally does:
- Ignores conventions (even when emphasized in the CLAUDE.md)
- Decides to just not implement tests if gets spins out on them too much (it tells you, but only as it happens and that scrolls by pretty quick)
- Writes badly performing code (N+1)
- Does more than you asked (in a bad way, changing UIs or adding cruft)
- Makes generally bad assumptions
I'm not trying to be overly negative, but in my experience to date, you still need to babysit it. I'm interested though in the idea of using multiple models to have them perform independent reviews to at least flag spots that could use human intervention / review.
Sure, but non of those things requires you to watch it work. They're all easy to pick up on when reviewing a finished change, which ideally should come after it's instructions have had it run linters, run sub agents that verify it has added tests, run sub agents doing a code review.
I don't want to waste my time reviewing a change the model can still significantly improve all by itself. My time costs far more than the models.
you give it tools so it can compile and run the code. then you give it more tools so it can decide between iterations if it got closer to the goal or not. let it evaluate itself. if it can't evaluate something, let it write tests and benchmark itself.
I guarantee that if the criteria is very well defined and benchmarkable, it will do the right thing in X iterations.
(I don't do UI development. I do end-to-end system performance on two very large code bases. my tests can be measured. the measure is very simply binary: better or not. it works.)
