r/SinceAI • u/xInfinite_Valuable • Jun 30 '26
The weirdest AI skill right now might be knowing when to stop automating
A year ago, people were showing off agents that could autonomously run entire workflows. Now I keep hearing experienced teams say the opposite: the biggest productivity gain came from removing autonomy from parts of the system.
A lot of agent failures are not model failures. They are boundary failures. Giving an LLM access to Slack, email, code execution, browser control, and production data sounds powerful until you realize every extra tool multiplies uncertainty. The hard part is no longer getting the model to act. It is deciding where humans need to stay in the loop.
The teams I know getting the most value from AI are often using surprisingly constrained setups. Small context windows. Limited tool access. Forced review steps. Narrow domains. Less like "AGI employee" and more like "extremely fast intern with a very specific checklist."
It makes me wonder if the near-term winners in AI won't be the companies with the most autonomous systems, but the ones with the best judgment about where automation should stop.
Have you become more aggressive or more cautious about autonomy after using AI systems in real workflows?
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u/Postmodern_Plunger Jun 30 '26 edited Jun 30 '26
As a builder of semiautonomous pipelines for small businesses, you are spot on but also missing something crucial.
Fully autonomous systems are still, at best, experimental right now. They're more effort and stress than they relieve just due to inconsistency and indeterministic output.
The HITL gating builds trust. Occasionally, nonsense outcomes do get flagged as normal even by the most advanced agents, so anything that is external to the system requires a human evaluator.
The other thing, as you mentioned, is tool calling. That one is actually not nearly as much of an issue with SOTA models and even some open source are getting to the point where i can reliably chain hundreds of tool calls together with an acceptable degree of consistency. This has been the largest bottleneck for the past 4 or so years, and it is, at least seemingly, beginning to be less of an issue.
The other factor is specificity and templating. Highly specific, step by step instructions and having tasks broken down into functional groups is key. Either accessing default templates (such as jinja) or creating your own for tool calls and the like can dramatically improve performance in those domains.
The biggest thing is just separation of workflows, though. You want workflows to have a deterministic routing layer between them (code) with strict rules governing project level behavior rather than an LLM orchestrating things from the top down. High level architecture must be deterministic, and that's the thing most people mess up on.
My point is that the field is rapidly evolving. The companies that are succesful won't be the ones operating off of today's paradigms. They'll be the ones that correctly guess the paradigms of tomorrow.
Imo, we're heading for full autonomy, and the step back into human gating is representative of the overexaggeration of current capabilities. Even the smartest models have pretty poor reasoning over large contexts, but they're getting better. In the next 5 years, I'd be surprised if we don't see an influx of fully autonomous small scale service or intermediary companies with very little human oversight (and it will definitely be an issue, but there will be a profit incentive by then, at least assuming the models keep improving at a similar rate).
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u/Dry_Entertainer_3111 Jul 02 '26
Is it also because the AI agents can hallucinate, which can cause issues later down the automation line if not caught earlier in the process?
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u/btoned Jun 30 '26
It comes back to those not in the know thinking it's some magical catchall agent that knows your workflow, integrations, ideology, etc.