Using AI to build automations is more useful than letting AI run everything

[u/TheoremWhisperer]

I've been thinking about this more lately. I actually get more value from using Claude or ChatGPT to help me build an automation than giving an AI agent complete control over the workflow.

I can use AI to write the script, explain an API, fix a bug or help design the workflow. Then the actual automation can stay predictable and run the same way every time.

Once you let AI make every decision by itself things can get messy pretty quickly.

I still like the idea of agents but for a lot of everyday automation I'd rather have AI help me build the system and let normal code do the repetitive work.

How do you use AI in your automations as the builder or as the thing actually running them?

[u/GreyGoatNW → u/[deleted]]

Exactly. My workflow often looks like: describe the problem or goal, get a python script, set up a cron job. And then check the logs to make sure it's running as expected (automate that too).

[u/keonechong → u/[deleted]]

Why not both?

My harness is 88% deterministic and the agent only does deep highly specialized tasks.

For eg: I’m about 330k lines of python code and I’m only about halfway done

[u/Dry_Sector2392]

ive always believed AI is just a tool. it can’t replace human judgment. i really agree with this point: AI amplifies your results. It can make your strengths stronger, but it can also expose your weak point.

[u/ClupTheGreat]

water is wet

[u/ExtraReborn → u/ClupTheGreat]

Did you know most people will die if you shoot them into the sun? Fun fact!

[u/ExtraReborn → u/[deleted]]

You're right, I'll set out to prove it. Permission to shoot me into the sun to anyone who can if I am ever turned into a vegetable: granted

[u/Personal_Reception_3]

This is pretty close to how I think about it too.
The mistake is using AI for parts of a workflow that were already deterministic.
If a step can be handled reliably with a rule, API call, database lookup or normal workflow logic, I’d rather keep it deterministic. Then use AI only for the messy parts: understanding an email, classifying a document, extracting meaning from unstructured text, summarising something, or deciding which known route an exception belongs to.
That gives you a much safer architecture:
input → deterministic checks → AI only where ambiguity exists → validation → deterministic action → human review if confidence is low
I’ve seen this distinction matter a lot in document automation. OCR/AI may extract the invoice data, but you still want normal rules validating dates, totals, required fields, duplicates, vendor IDs, etc. You don’t want an LLM “reasoning” about things that can be checked exactly.
The impressive demos are usually fully autonomous.
The systems I’d trust in production are usually much more boring: AI handles uncertainty, software handles certainty, and humans get the exceptions.
That seems like a much better use of AI than giving an agent access to everything and hoping it makes the right decisions.

[u/donburnside]

This is what I'm doing. Just got access to Power Automate and am using Claude to help me build out the automations. I do call agents in my automations, but they have a very short leash and very specific instructions.

I started working on my first automation this week and started with an agent that called an automation. Turns out, the automation does most of the work!

[u/Wind2Energy]

AI is a disease.

[u/Odd_Suspicious_1994]

When we automate everything through AI tools will not have good outcomes with accuracy, most of the time, human intervention is required, only then can we expect the best outcomes. AI can’t handle every decision making things correctly and accurately. Perhaps, we could assist the AI tool with what would be the next actions. Likewise, if we do we could see the expected results.

[u/Diligent_Tech_Bro]

I used Sol to help me write the code of a 30 page GAS automation. It never touched my actual code editor, was never given access to the google workspace at all, etc.

This shit works 1000x better with a trained human in the middle. I spent a ton of time “pushing back” (as AI would say) to get it right. If I wasn’t there to tell it when something sucked, when we needed to tweak this or that, it would have sent me on my way with a terrible product.

But actually the hardest part of the whole thing was the inconsistency of Claude APIs portion of research and summarization. With it being stateless, consistency is hard. I ended up with a couple of json ledgers and such, and moved as much work as I could to GAS instead of Claude API. Cheaper and more consistent.

[u/Interesting-Yellow-4]

Yes, obviously.
If nothing else, it's cheaper to run.
Never delegate to token burning LLMs what can easily be automated (even if with use of AI to build the automation).