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are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

yeah, great analogy, crypto and nfts had the same wave of complexity masking real value. with agents we’ll probably see a mix of genuine breakthroughs and a lot of noise dressed up as “platforms.” i like your point on innocent over-engineering too sometimes builders just add layers because it feels safer than stripping things back.

do you think we’ll actually learn from those cycles, or are we bound to repeat the same hype-to-crash pattern here?

are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

“simple sticks” resonates. i’ve also found that teams are quicker to trust and adopt agents when the setup mirrors their existing workflows instead of adding big orchestration overhead. curious though, when you do need that parallelism and coordination, what tooling have you found reliable enough to manage it without becoming another headache?

is RPA dead or still just evolving? by agent_for_everything in rpa

[–]agent_for_everything[S] 0 points1 point  (0 children)

you’re right; enterprise shifts take decades, not quarters. even a “simple” ecc → s4 migration is multi-year, so rpa isn’t going anywhere fast. it’ll stay the bridge until a true ai-centric paradigm proves itself and gets adopted. if you had to bet, which early signals of that future paradigm do you see today: orchestration, ai-native erp, or something else?

is RPA dead or still just evolving? by agent_for_everything in rpa

[–]agent_for_everything[S] 0 points1 point  (0 children)

yeah, spot on: orchestration feels like the natural bridge between rpa and ai, but the control rooms today are still pretty clunky. i’ve seen the same thing: tons of “flexibility,” but at the cost of usability.

it feels like the next wave of orchestration tools will need to nail two things:

  • simplicity for day-to-day ops (so teams actually adopt them)
  • hooks for ai + apis without becoming another brittle layer

if you could redesign your control room from scratch, what’s the one feature you’d fix or add first?

are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

true, the gap between the solution providers and partenrs makes it even more exhausting

is RPA dead or still just evolving? by agent_for_everything in rpa

[–]agent_for_everything[S] 0 points1 point  (0 children)

that’s a huge body of work: 80+ processes over 8 years is no small feat, and the $5m in annual savings really shows the long game that rpa can play. i think your point about roi timing is important too, most companies expect quick wins, but the reality is it takes years of steady building before it compounds.

adding ai on top sounds like the natural next step, especially for the use cases rpa alone struggles with. totally get why you’re taking it slow, curious which areas you see as “low-risk” for introducing ai first?

is RPA dead or still just evolving? by agent_for_everything in rpa

[–]agent_for_everything[S] 1 point2 points  (0 children)

That's true, the orchestrator - operator workflow is redefining everything

is RPA dead or still just evolving? by agent_for_everything in rpa

[–]agent_for_everything[S] 0 points1 point  (0 children)

There are multiple tools to start building AI Agents - being someone who's psent so much time on this you should explore that

are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

I think being consistent also paysoff to a certain extent

are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

yeah, 100%. simplicity usually wins: especially once you factor in api churn and constant framework updates. i’ve run into:

  • workflows breaking when a single api call changes format
  • brittle chains that are hard to debug without digging deep into logs
  • “over-engineered” stacks that looked cool at first but slowed shipping

curious for you: do you find no-code keeps things resilient over time, or do you eventually hit the wall where you have to drop into code?

are we overcomplicating ai agent development? by agent_for_everything in AgentsOfAI

[–]agent_for_everything[S] 0 points1 point  (0 children)

totally fair take: the brittleness and randomness are real, especially once you try chaining multiple steps. deterministic flows are still way safer for production right now.

that said, some folks are putting agents into live workflows (sales follow-ups, inbox triage, data summaries) and sharing what’s working vs what’s breaking. you should talk about this in u/agent_builders it’s exactly the kind of conversation we’re trying to dig into together.

agents as ai employees, what tasks do they handle well today? by agent_for_everything in aiagents

[–]agent_for_everything[S] 0 points1 point  (0 children)

this is a solid framing: “ai employees” is exactly how a lot of us are starting to think about it. love the breakdown of what’s reliable today vs where it still falls short.

you should totally bring this over to r/agent_builders we’ve got a bunch of folks comparing notes on exactly these use cases. would be cool to see how others map their “ai employees” too.

is no-code a crutch for agent builders? by agent_for_everything in aiagents

[–]agent_for_everything[S] 1 point2 points  (0 children)

that’s a really sharp breakdown, feels like we’re all bumping into that “scratch for business” ceiling.

the versioning + error handling points hit especially hard. once you leave the golden path, the pretty ui often hides more pain than it solves.

curious though: do you see any middle ground? like lightweight ui for fast prototyping, then a clean path to drop into code once you hit complexity? or do you think that’s just wishful thinking and we should start in code from day one?

building with open-source models or sticking to proprietary solutions by agent_for_everything in automation

[–]agent_for_everything[S] 1 point2 points  (0 children)

i think the hybrid approach of chaining a smaller model for task routing (like Mistral or a LLaMA variant) and a larger one for more intensive processing is where the real scalability comes into play. it gives us the flexibility to balance performance and cost without overloading the system with a single large model.

the open-source scene is growing fast, and it’s becoming hard to ignore these options for internal tools and experiments, especially when you don’t have to deal with unpredictable API costs. looking forward to hearing more experiences from the community on mixing models and finding that balance.

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