u/Arc_bong

I've been someone who started building stuff in last 2 yrs so, no-code AI tools lately, and something has been bugging me. Building and deploying and testing one agent seems textbook now.

But then I started wondering what happens when people actually start applying these things seriously.

Say I have 10 agents across different workflows: one handles lead qualification, one summarizes support tickets, one works with internal docs, one handles reporting, one triggers automations

At that point for real work, what's used to keep track...like

How do I know which agents I have?

How do I version them when I change prompts/tools?

How do I control what each agent is allowed to access?

How do I test an agent before letting it loose on real users/data?

How do I see what actually happened when an agent makes a bad decision?

And if I'm a no-code builder, I'd really rather not have to suddenly learn a whole DevOps stack just to manage the things I created without code 😅

I'm curious how people here handle this today. Are there really any no-code tool capable of this?

Is the normal answer basically "use something like n8n/Make/Zapier + spreadsheets + logging + some manual discipline", or are the newer AI-agent platforms starting to solve the management/governance layer as well?

I've seen Lyzr's control plane/ Agent studio discussed as one approach to this, while products like Relevance AI, Microsoft Copilot Studio and others are coming at the broader no-code/agent-management problem from different angles.

Would be interested to hear what people here are actually using once they go beyond 1–2 agents or what companies or start-ups use, and where the no-code abstraction starts to break down?

I have been playing with the local-LLM side of things, once Qwen3.8-27B dropped yesterday... Ofc i spent the initial hours bawling over the benchmark numbers 😅

But ofc while all the running the model is good... For someone like me (grad student), what happens after you've got the model running becomes less obvious...

By now the argument for "harness engineering" is really strong... Establishing that the durable engineering advantage may increasingly sit around the model, and that a better harness improves agent performance far more than simply swapping one good model for another.

Qwen3. 8-27B feels like a good example of why that matters.

Currently stacks like:

Model -> harness -> tools -> state/context -> permission -> eval -> deploy

I know I just wrote basic stuff😭

Me can figure out this stuff for 1 agent... But for people who actually get work done by locally running models or for companies that have 20,50,more agents:

How do u manage and version them?

How do u give each one scooped tool access?

How evaluate, and actually observe their actual actions after deployment?

How do u keep the whole thing manageable across machines/clouds?

I'm curious as to what ppl here are actually using for this parts.... Is the ans basically DIY stack around llama.cop+ Langgraph/OpenCode + own tooling or are the different llatforms for agent-infrastructure and control plane doing good?

I saw NVIDIA is going more runtime direction with NemoClaw and OpenHands has something on control plane, on ln I came across Lyzr and their no code control plane .... How much of those are branding and how much actual work?

Would like to know ur views...that is if this doesn't get flaged as marketing 🥀

I have been playing with the local-LLM side of things, once Qwen3.8-27B dropped yesterday... Ofc i spent the initial hours bawling over the benchmark numbers 😅

But ofc while all the running the model is good... For someone like me (grad student), what happens after you've got the model running becomes less obvious...

By now the argument for "harness engineering" is really strong... Establishing that the durable engineering advantage may increasingly sit around the model, and that a better harness improves agent performance far more than simply swapping one good model for another.

Qwen3. 8-27B feels like a good example of why that matters.

Currently stacks like:

Model -> harness -> tools -> state/context -> permission -> eval -> deploy

I know I just wrote basic stuff😭

Me can figure out this stuff for 1 agent... But for people who actually get work done by locally running models or for companies that have 20,50,more agents:

How do u manage and version them?

How do u give each one scooped tool access?

How evaluate, and actually observe their actual actions after deployment?

How do u keep the whole thing manageable across machines/clouds?

I'm curious as to what ppl here are actually using for this parts.... Is the ans basically DIY stack around llama.cop+ Langgraph/OpenCode + own tooling or are the different llatforms for agent-infrastructure and control plane doing good?

I saw NVIDIA is going more runtime direction with NemoClaw and OpenHands has something on control plane, on ln I came across Lyzr and their no code control plane .... How much of those are branding and how much actual work?

Would like to know ur views...