DEV Community: Nidhish Akolkar

I Built a 600+ Node AI Orchestration Infrastructure in n8n. Here’s What Actually Happened.

Nidhish Akolkar — Sun, 24 May 2026 13:18:12 +0000

[ The full infrastructure at peak scale. This is what 600+ nodes of parallel agent execution looks like.]

Most people use n8n for simple automations.
Webhook → Gmail → Slack.
That’s fine. That’s what it was built for.
I used it to build a distributed AI orchestration system with 600+ interconnected nodes, parallel agent execution pipelines, dynamic routing layers, modular tool registries, and centralized aggregation systems.
It took several months of continuous iteration to get there. It was never designed to be this big. It grew one workflow became ten, ten became interconnected execution chains, and eventually the whole thing became something that looked less like automation and more like a distributed cognitive infrastructure.
This is the honest story of how that happened, what broke along the way, and what building it actually taught me.

Why n8n and Why It Became Surprisingly Powerful
The original problem was simple: I wanted to build AI systems that could think modularly, route tasks dynamically, use tools autonomously, and execute multi-step reasoning without me hardcoding every decision path.
Most frameworks at the time either hid too much, required too much setup, or made it impossible to see what was actually happening during execution.
n8n accidentally solved one problem I didn’t expect: it made complex execution visible.
You could literally see the execution graph. Every branch, every connection, every data flow laid out in front of you. That changed how I thought about building systems entirely. Instead of reasoning about code, I started reasoning about execution architecture. That shift is what eventually led to 600+ nodes.
The Architecture Five Layers
The infrastructure wasn’t built randomly. It evolved into five distinct layers, each with a specific responsibility.
Layer 1: Trigger Layer
Everything entered through a centralized HTTP trigger system. The entire infrastructure was API first external applications, frontend systems, and agents themselves could invoke execution dynamically. Every incoming request passed through normalization before touching any execution logic. Nothing entered the pipeline raw.
Layer 2: Preprocessing Layer
Before any AI execution began, requests were enriched, classified, and transformed. This layer handled intent classification, capability mapping, context injection, and validation. At scale this became the most quietly important part of the system without it, downstream agent execution became inconsistent in ways that were almost impossible to trace back to their source.
Layer 3: Routing Layer
This was the most complex section. The routing layer functioned like an operating system scheduler for AI execution deciding which agents should run, in what order, under what conditions, with what tools, and at what priority. It contained conditional execution systems, intent routers, capability dispatchers, fallback mechanisms, and retry logic. This is where the workflow stopped being linear and became a tree.
Layer 4: Parallel Agent Execution
This is where the infrastructure became genuinely powerful. Instead of a single chain, the system ran specialized agents simultaneously across parallel branches research agents, summarization agents, validation agents, transformation layers, classification pipelines. Multiple branches executed at the same time, and their outputs were later merged downstream. New capabilities could be added as new branches without touching anything else. One branch failing didn’t collapse the whole system.
Layer 5: Aggregation Layer
Every parallel branch eventually converged into a centralized aggregation system. This layer collected outputs, resolved conflicts, synthesized context, ranked results, and formatted responses. It was the hardest layer to get right and the one that nearly broke everything.

The Night Everything Broke
I want to be specific about this because vague references to “debugging challenges” don’t capture what working at this scale actually feels like.
The worst problem I hit was context consistency during aggregation.
Here’s what was happening: parallel branches were executing simultaneously, but they didn’t finish at the same time. One branch would complete immediately. Another would hit a retry loop after a transient failure. A third would take longer because of a heavy processing step. When these branches finally converged at the aggregation layer, they were merging states from different points in time.
The result: outputs became inconsistent. Memory objects got corrupted. Valid data from one branch would collide with stale or partial data from another. And the worst part the individual branches looked completely fine when you inspected them in isolation. The problem only appeared at convergence.
I spent an entire night manually tracing execution paths across dozens of interconnected branches trying to understand why perfectly valid outputs were collapsing only during aggregation. It wasn’t a bug in any single node. It was an emergent property of timing differences across the entire graph.
The fix was enforcing strict execution barriers aggregation layers would only merge outputs after all upstream branches either completed or explicitly failed, never during an intermediate state. Simple in hindsight. Invisible until you’ve been burned by it.

What This Scale Actually Taught Me
Building to 600+ nodes teaches you things that tutorials and courses simply cannot.
Debugging changes completely. At small scale, bugs are local. At this scale, failures are emergent they appear in places completely disconnected from where they originate. You need execution observability built into the architecture from the start, not added later.
State is the hardest problem. Parallel systems share state across branches that execute at different speeds. State drift where different parts of your system have inconsistent views of the same data becomes a constant engineering concern.
Visual architecture has real limits. n8n’s visual nature was the biggest advantage early on and the biggest challenge late on. At 600+ nodes, navigation became difficult. Dependency tracing became painful. What made experimentation fast made maintenance hard.
Modularity is not optional at scale. The tool registry system abstracting tools into reusable callable modules rather than hardcoding execution logic was the single architectural decision that made the difference between a system that could evolve and one that would have collapsed under its own complexity.

The Bigger Lesson
The most important thing this project taught me has nothing to do with n8n specifically.
It’s that intelligence alone is not the hard part of AI systems.
Calling an LLM API is easy. Getting one model to respond to one prompt is a solved problem. The real engineering challenge begins when you try to coordinate multiple capabilities, manage execution state across parallel processes, route tasks dynamically based on context, recover from partial failures, and aggregate outputs from systems that don’t finish at the same time.
That’s orchestration. And orchestration is where most AI systems fail quietly not because the model was wrong, but because the infrastructure around it wasn’t built to handle real-world execution complexity.
This project was my education in that problem.
Six hundred nodes. Several months. One very long night staring at an aggregation layer that shouldn’t have been failing.
Worth every minute of it.

Myself Nidhish Akolkar I am a Computer Engineering student and AI Systems Engineer based in Pune, India. I build autonomous multi-agent AI infrastructure and run a funded institutional AI & ML laboratory.
GitHub: github.com/nidhishakolkar01-lgtm
LinkedIn: linkedin.com/in/nidhish-a-akolkar-30a33238b

I Built a 600+ Node AI Orchestration Infrastructure in n8n. Here's What Actually Happened.

Nidhish Akolkar — Sun, 24 May 2026 12:52:28 +0000

[ The full infrastructure at peak scale. This is what 600+ nodes of parallel agent execution looks like.]

Most people use n8n for simple automations.
Webhook → Gmail → Slack.
That's fine. That's what it was built for.
I used it to build a distributed AI orchestration system with 600+ interconnected nodes, parallel agent execution pipelines, dynamic routing layers, modular tool registries, and centralized aggregation systems.
It took several months of continuous iteration to get there. It was never designed to be this big. It grew one workflow became ten, ten became interconnected execution chains, and eventually the whole thing became something that looked less like automation and more like a distributed cognitive infrastructure.
This is the honest story of how that happened, what broke along the way, and what building it actually taught me.

The Architecture Five Layers
The infrastructure wasn't built randomly. It evolved into five distinct layers, each with a specific responsibility.
Layer 1: Trigger Layer
Everything entered through a centralized HTTP trigger system. The entire infrastructure was API first external applications, frontend systems, and agents themselves could invoke execution dynamically. Every incoming request passed through normalization before touching any execution logic. Nothing entered the pipeline raw.
Layer 2: Preprocessing Layer
Before any AI execution began, requests were enriched, classified, and transformed. This layer handled intent classification, capability mapping, context injection, and validation. At scale this became the most quietly important part of the system without it, downstream agent execution became inconsistent in ways that were almost impossible to trace back to their source.
Layer 3: Routing Layer
This was the most complex section. The routing layer functioned like an operating system scheduler for AI execution deciding which agents should run, in what order, under what conditions, with what tools, and at what priority. It contained conditional execution systems, intent routers, capability dispatchers, fallback mechanisms, and retry logic. This is where the workflow stopped being linear and became a tree.
Layer 4: Parallel Agent Execution
This is where the infrastructure became genuinely powerful. Instead of a single chain, the system ran specialized agents simultaneously across parallel branches research agents, summarization agents, validation agents, transformation layers, classification pipelines. Multiple branches executed at the same time, and their outputs were later merged downstream. New capabilities could be added as new branches without touching anything else. One branch failing didn't collapse the whole system.
Layer 5: Aggregation Layer
Every parallel branch eventually converged into a centralized aggregation system. This layer collected outputs, resolved conflicts, synthesized context, ranked results, and formatted responses. It was the hardest layer to get right and the one that nearly broke everything.

The Night Everything Broke
I want to be specific about this because vague references to "debugging challenges" don't capture what working at this scale actually feels like.
The worst problem I hit was context consistency during aggregation.
Here's what was happening: parallel branches were executing simultaneously, but they didn't finish at the same time. One branch would complete immediately. Another would hit a retry loop after a transient failure. A third would take longer because of a heavy processing step. When these branches finally converged at the aggregation layer, they were merging states from different points in time.
The result: outputs became inconsistent. Memory objects got corrupted. Valid data from one branch would collide with stale or partial data from another. And the worst part the individual branches looked completely fine when you inspected them in isolation. The problem only appeared at convergence.
I spent an entire night manually tracing execution paths across dozens of interconnected branches trying to understand why perfectly valid outputs were collapsing only during aggregation. It wasn't a bug in any single node. It was an emergent property of timing differences across the entire graph.
The fix was enforcing strict execution barriers aggregation layers would only merge outputs after all upstream branches either completed or explicitly failed, never during an intermediate state. Simple in hindsight. Invisible until you've been burned by it.

What This Scale Actually Taught Me
Building to 600+ nodes teaches you things that tutorials and courses simply cannot.
Debugging changes completely. At small scale, bugs are local. At this scale, failures are emergent they appear in places completely disconnected from where they originate. You need execution observability built into the architecture from the start, not added later.
State is the hardest problem. Parallel systems share state across branches that execute at different speeds. State drift where different parts of your system have inconsistent views of the same data becomes a constant engineering concern.
Visual architecture has real limits. n8n's visual nature was the biggest advantage early on and the biggest challenge late on. At 600+ nodes, navigation became difficult. Dependency tracing became painful. What made experimentation fast made maintenance hard.
Modularity is not optional at scale. The tool registry system abstracting tools into reusable callable modules rather than hardcoding execution logic was the single architectural decision that made the difference between a system that could evolve and one that would have collapsed under its own complexity.

The Bigger Lesson
The most important thing this project taught me has nothing to do with n8n specifically.
It's that intelligence alone is not the hard part of AI systems.
Calling an LLM API is easy. Getting one model to respond to one prompt is a solved problem. The real engineering challenge begins when you try to coordinate multiple capabilities, manage execution state across parallel processes, route tasks dynamically based on context, recover from partial failures, and aggregate outputs from systems that don't finish at the same time.
That's orchestration. And orchestration is where most AI systems fail quietly not because the model was wrong, but because the infrastructure around it wasn't built to handle real-world execution complexity.
This project was my education in that problem.
Six hundred nodes. Several months. One very long night staring at an aggregation layer that shouldn't have been failing.
Worth every minute of it.

4 Years of Coding Before College — What I Actually Learned

Nidhish Akolkar — Sun, 24 May 2026 09:23:12 +0000

I didn't start coding because someone told me to.
There was no career counselor, no parent pushing me toward it, no school assignment that sparked something. I started because I was obsessed with one idea: creating things instead of just consuming them.
I'd spend hours watching tech videos, exploring random software, breaking things, fixing them, breaking them again. The whole ecosystem AI, futuristic systems, communities, events, branding all of it fascinated me. But it wasn't about becoming a developer. It was pure curiosity. It was the feeling that somewhere inside all of this, I could build things that only existed in my head.
The moment that clicked, I just kept going deeper.
That was four years ago. I was still in school.

The Trap I Fell Into Early: Perfecting the Vision Instead of Shipping It
I think big. Always have.
When I get an idea, I don't just see the first version I see the whole thing. The scale, the community around it, the systems underneath it, the experience someone would have using it three years from now.
For a long time, I thought that was a strength. And in some ways it is. But I learned the hard way that thinking too far ahead without executing in the present is just a comfortable way to avoid the discomfort of starting.
I'd plan projects in extreme detail. Build mental structures around them. Imagine the future scale of what they could become. And then delay actually building them because they didn't feel ready enough yet.
Nothing is ever ready enough. That's not a cliche it's something you only really understand after watching a perfectly planned project die in a notebook while a half-baked idea someone else shipped becomes real.
The other thing I learned the hard way: motivation isn't a system. Some days you feel unstoppable. Some days you don't want to touch anything. I used to wait for the motivated days. Now I build anyway. The consistency of showing up even on the flat days is what separates things that get finished from things that stay ideas.

The Moment It Stopped Feeling Like Practice
There's a specific feeling I remember.
It was when people started looking at me for direction instead of me looking at others.
Not because I announced anything. Not because I put a title on it. Just because things I had imagined clubs, events, workshops, systems, communities had become real enough that other people were experiencing them. Things that were random thoughts in my head were now things that existed in the world, that people showed up for, that had a life outside of me.
That was the moment it started feeling real.
Not the first time code ran. Not the first time a project worked. It was the first time I looked at something and thought I made that. It didn't exist before me. Now it does.
That feeling is still what drives everything.

The Part Nobody Talks About: It Can Feel Lonely
Most people in my college knew me as the tech and community guy. Teachers noticed the clubs and workshops and events. Students would come to me for direction on technical things.
But the actual journey the real learning happened almost entirely outside of classrooms.
YouTube videos at midnight. Random communities online. Experimenting with things nobody around me was experimenting with. Failing at ideas that nobody around me even understood well enough to tell me were bad ideas.
When you're trying to build things that are bigger than your current environment, there's a part of the journey that feels internally lonely. Not in a sad way. Just in a I'm figuring this out without a map kind of way.
I don't think that's a bad thing. I think it's just what it actually feels like to be early on something. The people around you are living in the present version of the world. You're spending your energy trying to build the next one.

What I Actually Know Now That I Didn't Know Then
Execution matters more than potential.
That's it. That's the whole lesson.
You can have the best ideas, the clearest vision, genuine talent, and real ambition and none of it matters if things never get finished. The world doesn't reward people for what they were capable of. It rewards people for what they actually shipped.
I used to think bigger was always better. That the scale of your vision was the measure of your seriousness. Now I think the real skill — the one that actually compounds over time is learning how to sustainably turn ideas into reality. How to build systems around your work instead of relying on energy and inspiration. How to finish things. How to be patient with the process without losing the ambition that started it.
My biggest growth over these four years isn't that I learned how to think bigger.
It's that I'm learning how to build better.

Four years in. First year of college. A funded AI lab, a multi-agent system in development, and a patent in progress.
And genuinely, the most useful thing I can tell anyone starting out is this: start before you're ready, ship before it's perfect, and stay consistent when the motivation runs out.
Everything else is details.

Nidhish Akolkar is a Computer Engineering student and AI Systems Engineer based in Pune, India. He builds autonomous AI systems and runs a funded institutional AI & ML laboratory.
GitHub: github.com/nidhishakolkar01-lgtm
LinkedIn: linkedin.com/in/nidhish-a-akolkar-30a33238b