Building AI Orchestration

48 hours. A room full of brilliant minds. A swarm of agents, both human and artificial, working side-by-side.

The recent n8n AI orchestration hackathon in SF wasn’t just a sprint. It was a glimpse into the future of work.

Our team walked in with curiosity, taste, and a healthy respect for what’s coming. We walked out placing in the top 5 of over 30 teams—and more importantly, with a working prototype powered by a swarm of AI agents and deterministic workflows executing across coding, product development, strategy, and automation. Here’s what we built, how we did it, and why this moment felt like the beginning of a tectonic shift.

🚀 What We Built: An AI-Powered Operating Unit

Our project was Holistic, an AI running coach, and revolved around a modular system of AI agents—each responsible for discrete, functional business tasks:

A customer support agent that updates the end user on personalized workout goals to optimize performance, day-by-day calendar scheduling, and making recommendations on health data.

A coding agent that inspects the codebase, drafts files, runs tools, compiles, tests, and iterates.

A strategy agent that helps draft product requirement docs (PRDs), conduct competitive research, and identify integration partners.

A data agent that ingests data from APIs like HealthKit and Oura, analyzes trends, and feeds back into the product loop.

The end result? A swarm of asynchronous, purpose-built agents, orchestrated using n8n, that gave us the superpower of working like a team 5x our size.

🧠 Behind the Curtain: Our Tool Stack

We pulled together a best-in-class AI-first workflow:

n8n orchestration workflows

ChatPRD to draft and refine product specs

Cursor for rapid coding and iteration

v0.dev for UI prototyping

Supabase for vector storage

Perplexity API + GPT-4o for strategy and content generation

Airtop for web automation and repurposing content

Gamma AI to convert our concepts into a pitch-ready deck

Every AI workflow we built followed a hybrid pattern: deterministic logic layered with intelligent AI agent components. The deterministic backbone gave us structure and reliability. The agents gave us depth, adaptability, and speed.

🧩 The Agentic Workflow Shift

Most workflows today rely on static automation. What we experienced was a whole new paradigm: agents making decisions, iterating autonomously, and collaborating like an intelligent swarm.

Example: our AI would write a basic PRD, generate an implementation plan, write tests for each function, then iterate until all tests passed—while we stepped back and watched. At one point, watching code cascade through the terminal felt like riding in a Waymo—autonomous, focused, fast.

🔍 Use Cases We Explored

AI usage can be broken down into six core primitives:

Content creation – generating slides, documentation, and messaging

Research – market sizing, trend analysis, partner scouting

Coding – prototyping, debugging, refactoring

Data analysis – reporting and dashboarding with real-time insights

Ideation & strategy – building out roadmaps based on goals and constraints

Automation – scheduling, summarizing, and acting on recurring tasks

💡 Lessons Learned

Not choosing UI first - prototyping the interaction with AI before thinking about what kind of web or mobile interface to wrap around it. When extended out actual AI first interactions, they gave us fresh ideas about what the right interface to wrap around it.

Expect AI to be able to do magic and not think deeply enough about all the hard work of evaluation, creation of guard rails, interface design, user authorization, security, cloud deployment — although the latter was outside the scope of the hackathon.

Every agent needs a job description. Specialization was key: agents with tightly defined roles performed better and could collaborate more effectively.

Start with outcomes. Reverse-engineering from end-user goals helped us define what mattered most in the flow.

Don’t forget the human side. Amid code and competition, we found that stepping back, connecting on a personal level to better understand each other made the biggest difference to motivate and supercharge our team.

⚠️ Mistakes to Avoid

Thinking AI can “just figure it out” without detailed, structured system prompts.

Ignoring the complexity of product integration, functional webhooks, API endpoints, evaluation, UX, and deployment

Prioritizing feature completeness over the “why” and “how” moment in demos

🌎 What’s Next

These aren’t just isolated capabilities. We’re on the cusp of agent-to-agent communication, a world where AI agents evolve into operating business units: sales agents talking to product agents, finance agents working with competitive intel agents, all towards shared company goals and resulting in shortens sales cycles, accelerates GTM, and outpacing incumbents clinging to legacy stacks.

This wave of tech makes the internet look quaint and will build the next generation of startups.

Those who aren’t embracing it aren’t standing still—they’re moving backward.

If you’re not experimenting with agents, workflows, and orchestration layers, you’re missing the biggest unlock in decades. The winners won’t be the ones who build the biggest models—they’ll be the ones who orchestrate them best.