From Idea to Impact in 3 Hours: Inside the First-Ever Agent Builders Challenge

Imagine building a tailored AI assistant for your operations in a single afternoon. One that could automate shift handovers, audit new apps, or even capture the knowledge of your most experienced operator – turning decades of human expertise into a scalable resource.

That's the core promise behind Tulip's new Composable AI Agents. But we wanted to cut through the hype once and for all, and test that promise ourselves. How fast could our Tulip community translate the potential of agentic AI into real operational impact?

So, at our annual Operations Calling conference, we hosted our first-ever Agent Builders Challenge. The premise was simple: 15 top app builders brought their real, everyday operational challenges and got hands-on with Tulip Agents.

The goal? See what was possible in just three hours. The results were staggering.

Ken’s experience highlights a key takeaway from the day: the builders weren't data scientists. They were operations experts who know their problems inside and out. Agents simply gave them a powerful new way to encode, apply, and augment that expertise.

As our Chief Product and Engineering Officer, Mason Glidden, explained, what makes agents different is their ability to not just analyze, but to act. They "unlock the ability to solve problems where there’s a clear goal, but the path to achieve it is a lot less clear."

The builders were able to develop these impactful solutions so quickly because Tulip AI Agents are native to a composable, extensible platform. This means that unlike rigid, black-box AI (the new monoliths), a Tulip Agent is a component you can rapidly configure and embed directly into your operational apps and workflows.

Importantly, the builders didn't need to write complex algorithms to build a powerful agent that could integrate seamlessly into their production system. They simply went into Tulip's no-code Agent Builder and defined:

• The Goal: What they wanted the Agent to achieve.

• The Instructions: How it should achieve those goals, expressed in plain English.

• The Tools: What capabilities it could use (the core of the platform advantage) – like accessing specific Tulip Tables, other apps, or connectors to other systems like ERP or QMS.

This flexibility and transparency means you can build an agent that isn't just a generalist. It's a specialist that understands and interacts with your specific operation, using your live data. This deep integration into a broader platform that connects your operations is what makes them so powerful – and provides the direct answer to the limitations of "bolt-on" AI solutions.

(To learn more about the architecture and capabilities of Composable AI Agents, you can read our AI Agents Overview Blog.)

Customers at the workshop repeatedly used the term "human macro" to describe one of the most powerful opportunities with Tulip Agents: the ability to finally encode their experts' domain and process knowledge into a tool that anyone at their organization can use. As one of the builders put it:

What was most impressive was the variety of challenges the builders solved by configuring the same underlying tool, from frontline production analysis to back-end app governance. The solutions naturally fell into two main categories, showing how agents can augment and unlock new efficiencies for every layer of the manufacturing organization.

These agents act as assistant for frontline teams, from operators to production supervisors, closing the gap between data and action on the shop floor.

• Shift Handover Agent: One team built this agent to analyze production data (like KPIs, safety issues, and machine hours) and automatically generate a prioritized to-do list for the next shift. As Jason Gillespie from Jazz Pharmaceuticals noted, this single use case could save "10 hours a week minimum" by eliminating the 2-4 hours per day teams spend just gathering information.

• Inventory Analysis Agent: Another team built this agent to search through BOMs to identify components at risk of stockouts in the near future and suggest preventative measures, helping to prevent costly line-down situations.

• Production Scheduling Agent: This agent identified process similarities across product lines to generate optimal, dynamic production schedules that maximize efficiency in space-constrained facilities.

• Defect Disposition Agent: This agent directly attacks what one builder called the "SME Bottleneck," turning a manual defect disposition report that might take "two weeks" for an expert to compile into something that can be generated in minutes.

The teams also created agents that unlocked new capacity for app builders, accelerating app design and deployment, and making it easy to onboard and support new team members.

• App Quality & Governance Agent: This agent audits Tulip apps against enterprise best practices (for things like UI, naming conventions, and data modeling conventions) and automatically suggests improvements before the app is ready to deploy, massively speeding up development cycles and ensuring enterprise-wide consistency.

• New Builder Coach Agent: The teams also built this agent, which connected to the Tulip Knowledge Base, Library, and existing apps to provide guidance on how to design a new app for a particular use case, dramatically cutting down the onboarding time for new Tulip builders.

• Expression Creator Agent: Another team built this simple agent to analyze an app’s variables, triggers, and data flow to accelerate the process of writing complex logic.

• App Change Reviewer Agent: One builder created this agent to automatically identify the changes between two app versions and assess the impact of those changes – a massive time-saver for any validation or quality assurance process.

One thing the Agent Builders demonstrated loud and clear: The power of agentic AI for operations doesn't lie in replacing your people. It’s a force multiplier that augments your team, scales their expertise, and automates the manual, time-consuming tasks that get in the way of real problem-solving. It closes the gap between data, decision, and action.

It's a new building block for manufacturers to drive their own, unique transformation.

We were blown away by what our Tulip community built in just a few hours and can't wait to see what they'll do next.

Tulip AI Agents are entering open beta this winter. To get on the list, or to inquire about our ongoing closed beta, contact your Tulip representative.

The real question is: what’s the first agent you’ll compose?