OpenAI’s launch of AgentKit is the latest signal of a shift already underway: the rise of AI Agents in operations. It doesn’t start the trend, but it accelerates it, making it easier for developers to prototype agents quickly and speeding up how fast AI-driven workflows can go live.

What AgentKit Is Good At

At its core, AgentKit is a developer platform. It’s not truly no-code, and it’s not pre-wired into your systems. Its value lies in helping developers prototype and test agentic workflows faster.

AgentKit brings together a previously fragmented toolset for building agents into one package:

• Agent Builder → a drag-and-drop visual canvas for creating and versioning multi-agent workflows with guardrails and evaluations built in.

  
  

• Connector Registry → a central hub for managing data and tool connections across OpenAI products and select third-party connectors.

  
  

• ChatKit → makes it easier to embed chat-based agent experiences into apps and websites with streaming responses and branded experiences.

  
  

• Evals 2.0 → expanded evaluation capabilities, including datasets, trace grading, automated prompt optimization, and support for third-party models.

  
  

For developers, this is a major step forward. AgentKit reduces the complexity of orchestration and helps teams go from concept to prototype in a shorter period of time.

But AgentKit is infrastructure. It’s a foundation for building, not a business solution for operators.

Where AgentKit Falls Short

AgentKit is developer tool designed to speed up prototyping not to run your operations.

Consumer brand operations live in the messiest part of the enterprise stack. They sprawl across NetSuite, Cin7, QuickBooks, SPS Commerce, Shopify, Amazon, 3PL portals, and an endless stream of supplier and distributor emails.

This isn’t a clean sandbox environment. These systems don’t “just connect” to each other and the cost of small errors (a missed PO, an incorrect landed cost, a wrong inventory sync) is extremely high.

For an ops leader, that means the gap between “dragging blocks on a canvas” and automating your PO processing reliably every day is still significant. And critically it puts the burden of validation on your team. If you tried to run mission-critical workflows through AgentKit directly, your ops team would need to manually review every output to catch hallucinations or errors.

Where JP Fits In

This is why we built JP, the AI Ops Assistant for consumer brands.

JP isn’t infrastructure. It’s application, designed specifically for CPG back-office workflows:

• Purchase Orders → Automatically pulled from email or EDI (SPS Commerce) and logged into ERP + spreadsheets
  
  

• Inventory Reconciliation → Keeps Shopify, Amazon, ERP, and distributor counts aligned
  
  

• Supplier & Carrier Docs → Parsed from email and PDFs into structured data
  
  

• Landed Cost & Deductions → Automated tracking of distributor deductions and cost adjustments
  
  

  
  

Human-In-The-Loop Controls

This means the burden is on us, not on your team, to validate that the data is 100% accurate. If you tried to run high-stakes workflows directly through AgentKit, your ops team would need to review every single output to catch hallucinations or errors. With JP, exceptions are intercepted before they ever hit your systems. Your team only reviews what truly needs attention.

Works with the Best Model for the Job

AgentKit is bound to OpenAI’s models. JP flexibly uses OpenAI, Anthropic, Llama, or others choosing the right tool for each workflow.

Ongoing Development & Iteration

Kaizntree doesn’t just hand you a tool and walk away. We carry the burden of development, deployment, and iteration as your operations change without your team needing to rebuild or reconfigure.

These aren’t generic automations. They’re workflows built for the back office of CPG brands and already live at companies like Firstleaf, Actual Veggies, and Avaline.

Infrastructure vs. Application

The way to think about this is simple:

• AgentKit is infrastructure. It provides the rails for a new generation of autonomous agents, but it’s not a business solution. It’s built for prototyping and still requires development work. Validation falls on your team, it only runs on OpenAI’s models, and it doesn’t come with the integrations operators actually rely on like NetSuite, Cin7, SPS Commerce, Shopify, Amazon, Xero, and more.
  
  

• JP is application. It’s built specifically for CPG brands, delivering ROI on day one with Kaizntree carrying the burden of development, deployment, data accuracy and iterating as your operations evolve. On top of that, JP comes with integrations to the systems you already use and the flexibility to run on whichever AI model is best for your workflow.
  
  

One is the industry agnostic blank canvas for prototyping. The other is the operational layer that fits seamlessly into your existing tools and evolves as your workflows change.

Final Thoughts

OpenAI’s AgentKit will accelerate the entire automation ecosystem. But for CPG Operators, the real opportunity isn’t in experimenting with a blank canvas, it’s in deploying AI that’s already wired into your workflows, understands the complexity of supply chains, and saves your team hours every single week.

That’s where JP fits in.

While the infrastructure is exciting, the real competitive advantage comes from applications that deliver outcomes today with data accuracy, full integration into your stack, and the ability to evolve alongside your operations.