At DevDay last week (October 6th), OpenAI rolled out Agent Builder, a low-code, drag-and-drop canvas that promises to let anyone string together LLM calls, tools, and connectors in minutes using OpenAI’s primitives.

While this is a great tool to reduce time to build and lower technical barriers of entry (which is why Pryon has been building our own orchestration engine), this release fundamentally misunderstands the current state of the GenAI market, and where the pain really lives. It is easier than ever to build beautiful demos of killer apps and agents. The trouble is in taking these impressive looking, but lightweight pilots and having them withstand the weight of production deployment. This is where value is actually realized in an enterprise environment. In 2025, MIT found that 88% of surveyed organizations are piloting GenAI, yet only 5% are deploying to production and finding value. OpenAI’s AgentBuilder will help teams build more of these pilots and prototypes but does not solve the problems causing production deployment failure.  

This release from OpenAI will allow developers to build these demos in minutes rather than hours; however, it does not solve the hard problems of GenAI deployment. Organizations are tripping over systems integration, compliance and legal risk, data preparation, accurate trustworthy systems downstream of information retrieval, and ongoing maintenance. This is the unglamourous plumbing that determines whether an app or an agent actually survives production and delivers value.  

Here's Pryon's take: Agent Builder looks slick, works like an automated assembly line, and absolutely shortens time-to-demo for GenAI apps, assistants, and agents.  

But an assembly line is only as good as the parts it welds together.

Builder First Principles: Think like a carmaker

If you’re building a car, the paint robots and conveyor belts matter. They will boost throughput and lower the chances of defects. But if you put a cheap carburetor into that gorgeously assembled chassis, the whole vehicle will still sputter the first time you hit the gas.  

In GenAI Speak:

Assembly Lines are visual workflow builders/orchestrators. There is no shortage of these in the market; they lower the barrier to entry for agent building, removing some (though not all) of the technical fluency needed to build, and they speed up time for development.  

Carburetors (fuel systems) are your data ingestion/preparation and information retrieval pipelines. These are what make your systems run in the real world.  

Agent Builder gives users a great assembly line. It even bolts on a default ‘carburetor’ in the form of OpenAI file search + Vector DB. For quick prototypes and small-scale deployments, great. For real world RAG, you’ll want performance parts.  

Why does this matter?

Somewhere between 85-95% of AI PoCs are failing in production. The reasons for these failures happen at the component level (and the downstream effects of this).  

• Reliability: can you trust that the answers these systems give you (and actions they take) are based in reality or hallucination? See the recent hallucination riddled Deloitte report backlash for an example of this can go disastrously wrong.  
• Maintainability: How much work is it going to take to keep this agent or app in production? How many extra hours is your team going to have to spend patching component failures before they don’t have the capacity to keep it running?
• Ecosystem compatibility: How easily can you plug in the latest state of the art models when you want to? OpenAI is using this GUI to drive traffic to their models and do not surface other, potentially better fit models into your workflows.  

The Pryon Difference: Orchestrate, With Performance Parts

• Advanced Data Parsing
  
  • Pryon’s ingestion engine is built for the reality of enterprise context; blends of structured and unstructured content, messy hard to read file types, critical context that lives in email threads, gong calls, and videos. Pryon's advanced data preparation dissects every file, slide, hard-to-read PDF, wiki page, and database row to extract clean, structured knowledge units. Our advanced parsing automatically normalizes formats, resolves duplicates, and tags critical metadata the moment content arrives. That precision up-front means agents never waste cycles unraveling messy text or chasing conflicting versions, so their answers stay fast and consistent. For enterprises, the payoff is lower prep overhead, instant compliance checks, and a content foundation sturdy enough to survive production traffic.
• Dynamic Retrieval
  
  • When agents move from answering questions to taking autonomous actions, “close enough” search stops being good enough. Pryon’s dynamic retrieval engine selects the optimal blend of vector semantics, keyword precision, metadata filters, and NL2SQL calls for each slice of a query. By tailoring retrieval strategy in real time, we drive higher recall, lower latency, and the confidence stakeholders need to let agents operate unsupervised. 
• Managed Index
  
  • Keeping an enterprise-scale index accurate is a 24/7 job, so Pryon does it for you. Our managed index refreshes on content changes, enforces fine-grained ACLs that mirror your source systems, and provides a central admin console to manage your context. Customers get world-class RAG hygiene without hiring a search SRE squad.
• Built for portability
  
  • Real-life GenAI deployments must meet legal and compliance standards. Pryon is fully portable with our complete platform, meaning you can have parity between our full RAG suite in the cloud or on-premises, air gapped. Compliance should not mean needing to sacrifice performance standards.  

Takeaway

OpenAI just gave the community a fantastic assembly line. But if the components inside the system are bargain-basement, your shiny new agent will roll out the lot clunky and coughing smoke.

Agents' value will be determined by how they execute for enterprises, not by how efficiently they were built.  

Kick the tires of Agent Builder, then come test drive our high-performance Agent stack. We know you‘ll feel the difference the moment you step on the gas.