VentureBeat | May 1, 2026 11:01 AM PT

The scaffolding layer that developers once needed to ship LLM applications -- indexing layers, query engines, retrieval pipelines, carefully orchestrated agent loops -- is collapsing. And according to Jerry Liu, co-founder and CEO of LlamaIndex, that's not a problem. It's the point. "As a result, there's less of a need for frameworks to actually help users compose these deterministic workflows in a light and shallow manner," Jerry Liu explains in a new VentureBeat Beyond the Pilot podcast. Liu's LlamaIndex is one of the foremost retrieval-augmented generation (RAG) frameworks connecting private, custom, and domain-specific data to LLMs. But even he acknowledges that these types of frameworks are becoming less relevant. With every new release, models demonstrate incremental capabilities to reason over "massive amounts" of unstructured data, and they're getting better at it than humans. They can be trusted to reason extensively, self-correct, and perform multi-step planning. Model Context Protocol (MCP) and Claude Agent Skills plug-ins allow models to discover and use tools without requiring integrations for every one independently. Agent patterns have consolidated toward what Liu calls a "managed agent diagram" -- a harness layer combined with tools, MCP connectors, and skills plug-ins, rather than custom-built orchestration for every workflow. Further, coding agents excel at writing code, meaning devs don't need to rely on extensive libraries. In fact, about 95% of LlamaIndex code is generated by AI. "Engineers are not actually writing real code," Liu said. "They're all typing in natural language." This means the layers between programmers and non-programmers is collapsing, because "the new programming language is essentially English." So what's the core differentiator when the stack collapses? Context, Liu says. Agents need to be able to decipher file formats to extract the right information. Providing higher accuracy and cheaper parsing becomes key, and LlamaIndex is well-positioned here because of its developments with agentic document processing via optical character recognition (OCR).

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