Optimizing Document Pipelines for Agentic Workflows
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the gist
Moving from RAG to agents requires treating document parsing as a multi-step, verifiable pipeline where high-quality structured inputs significantly reduce reasoning tokens and improve accuracy.
Improving Input Quality for LLMs
Transitioning from simple RAG to agentic workflows shifts the primary bottleneck from information synthesis to input reliability. Because agents perform multi-step tasks, errors in initial document parsing compound throughout the pipeline. To mitigate this, teams should move away from raw PDF ingestion and toward structured representations. Providing frontier models with parsed, structured data rather than raw PDFs consistently outperforms models on benchmarks like the GDP PDF, while simultaneously reducing the number of reasoning tokens required for the model to reach a correct conclusion.
Agentic Parsing and Orchestration
Effective document processing requires a hybrid approach rather than relying solely on large Vision-Language Models (VLMs). Small, CPU-efficient computer vision models should be used for layout detection and segmentation at scale, while VLMs are reserved for semantic tasks. To maintain accuracy, implement an agentic OCR layer that performs token-level corrections (such as fixing a '0' versus an 'O') rather than forcing the model to regenerate entire pages, which often leads to unfaithful hallucinations.
For retrieval, embedding models struggle to correlate natural language queries with raw HTML or complex markdown blobs. The most effective strategy is to generate two distinct representations for the same document: an HTML or markdown structure for the LLM to reason over, and a natural language summary specifically optimized for the embedding model to improve retrieval accuracy.
Harnessing Agents for Complex Tasks
Complex tasks like extracting data from line charts or processing massive forms require agent harnesses rather than single-shot prompts. For line charts, an agent should use a code interpreter to iteratively reconstruct data points, performing self-checks until the output matches the visual data. This approach solves the precision-recall trade-off observed in current benchmarks, where frontier models are often precise but suffer from high rates of row-dropping. Teams should implement granular evaluations at every stage of the pipeline, treating the document processing layer as a file system that agents can interact with, rather than a fixed, linear pipeline.