Agents in Document Digitization and Chunking for LLMs

Apr 13, 2025

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Agents in Document Digitization and Chunking for LLMs
Technical Implementation of Agents in OCR and Chunking
Comparative Analysis of Agent-Based Strategies
Practical Deployment Considerations

Large Language Models (LLMs) excel at language tasks but face challenges with raw document inputs and long contexts. Document digitization (converting scans or complex formats into text) and chunking (splitting text into manageable pieces) are critical preprocessing steps. Recent research in 2024–2025 shows that agent-based approaches – intelligent processes that utilize LLMs or other AI modules as autonomous agents – significantly improve these steps compared to static pipelines. Agents can coordinate OCR tools, adaptively segment documents, and refine chunks to maximize LLM performance. Below, we review the technical implementations, compare agent strategies (rule-based vs. learning-based vs. multi-agent), and discuss practical deployment considerations, citing cutting-edge arXiv works from 2024 and 2025.

Technical Implementation of Agents in OCR and Chunking

LLM-Powered OCR and Parsing: Traditional Optical Character Recognition (OCR) often produces errors or loses layout information, degrading downstream LLM accuracy. Agent-based systems tackle this by combining multiple extraction methods and using LLMs themselves as OCR agents. For example, Perez et al. (2024) introduce a multi-strategy parsing pipeline that uses a multimodal LLM as an OCR agent alongside conventional OCR and fast text extraction (Advanced ingestion process powered by LLM parsing for RAG system) . A specialized Multimodal Assembler Agent then merges text, tables, and described images into a structured markdown per page . This node-based parsing captures document structure (headers, tables, figures) and metadata, yielding richer context for the LLM . By integrating LLM-based vision understanding, such agent pipelines improve overall comprehension; indeed, Perez et al. report improved answer relevancy and faithfulness in a Retrieval-Augmented Generation (RAG) system using this method .

Adaptive Chunking and Preprocessing: Once text is extracted, agents determine how to chunk it for LLM input. Naïve splitting (fixed-size or simplistic rules) often fails on complex layouts . Agent-driven approaches instead adapt to content semantics and structure. One strategy is to use the LLM itself as a chunk evaluator. Singh et al. (2024) propose ChunkRAG, where an LLM agent semantically splits documents and scores each chunk’s relevance to the query (HERE). Irrelevant chunks are filtered out before prompting the LLM, significantly reducing hallucinations and improving factual accuracy in answers . This shows agents can not only chunk but also selectively curate chunks for optimal LLM grounding. Other systems generate auxiliary context to enhance chunks: for instance, an agent may create questions and summaries for each section or table to enrich the chunk with context . Such preprocessing agents ensure that each chunk is self-contained and informative, which is crucial given LLM context window limits.

Pipeline Orchestration: Agents often operate in sequences, forming an end-to-end digitization pipeline. In the multi-agent RAG ingestion system above, separate agents handle assembling pages, extracting metadata, generating sectional Q&As, and summarizing nodes (Advanced ingestion process powered by LLM parsing for RAG system) . Each agent’s output feeds the next, preserving important relationships (e.g. linking a table with its caption and summary). Likewise, DocETL (Shankar et al., 2024) introduces an agent-based optimizer that rewrites and reorganizes document-processing steps to improve accuracy (DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing). It programmatically inserts additional LLM operations (e.g. split a long task into sub-tasks, or verify an extracted field) when a single-pass approach would likely miss details . These implementations highlight that agents can intervene at multiple points: performing OCR, splitting text, enriching chunks, and validating or adjusting the results, all to ensure the final chunks fed to an LLM are as useful and correct as possible.

Comparative Analysis of Agent-Based Strategies

Various agent designs have been explored for document digitization and chunking:

Rule-Based Agents: Early or simple pipelines use fixed rules or heuristics for chunking (e.g. “split every N tokens” or by paragraph boundaries). While fast, these lack adaptability. For instance, chunking solely by length can cut tables or semantic units improperly, and purely hierarchical splitting might ignore content heterogeneity (Advanced ingestion process powered by LLM parsing for RAG system). Rule-based splitting often serves as a baseline but struggles with complex documents (figures, multi-column text), leading to information loss or poor retrieval chunks.
Reinforcement Learning Agents: New research applies reinforcement learning to let agents learn optimal parsing strategies. An example is Matrix (2024), a framework where an LLM-based agent is trained via iterative exploration on business invoices (HERE). The agent tries sequences of extraction actions on documents and receives feedback, gradually improving its policy for reading and chunking. Matrix uses a self-refinement loop to adapt to document structure, yielding substantial accuracy gains: after training, its autonomous agent outperformed vanilla chain-of-thought prompting by 30–35% in extracting key fields . Notably, the agent learned to handle longer documents with lower latency as it refined its approach . This demonstrates how an RL-enhanced agent can optimize both effectiveness and efficiency in chunking through experience.
Self-Improving Autonomous Agents: Beyond formal RL, agents can self-improve by analyzing their own outputs and errors. DocETL’s agent falls in this category: it dynamically rewrites its plan if the current processing pipeline isn’t accurate enough (DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing). It employs an LLM “planner” agent to propose pipeline modifications (e.g. add a step to double-check a section), and a “validator” agent to test results, iterating until quality criteria are met . This self-correcting behavior, akin to the Reflexion technique, lets the agent autonomously enhance chunking and extraction without explicit human rules. Such agents bridge rule-based and learned approaches: they follow general heuristics (rewrite directives) but flexibly adjust to each document’s challenges.
Multi-Agent Collaboration: Instead of a single monolithic agent, some systems employ a team of specialized agents that collaborate. In multi-agent frameworks, one agent might handle vision (OCR or layout analysis), another focuses on text understanding, and others on summarization or verification. Perez et al. (2024) exemplify this with dedicated assembler, metadata-extraction, question-generation, and summarizer agents working in concert (Advanced ingestion process powered by LLM parsing for RAG system) . Similarly, multi-agent orchestration frameworks like Autogen (used by Zeeshan et al., 2025) enable distributed agents to pass messages and results via a broker (e.g. Kafka) for complex workflows (Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things). The benefit is modularity and specialization – each agent can use different models or techniques best suited to its sub-task. Collaboration can be synchronous (agents iterating together) or pipeline-staged as in a traditional workflow. Multi-agent systems naturally support dynamic task decomposition: complex documents can be broken down by one agent into pieces that others tackle (LLM-based Multi-Agent Systems: Techniques and Business Perspectives). The trade-off, however, is overhead in communication and coordination. Studies found that increasing agent count can raise latency, although it maintains high output quality due to the agents’ combined reasoning power .
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Practical Deployment Considerations

Scalability and Efficiency: Deploying agent-based digitization at scale requires careful design to avoid bottlenecks. Each agent invocation (especially if using large LLMs) adds compute cost. Researchers have addressed this by parallelizing steps and caching results. For example, chunking by page can be done in parallel, and only relevant chunks are processed further (HERE). DocETL explicitly optimizes for minimal agent calls by exploring rewrite plans top-down, pruning expensive branches early (DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing) . In practice, a balance is needed between thoroughness and speed. The Matrix system shows that after an agent learns a task, it can achieve lower latency and cost than non-adaptive methods (HERE), suggesting that initial training overhead can pay off in long-term efficiency for repetitive document types.

Latency vs. Quality Trade-offs: Multi-agent pipelines introduce latency from inter-agent communication and sequential dependencies. A recent multi-agent CEP (Complex Event Processing) pipeline demonstrated that adding more LLM agents to handle complexity increased end-to-end latency, though it improved the system’s ability to handle complex inputs (Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things). For document processing, this means there is a practical limit to how many agent stages can be added before the user experience suffers. Strategies to mitigate latency include using smaller specialized models for simple tasks (e.g. a lightweight OCR before an LLM agent double-checks critical text) and triggering agents only when needed (e.g. skip the summarizer agent if a section is already short). Some systems also use asynchronous agent execution or streaming of partial results to the LLM so that generation can begin before all chunks are finalized.

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Integration with LLM Pipelines: Agent-based digitization modules must integrate with retrieval and LLM inference pipelines. Typically, after chunking, the chunks are embedded into a vector store for retrieval or fed directly into an LLM prompt for answering. Ensuring compatibility with these downstream components is crucial. The ingestion framework by Perez et al. produces outputs in markdown with rich metadata, which can be easily indexed in a database and later reconstructed into a prompt with relevant context (Advanced ingestion process powered by LLM parsing for RAG system) . Modern tool orchestration libraries (LangChain, HuggingGPT variants, Autogen, etc.) provide abstractions to incorporate custom agents into LLM workflows. For instance, Autogen was used to connect multiple LLM agents with a pub-sub system, illustrating that agent pipelines can plug into existing infrastructure like message queues and microservices . Key integration considerations include format standardization (so that one agent’s output is another’s input) and error handling (if one agent fails, the system should fallback or recover gracefully).

Real-World Use Cases: The need for agentic document processing is driven by real-world applications. In enterprise settings, documents such as financial reports, legal contracts, and invoices are long, heterogeneous, and often scanned – a perfect storm of challenges for LLMs. Agents have been successfully applied here: the Matrix agent was trained on logistics invoices to extract shipment references, achieving high accuracy in a critical business task (HERE). RAG systems for corporate knowledge bases also benefit; a multi-agent RAG ingestion approach was evaluated on diverse document types (presentations, forms, dense PDFs) and showed improved answer relevancy on domain QA tasks . Moreover, benchmarks like OHRBench highlight how current OCR pipelines falter on complex PDFs from healthcare, finance, etc., leading to downstream QA errors (OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation). This underlines the necessity of more robust agent-driven pipelines. As of late 2024, open-source adoption of such techniques is growing – e.g. DocETL’s framework has been embraced by developers for tasks like police report analysis (DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing) – indicating practicality outside of research labs.

In summary, agents play an increasingly important role in document digitization and chunking for LLMs. Technically, they enable multi-step reasoning over documents: parsing layouts, extracting text, segmenting intelligently, and even verifying the chunks. Compared to one-shot or rule-based preprocessing, agent-based methods handle complexity and errors more gracefully, whether through learned policies or cooperative sub-agents. Different paradigms (from straightforward scripted agents to learning agents with memory) offer trade-offs in implementation complexity and adaptability. Finally, real-world deployments show that while agents add some overhead, they greatly boost the quality and reliability of LLM responses on long or scanned documents – often a worthwhile trade-off for enterprise and high-stakes applications (Advanced ingestion process powered by LLM parsing for RAG system) . As LLMs continue to be integrated into workflows, the consensus in recent literature is that intelligent agent frameworks are necessary to bridge the gap between raw documents and the limited yet powerful reasoning capacity of modern LLMs.