Why agentic AI document processing is the future of IDP

"Applying AI agents to simple tasks is like hiring a Michelin-star chef to microwave leftovers" – this was an argument by a major intelligent document processing (IDP) vendor to suggest large language models (LLMs) don't belong in document automation software. It's a clever analogy, but one that elicits a compelling counterargument. If the chef only costs you a few cents per meal, why wouldn't you hire them?

Agentic AI document processing is the newest development in IDP, and it’s already changing how teams handle document-heavy work. Inference costs have dropped so dramatically that using powerful LLMs for routine document tasks isn't wasteful – it's practical. 

The economics have fundamentally shifted, but many IDP providers haven't yet embedded AI agents for document processing within their platforms. While they’re still busy raising concerns about hallucinations and defending architectures that are rapidly becoming legacy systems, their competitors (like us) have moved ahead. Understanding the evolution of intelligent document processing – from rules to machine learning to agentic systems – reveals who's adapted and who's stood still. 

Inside the three eras of the document processing evolution

Document processing has evolved through three distinct phases over the years.

Era 1: template IDP (aka, The rules era, mid-1990s–2000s)

It started with template IDP (aka, the rules era), where we needed to tell the technology, in very clear and explicit terms, where to look for the data we wanted it to extract and process. We defined anchors, set up regex patterns and mapped out fixed zones on the page. Template IDP systems assumed that if ‘Vendor A's’ invoice looked a certain way last month, it would look the same this month. As such, they leveraged optical character recognition (OCR) to ‘read’ the text, extracting the characters into the system. The software would then parse the layout and execute your previously defined ‘rules’. It would then spit out a ‘Confidence score’ based on simple heuristics about how well the rule could be applied. In other words, was there indeed a date on the page where it expected one to be?  

The trouble started when template IDP hit its ceiling of scalability due to layout drifts. Layout drifts are a constant problem in document processing because there is no standard or consistency in how vendors, banks, financial institutions, identification providers or other organizations produce their documents. One company’s invoice may have a date field on the top right, the other may have it on the top left. One customs agent may have the delivery address on the bill of lading at the bottom left, the other at the bottom right. 

Template IDP simply couldn’t handle that level of variation automatically. Instead, every time there was a change, your team would need to create and input another rule. Before long, you were drowning in rule debt of maintaining hundreds, if not thousands, of hand-written extraction rules and templates that grew faster than your teams could manage. These costly and slow template IDP systems struggled with messy scans, had difficulty with complex cross-page logic and added a heavy ‘rules and testing’ toll to the very teams who were meant to benefit from increased efficiency.

Era 2: machine learning IDP (aka, The transformer era, 2010s–early 2020s)

The advent of machine learning (ML) opened up a new wave of possibilities in IDP. The machine learning IDP era (aka, the transformer era) began with earlier ML models and then shifted to transformers – a newer type of neural network that can be fine-tuned for specific document tasks and deeper language understanding. Instead of telling the system where to look, the new transformer models learned visual and linguistic patterns across different documents and layouts to understand how to classify each word. Post-processing logic then assembled these labeled words into the final extracted values. These ML IDP systems handled noisy scans better, were easier to work in multiple languages and needed far fewer brittle rules than the template IDP approach.  

Once again, though, the possibilities of ML IDP hit a scalability ceiling due to the extensive operational weight, with each use case needing its own fine-tuned model. And, when users made corrections, the model didn’t improve immediately, resulting in a slower feedback loop. 

Now, we’re speaking in the past tense here – but the truth is, the vast majority of IDP solutions available right now are still machine learning IDP systems. Modern generative AI and LLMs have been at our fingertips for over three years now, and yet many IDP providers haven’t integrated AI beyond adding token AI features. This is why you need to do your research when choosing an IDP provider.

Era 3: agentic IDP (aka, The generative era, 2022–present)

The latest evolution in IDP is agentic AI document processing (aka, the generative era). Today’s developments see us move the data extraction from happening before the ‘processing’, to embedding it directly within by reimagining the whole process inside a generative model. Instead of creating templates that define where the system can find a data field, or generating rules to point the system to them, agentic AI document processing uses AI agents to locate the data fields and then verify the outputs against the source document and business rules to produce decision-ready data. 

What agentic AI document processing really is

Agentic AI document processing can deliver exceptional results from only one model, which can adapt to new document types and fields with just a few examples when supported by specialized tools where needed. Accounting for edge cases or new formats can often be as simple as adjusting context through natural language instruction – meaning no long training cycles, annotation sprints or rigid templates to update. 

Agentic AI document processing systems can leverage the best of existing IDP technologies – such as optical character recognition (OCR), intelligent character recognition (ICR) and integrations with robotic process automation (RPA) – then layer in the latest advances in retrieval augmented generation (RAG), LLMs and AI – to deliver accurate extraction, retrieval, classification and validation of decision-ready data. 

Together, this agentic architecture and its underlying technologies facilitate seamless straight-through processing and integration to downstream systems. In well-designed IDP platforms, every interaction – human and tech – instantly updates the agents’ instructions and model memory, improving the next document processing workflow output without the need for significant human input. For example, this agentic AI approach to document processing asks, "What is the invoice number?" and then grounds that answer by linking back to exact regions in the document and cross-checking against your business’ rules and logic. 

How agentic IDP keeps hallucinations contained

One critical job that these document processing AI agents tackle is reducing and containing hallucinations. Well-designed agentic IDP platforms require each extracted value to carry ‘provenance’ – a traceable link or citation trial detailing where a data point came from in the source document and how it was transformed. Schema-aware validators also check the accuracy of each data point extracted and processed. 

This means when evidence of accuracy is insufficient, the document processing AI agent re-reads it and retrieves more context or escalates that data point for human-in-the-loop review. These controls make it much harder for unverified, non-decision-ready outputs to slip into production unnoticed while forcing low-confidence cases into review and are a key reason why agentic AI document processing solutions can achieve consistent accuracy scores of 99%+.

Agentic AI document processing delivers outstanding speed and coverage for organizations with document-heavy workflows. Standing up a new document type or adding fields for processing takes minutes with the right agentic AI platform, instead of weeks of model training. Automating document processing using AI agents also empowers IDP platforms to handle messy PDFs, unfamiliar document formats and even images with scribbled handwriting with ease. They can also reason across multiple documents to reconcile seemingly disparate document types with one another.  

That constant ability to learn from new documents and edge cases is exactly what makes agentic AI document processing the living, evolving era of IDP in production today.

Constant evolution: why agentic AI document processing is the future

The biggest problem with the rules and transformer eras was the scalability ceiling each system and solution eventually hit. 

With template IDP in the rules era, the scalability ceiling was document variability. Those IDP systems could only handle variability with significant manual reworking of the templates. Scalability was technically possible, but it required human effort and resources to the volume that effectively voided any efficiency benefit, thanks to the rule debt it created.

With machine learning IDP in the transformer era, the scalability ceiling was model fine-tuning. Again, scalability was technically possible, but the ongoing human effort still raised the question of whether the benefit outweighed the cost in time and resources to maintain, let alone optimize. 

The reason agentic AI document processing – the generative era – is the future of intelligent document processing is that its scalability ceiling is much higher: instead of hitting limits on templates or fleets of fine-tuned models, scaling is mainly an engineering problem of infrastructure, governance and retrieval quality. Even in the very short time that AI agents have been used in document processing, the technology has evolved consistently through distinct stages of sophistication, each unlocking new capabilities.

The three stages of agentic AI maturity

Agentic AI evolution 1: prompt-led systems 

The new era started with prompt-led systems where the technology would bundle field-level instructions into a large prompt, send the prompt and the document to the LLM, and then get back structured data. 

It was quick to start because it needed no training or labeling. Non-technical teams could even edit the desired behavior in the prompt. It worked well enough for stable document types with predictable schemas. However, it failed to trigger iterative system learning based on corrections or inputs. 

Mistakes corrected on one document wouldn’t flow through to the next unless the user manually edited the prompt. But those manual prompt edits then triggered issues that meant users needed to spend time A/B testing different prompt wordings, rather than improving the overall system. 

A giveaway that the IDP solution you’re using (or trialling) is stuck in this evolution is if your release notes are mostly "updated prompt for field X" and your team spends more time tweaking instruction wording than resolving the root causes of errors.

Agentic AI evolution 2: memory-augmented systems 

The next evolution we’ve already seen is toward memory-augmented systems. These agentic AI document processing solutions are much better at learning from corrections and interactions. For example, when a user corrects an extraction or marks one as successful, the system captures that as a structured memory – essentially a mini case study of what was right or wrong, and most importantly, why. Then, when they process a new document, a retrieval pulls the most relevant memories and automatically adds them to the LLM's context. 

This changes the dynamic completely. One correction now influences the next similar document immediately, without human involvement. Different layouts trigger different memories, so the agents adapt to each supplier automatically. 

The economics of agentic AI document processing improved with this evolution too. Instead of hosting fleets of fine-tuned models, you're managing context engineering – the memories, examples and instructions that shape behavior. It’s easier and cheaper to scale at this level. The potential scalability ceilings with these systems are manageable engineering problems, not fundamental limitations. All you need is good retrieval quality, observability, governance and a commitment to solving for memory drift, or old corrections that become stale.  

Once you’ve nailed model memory, governance and economics, the next frontier is AI agent document processing that can use tools to drive the entire workflow end to end.

Agentic AI evolution 3: tool-orchestrated systems

The latest evolution we’re seeing is a shift toward document processing AI agents that don’t just read documents – they complete the entire job. The AI agent coordinates the LLM with a suite of tools to handle what a human would do from start to finish. Critically, these AI agents go beyond simply flagging exceptions for human review. Instead, they take proactive steps to resolve issues autonomously. 

For example, when a new supplier invoice arrives with no vendor match, the agent can create the vendor record directly in the integrated ERP system (if policy allows). If human-in-the-loop judgment is needed, it doesn't just queue a task – it might send a Slack message to the procurement manager with invoice details and a quick approve/reject prompt. Or, in more advanced setups, it may even initiate a voice call to walk through the discrepancy and capture the decision in real-time. 

These tool-orchestrated AI agent systems help facilitate the right action at the right time, whether that's an autonomous execution or reaching out through the channels humans actually use.

It’s time to take advantage of the future

Just as template IDP systems once felt like magic compared to manual entry, agentic AI document processing systems will soon feel like the only sensible way to process documents at scale. 

The winners in this space will be those who treat LLMs as the core engine, not a bolt-on feature. Especially those who design for provenance, control and rapid adaptation from day one, giving customers faster onboarding, higher straight-through processing and workflows that evolve as their business changes. 

For everyone else, the question will not be "should we move to agentic IDP?" but "why did we wait so long?"

To take advantage of the future of IDP and turn documents into decision-ready data, leverage the free trial of Affinda’s agentic AI document processing solution. In minutes, you’ll see the positive impact agentic AI can have on your document processing workflows.