The Agentic Revolution: How AI is Reshaping Enterprise Document Management

The enterprise landscape is undergoing a profound transformation, driven by the rapid emergence of AI agents. These intelligent software programs represent far more than mere automation tools; they are autonomous entities capable of interacting with their environment, making informed decisions, and performing complex tasks without constant human intervention.¹ This paradigm shift promises to redefine how organizations manage, process, and extract value from their vast repositories of information.

At their core, AI agents are sophisticated software programs that leverage artificial intelligence techniques to interact with their environment, make informed decisions, and perform tasks autonomously.¹ They are foundational to advanced AI query engines, designed to gather information, plan, reason, and execute actions, significantly enhancing generative AI model inputs.² This heralds nothing less than a "seismic shift" in how enterprises approach automation and decision-making, with far-reaching implications for everything from customer interactions to core business operations.³

A critical aspect of this transformation is the ability of AI agents to handle unstructured data, which constitutes a staggering 80-90% of all global data. This vast reservoir includes diverse formats such as emails, social media posts, videos, audio recordings, and images.¹ A primary objective for AI agents when processing this data is to transform raw information into actionable insights, achieved through advanced capabilities like data preprocessing, feature extraction, pattern recognition, classification, summarization, and recommendation.¹ The widespread adoption of AI agents underscores their perceived necessity; an overwhelming 96% of organizations plan to expand their use in the next 12 months, with half aiming for significant, organization-wide deployment. This highlights 2025 as a pivotal year for AI agent adoption.³

This evolution is fundamentally transforming software document control by automating tasks and unlocking entirely new capabilities across the entire document lifecycle, from intelligent data extraction to sophisticated workflow routing.⁴ It is not merely about replacing paper with digital formats, as traditional document management systems have aimed to do. Instead, it is about maximizing the inherent potential within an organization's data, turning passive information into an active strategic asset.⁴ The sheer volume of unstructured data, coupled with AI agents' unique ability to process it, means that effectively managing this data is no longer just an efficiency goal but a strategic imperative. Organizations that fail to leverage AI for this purpose risk falling behind, as the ability to extract actionable intelligence from this vast resource becomes a core differentiator. This suggests a fundamental shift from merely storing data to actively transforming it into strategic intelligence as a core business imperative for survival and growth. Furthermore, this transformation is not just about automating existing tasks faster or more accurately; it is about fundamentally altering the strategic role of document management within the enterprise. It moves from a reactive model, where users search for information to solve immediate problems, to a proactive model, where the system itself surfaces insights and anticipates needs. This elevates document management from a mere administrative function to a strategic intelligence hub, directly informing and accelerating business decisions.

2. Beyond Keywords: The Evolution of Enterprise Search

For decades, enterprise search has been a frustrating exercise in keyword matching, often yielding overwhelming, irrelevant results or failing to find crucial information buried in disparate systems. The rise of AI agents is poised to fundamentally rewrite this narrative, moving enterprises from a world of siloed data and basic queries to one of intelligent, context-aware insight discovery.

Traditional document management systems frequently suffer from a severe lack of advanced search features, making the process of finding specific information painstakingly slow and cumbersome, relying on basic keyword searches.⁵ A major pain point is the disorganized nature of digital storage, with documents scattered across various cloud drives, shared folders, and individual desktops. Without a centralized system, employees waste significant time navigating cluttered file structures or sifting through emails.⁶ Even in a digital environment, employees frequently spend excessive time digging through countless folders or attempting to guess vague filenames to locate the information they need, severely impacting productivity.⁶

AI agents are fundamentally changing this inefficient paradigm. AI Query Engines are designed to connect AI applications, or AI agents, directly to enterprise data. They efficiently process, store, and retrieve vast volumes of data, significantly enhancing the inputs for generative AI models.² These engines are instrumental in unlocking intelligence from unstructured data, capable of processing structured, semi-structured, and unstructured formats alike—including PDFs, images, and video—regardless of where they are stored.² Crucially, AI agents dramatically improve search and retrieval capabilities by understanding the context and semantic meaning of queries, rather than just keywords. This allows them to provide far more accurate and relevant search results, even when documents use different terminology for the same concept.⁴ For example, if a user asks for "blueprints A and B for building XYZ," a semantic NLP system can return a file or image labeled "Unit B schematics" because it grasps the underlying meaning, not just exact keyword matches.⁷

Leading platforms like Google Agentspace exemplify this by connecting a wide array of work applications, such as Confluence, Google Drive, Jira, Microsoft SharePoint, ServiceNow, Box, OneDrive, and Dropbox. This connectivity enables Google-quality multimodal search and the power of AI agents to break down long-standing data silos across the enterprise.⁸ Similarly, AI-powered workplace search tools like Lindy integrate seamlessly with major cloud storage platforms including Google Drive, OneDrive, and Dropbox, offering comprehensive cross-platform search that intelligently connects diverse content types, from emails to spreadsheets.⁹ Beyond simple retrieval, AI agents empower users to search, summarize, and extract deep insights from files that were previously difficult to analyze, thereby unlocking entirely new ways to interact with enterprise knowledge.¹⁰ Upcoming solutions, such as Box AI Agents, promise "smarter search" capabilities, offering both "quick lookup" for instant contextual answers and "Deep Search" functionality to meticulously analyze hundreds of files for intricate queries. This is performed securely, leveraging permission-aware access and Retrieval-Augmented Generation (RAG) on internal content.¹¹

The power of "Deep Research" is transformative, enabling AI agents to turn entire folders of unstructured data into actionable insights. This capability is invaluable for analyzing large volumes of content to uncover trends or synthesize critical information.¹¹ For instance, AI agents can analyze QBRs, support tickets, and feedback forms to surface key drivers of customer churn, or comb through surveys to identify shifts in employee sentiment.¹¹ Generative AI technologies further enable users to interact with their industrial data using natural language, allowing for the summarization of vast amounts of information, accelerated analysis, and enhanced decision-making across the entire enterprise.¹⁰ This evolution goes far beyond simply finding files faster. It fundamentally democratizes access to institutional knowledge, making previously inaccessible or hard-to-connect information universally discoverable and actionable by any authorized employee. This capability has profound implications for fostering cross-functional collaboration, significantly accelerating decision-making processes, and reducing an organization's over-reliance on individual "knowledge keepers" who might be bottlenecks. It transforms fragmented data into a cohesive, searchable enterprise brain.

The evolution of enterprise search is not merely about locating a specific document or a list of relevant files. Instead, it is about the system's capacity to generate novel insights directly from the collective content of those documents. Rather than returning a traditional search result, the system can provide a synthesized answer, a comprehensive trend analysis, or a predictive report based on deep contextual understanding. This shifts the user's role from a "data hunter" who manually sifts through information to an "insight consumer" who receives pre-digested, actionable intelligence, thereby dramatically boosting productivity and strategic capacity across the organization.

The transformative impact of AI on document management is further illustrated by a comparative analysis of traditional versus AI-driven systems:

Table 1: Comparative Feature Analysis: Traditional DMS vs. AI-Driven DMS

3. From Scans to Intelligence: The Transformation of Document Processing

The traditional model of scanning documents, often seen as a standalone digitization step, is rapidly becoming obsolete. The future lies in Intelligent Document Processing (IDP), a comprehensive approach that transforms raw document inputs into structured, actionable data, fundamentally altering the requirements for document intake and processing services.

Intelligent Document Processing (IDP) is defined as the automation of manual data entry from paper-based documents or document images, with the goal of integrating this data seamlessly into other digital business processes.¹⁴ IDP moves far "beyond simple digitization." Unlike traditional scanning services that primarily convert physical documents into digital images, IDP immediately processes and understands the content within them. This means the output of an IDP system is not just a digital image, but structured, actionable data ready for use.¹³ A key differentiator is that while Optical Character Recognition (OCR) merely reads text, IDP leverages AI to interpret, classify, and even validate the extracted data. Furthermore, IDP continuously improves its accuracy through machine learning and feedback loops, adapting to various document types and structures, including unstructured data, with minimal human intervention.¹³

IDP employs a powerful combination of OCR and Natural Language Processing (NLP) to automatically identify and extract key data points from a wide array of document sources, including scanned documents, emails, PDFs, and even handwritten forms.¹³ It intelligently automates tasks such as data entry, document sorting, and categorization, performing these functions quickly and with high accuracy.⁴ The process begins with preprocessing and classification, where the system enhances document quality by removing noise and correcting distortions. Documents are then categorized based on predefined rules or advanced AI-driven classification models.¹³ By automating data entry and processing, IDP significantly reduces human errors. AI systems can achieve up to 99.9% accuracy in data extraction, consistently surpassing typical human performance.⁴ Platforms like Box AI Agents are tackling "Enhanced Data Extraction," transforming messy, unstructured data—such as scanned PDFs, images, and handwritten forms—into structured, usable insights. They automatically identify and extract high-value data points like key dates, financial totals, contractual obligations, and clause types.¹¹ Computer vision integration further enhances document image recognition, enabling IDP to effectively process complex layouts, scanned images, and signatures.⁷

The impact of IDP on operational efficiency and business agility is profound. IDP minimizes human errors commonly associated with manual data processing, ensuring cleaner and more reliable data.¹³ AI-powered algorithms are unmatched in their precision for document classification.¹² IDP automates document processing, which inherently helps ensure regulatory compliance by maintaining robust audit trails and significantly reducing the risk of misfiled or lost documents.¹³ For example, IDP can automate KYC (Know Your Customer) document verification and detect fraud in real-time, thereby ensuring adherence to financial regulations.¹³ IDP makes it considerably easier to handle large volumes of documents effortlessly, allowing organizations to adapt seamlessly to growing business needs without manual bottlenecks.¹³ This automation accelerates document processing times, approvals, and decision-making, significantly enhancing overall business agility.¹³ By minimizing manual labor and processing errors, IDP significantly cuts operational expenses associated with traditional document handling.¹³ It also eliminates the need for physical storage and reduces the labor costs associated with manual data entry.⁵

The value proposition of a pure "scanning service" is rapidly diminishing. The market demand is shifting from simply digitizing paper to intelligently extracting, validating, and integrating data from any source, whether physical or digital. This implies that traditional scanning providers must urgently evolve their offerings to become comprehensive IDP solution providers, integrating advanced AI capabilities, or risk becoming irrelevant in an increasingly intelligent document ecosystem. Furthermore, IDP is not merely an efficiency tool for document processing; it is a critical enabling technology for the success of all subsequent AI initiatives within an enterprise. By ensuring cleaner, more reliable, and structured data at the ingestion and initial processing points, IDP directly fuels the accuracy, relevance, and utility of AI agents for advanced search, analytics, automation, and decision-making. This establishes IDP as a foundational layer for any comprehensive AI-driven enterprise transformation, transforming data quality from an operational concern into a strategic asset that underpins all intelligent operations.

4. Are Your Current Document Systems Dated? A Reality Check

Many enterprises today find themselves wrestling with document management systems (DMS) that, while once cutting-edge, are now struggling to keep pace with the demands of the digital age. The question isn't if a current system is dated, but rather, how severely its limitations are impacting operational efficiency, risk posture, and ability to innovate. The rise of AI agents exposes these vulnerabilities, making the need for modernization undeniable.

Common pain points and inefficiencies in traditional Document Management Systems (DMS) are widespread. Disorganized digital storage is a pervasive issue, with digitized documents often scattered across various cloud drives, shared folders, and individual desktops. Without a centralized, intelligent system, employees waste significant time navigating cluttered file structures or searching through emails.⁶ Even in a digital environment, employees frequently spend excessive time digging through countless folders or attempting to guess vague filenames to find the information they need, severely impacting productivity.⁶ In collaborative environments, it is common for multiple versions of a document to exist in different places, leading to confusion, errors, and wasted effort.⁶

Traditional systems often fall short in ensuring compliance with industry regulations like GDPR, HIPAA, or SOX. They typically lack the robust features needed to enforce security protocols, manage retention policies, and create comprehensive audit trails, increasing the risk of non-compliance penalties.⁶ Without proper safeguards, digital files are vulnerable to unauthorized access, hacking, or accidental exposure. Traditional DMS often lack the robust security features like encryption, user access controls, and audit logs necessary to protect sensitive information.⁶ Manually managing digital files and processes leads to delays, errors, and frustration. Traditional systems often rely on manual routing, approvals, and notifications, creating bottlenecks.⁶ Businesses often incur unnecessary costs for extra cloud storage due to disorganized, redundant, or unnecessarily large files, as traditional systems struggle to identify duplicates or automate archiving.⁶ Even with digital forms, manual data entry can lead to mistakes, inconsistencies, or missing information, compromising data accuracy.⁶ Ultimately, decision-making suffers significantly when key documents are hard to locate or workflows are delayed, hindering organizational agility.⁶

Beyond operational issues, the adoption of new DMS solutions presents its own set of challenges. Implementation is often not a plug-and-play process; it requires careful planning, customization, and often significant changes to existing workflows, which can overwhelm organizations attempting self-implementation.¹⁵ Achieving seamless integration with other critical enterprise software like Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, and email clients is frequently not straightforward. Compatibility issues, data silos, and the need for custom development can fragment workflows and lead to duplicate data entry.¹⁵ Many older systems use proprietary software, making AI agent integration difficult without substantial modifications.¹⁶ Finally, organizations frequently underestimate the time and expertise needed for continuous maintenance and support post-implementation, leading to performance issues, downtime, and security vulnerabilities without proper vendor support.¹⁵

The imperative for AI integration to maintain competitiveness and unlock new value is clear. AI agents are increasingly viewed as "essential to their business competitiveness" by enterprises.³ The market is moving rapidly: an overwhelming 96% of respondents plan to expand their use of AI agents in the next 12 months, with half aiming for significant, organization-wide expansion.³ A strong majority, 83% of organizations, believe it is critical to invest in AI agents to maintain a competitive edge within their industry.³ AI-driven DMS offers significant advancements over traditional systems, including intelligent routing for workflows, extensive APIs for broader integration, and context-aware search that moves beyond basic keyword matching.⁴ Beyond efficiency, AI solutions help businesses cut costs, mitigate risks, and uncover entirely new opportunities hidden within their existing data.⁴

Despite the clear benefits, integrating AI agents with existing, potentially legacy, systems presents its own set of challenges. A primary hurdle is that legacy systems are often built on outdated architectures that inherently lack compatibility with modern AI technologies, requiring substantial modifications.¹⁶ Data stored in silos within older systems is difficult to access and consolidate, which can lead to inconsistent or poor-quality data. This directly limits the effectiveness and accuracy of AI-driven decisions.¹⁶ Many legacy systems were simply not designed to handle the high computational demands of complex AI algorithms, potentially requiring significant hardware upgrades or migration to cloud-based solutions.¹⁶ Integrating AI agents introduces new security challenges, especially when dealing with sensitive data, as legacy systems often lack the robust security measures needed to protect against modern cyber threats.¹⁶ Connecting diverse systems, each with its own architecture, protocols, and quirks, is inherently complex. This involves dealing with various API protocols, authentication mechanisms, and data formats, requiring significant and specialized engineering effort.¹⁹ Handling high volumes of data ingestion for Retrieval-Augmented Generation (RAG), processing numerous concurrent user requests, and making frequent API calls for tool execution places significant load on both the AI agent's infrastructure and the connected legacy systems.¹⁹ Both AI models and the external APIs they interact with are constantly evolving. A new version of an LLM or an API update can break previously functional integrations if not managed proactively.¹⁹ Finally, employees accustomed to traditional workflows may view AI as a threat, necessitating effective change management strategies.¹⁶

The existing inefficiencies and architectural rigidities of older DMS are not merely minor operational inconveniences; they represent significant "technical debt" that directly impedes the successful deployment and value realization of AI agents. This means that for many organizations, the journey to AI-powered document management cannot simply involve "bolting on" AI. Instead, it often necessitates a foundational modernization or even an overhaul of the underlying DMS infrastructure to ensure data readiness and system compatibility. Ignoring this debt will severely limit AI's potential. Furthermore, the rapid, industry-wide shift towards AI agents means that enterprises delaying their AI agent integration and continuing to rely on dated systems risk falling significantly behind their competitors. This competitive chasm will manifest in higher operational costs, lower efficiency, and a diminished ability to derive critical insights from their data. The "dated" status of current systems is therefore not just an internal inefficiency but a direct threat to market position and long-term viability, creating a strong imperative for immediate strategic action rather than cautious observation.

5. The Human-AI Partnership: Augmenting, Not Replacing

As AI agents become increasingly sophisticated, a crucial question arises: what becomes of the human element in document management? The answer is clear: AI is not here to replace human expertise but to profoundly augment it. This shift necessitates a redefinition of roles, emphasizing human judgment, ethical oversight, and strategic decision-making in an increasingly intelligent ecosystem.

A fundamental principle is that AI is designed to "augment your expertise, not replace it".⁷ AI's primary benefit is to remove the burden of repetitive, low-value tasks from human workers—such as endlessly searching for files, manually tagging documents, or cross-checking multiple file versions. This liberation of time allows employees to focus on higher-level activities that truly require human innovation and strategic decision-making.⁷ Think of AI as an intelligent assistant, handling the heavy lifting and streamlining workflows, thereby freeing up valuable human capital.¹² By automating cumbersome and repetitive tasks, AI frees up significant time for employees to concentrate on work that demands uniquely human attributes like judgment, creativity, and complex problem-solving.¹² This transformation will naturally lead to the emergence of new, higher-value roles within organizations, specifically focused on AI oversight, advanced risk assessment, ethical governance, and strategic planning related to AI deployments.²⁰

While AI excels at processing vast amounts of data, human insight, experience, and intuition remain absolutely critical, especially in complex, ambiguous, or high-stakes situations where nuance and ethical considerations are paramount.²⁰ AI excels at processing data but often falls short in understanding the full context behind that data, including nuanced cultural factors, complex business ethics, or unique situational variables that AI simply cannot grasp. Human professionals, with their deeper understanding of these dynamics, are essential for assessing such situations more effectively and making truly informed decisions that transcend raw data.²⁰ AI operates based on data and algorithms, but it lacks the inherent ability to navigate complex ethical dilemmas in the way humans can. In compliance and document management, ethical considerations are often central to decision-making—whether it is ensuring fairness, actively avoiding bias in AI models, or handling sensitive situations where the "right" course of action is not black and white. Human judgment is indispensable for weighing the ethical implications of decisions, especially concerning fairness, privacy, and mitigating potential harm from unintended AI-driven outcomes.⁵ AI is highly capable of processing information and identifying patterns, but it lacks the experience, intuition, and foresight that human professionals bring to the table. In high-stakes or ambiguous situations, compliance and document management professionals rely on their deep understanding of regulations, their accumulated experience from similar cases, and their ability to think strategically. These situations demand a level of foresight and adaptability that AI simply does not possess.²⁰ Not every document management issue or compliance scenario fits neatly into predefined categories or rule sets. AI tends to struggle with "edge cases"—those unique, non-standard scenarios that fall outside of typical patterns or involve highly complex variables. These cases critically require professional human judgment and the ability to interpret the finer details of a situation.²⁰ While AI can be enabled to automate approvals for "small and low risk changes against a criteria," the evidence explicitly states that "there are still many aspects to the approval process that require human oversight." This is particularly true for high-risk or complex changes that extend beyond predefined automated criteria.⁷ Just like with junior team members, auditors and other professionals must conduct thorough quality checks on the work performed by AI agents to validate and monitor their outputs, ensuring accuracy and adherence to standards.¹⁷

The importance of AI governance, training, and a "human-in-the-loop" approach cannot be overstated. AI governance is not optional; it is essential for setting up frameworks, policies, and practices to ensure that AI is developed, deployed, and managed responsibly and ethically. It directly addresses key challenges such as bias, transparency, accountability, and regulatory compliance, effectively serving as an extension of existing data governance efforts.²¹ "Human-in-the-loop" models are crucial for maintaining human oversight and control. They ensure that professionals remain involved at critical decision points, with AI automating initial tasks but humans intervening for final reviews, thereby ensuring compliance with both ethical and regulatory standards.²⁰ Organizations must invest significantly in training programs to help employees understand AI functionalities, reduce potential resistance to new technologies, and maximize their productivity in an AI-augmented environment. Developing AI literacy across the workforce will be crucial as adoption increases.¹⁶ Actively encouraging collaboration between AI systems and human workers is key to enhancing overall efficiency and unlocking the full potential of these combined capabilities.¹⁶

The human role in enterprise document management is undergoing a significant elevation. It is moving away from operational execution and towards strategic oversight, ethical stewardship, and critical thinking. This is not merely about task automation; it is about fundamentally reshaping job functions and organizational structures. This transformation necessitates a substantial investment in upskilling the workforce in areas like AI literacy, critical thinking, and ethical frameworks, thereby profoundly altering the talent landscape within enterprises. Beyond mere regulatory compliance, building and deploying ethical AI in document management is becoming a strategic imperative. Failure to proactively address issues like algorithmic bias, ensure transparency in AI decision-making, and robustly protect sensitive data can lead to severe reputational damage, significant legal liabilities, and a profound erosion of customer and employee trust. Conversely, organizations that demonstrably implement an ethical AI approach can transform it into a powerful competitive advantage, attracting top talent and customers who prioritize responsible technology use. This underscores the critical need for robust AI governance frameworks and continuous human oversight throughout the AI lifecycle.

6. The Path Forward: Embracing the Future of Document Management

The agentic revolution is not a distant possibility; it is the present reality shaping the future of enterprise document and records management. For organizations to thrive in this evolving landscape, a proactive and strategic embrace of AI-driven solutions is no longer optional—it is a competitive imperative.

The rise of AI agents is a tangible force that is already fundamentally transforming enterprise operations and decision-making.³ The ability to efficiently process, understand, and derive actionable insights from the vast amounts of unstructured data (which constitutes the majority of enterprise information) is now paramount for maintaining a competitive advantage.¹ Enterprise search is undergoing a profound evolution, moving far beyond simplistic keyword matching to sophisticated, context-aware, and semantic intelligence that can seamlessly span across all previously siloed data sources.⁴ Traditional scanning services are being rapidly superseded by Intelligent Document Processing (IDP), which automates the entire document lifecycle, from initial ingestion and classification to intelligent data extraction and validation, transforming raw inputs into actionable data.¹³ Current, traditional Document Management Systems (DMS), if not actively augmented and modernized with AI capabilities, will increasingly struggle to meet the demands for efficiency, deep insight generation, and robust compliance in the modern business environment.⁶ The human role within this evolving ecosystem is shifting dramatically, moving away from manual, repetitive tasks towards strategic oversight, nuanced ethical judgment, and critical decision-making, allowing human capital to be deployed where it adds the most value.⁷

For successful AI integration, enterprises must adopt a multi-faceted approach. This includes investing in robust enterprise AI infrastructure platforms to develop and deploy AI agents effectively.³ Organizations should also explore open-source Large Language Models (LLMs) for their cost-effectiveness and deployment flexibility, which can help avoid vendor lock-in and address data sovereignty concerns.³ It is critical to prioritize data readiness and integration, addressing data quality issues, breaking down silos, and planning for seamless integration with existing legacy systems through strategies like system audits, middleware solutions, and standardized APIs.¹⁶ Establishing robust AI governance is paramount, emphasizing data privacy, security, and compliance from the outset. This involves implementing encryption, access controls, audit logs, and responsible AI tools to build trust and mitigate risks.⁵ Fostering human-AI collaboration is essential, achieved through comprehensive employee training programs and the adoption of "human-in-the-loop" models, which ensure human judgment and oversight remain central.¹⁶ Finally, organizations must continuously monitor emerging AI solutions and adapt their strategies, as the AI landscape is rapidly evolving.¹⁷ Proactively integrating AI into document management functions will yield significant efficiencies, enhance risk monitoring capabilities, and crucially, free up valuable human time for more strategic analysis and innovation.¹⁷ The question is no longer if AI will transform document management, but how soon an organization will make the leap.¹⁷

The adoption of AI in document management is not a static, one-time project but the initiation of a dynamic, continuous improvement loop. As more data is processed, more interactions occur, and more insights are generated, the underlying AI models refine and enhance themselves. This leads to progressively more accurate insights, increasingly efficient automation, and a continually improving user experience. This "flywheel" effect suggests that early adopters of AI-driven document management will gain compounding advantages over time, making the initial investment even more critical for long-term competitive differentiation. The ultimate strategic goal of AI-powered document management extends beyond merely managing individual documents or even disparate repositories. It is to construct a comprehensive, interconnected "brain" for the entire organization—an enterprise knowledge graph. This architectural shift moves beyond isolated document management systems to a unified, intelligent knowledge fabric that links disparate data points, systems, and content modalities. This enables holistic, context-rich intelligence and represents a critical strategic investment for organizations aiming to truly unlock the full potential of their information assets with AI.

The key benefits of AI agents in document management are summarized below:

Table 2: Key Benefits of AI Agents in Document Management