
Agentic AI: Your digital workforce for business acceleration
Manual tasks shouldn't limit your operational scale or potentially trigger compliance risks. Built for complex enterprise environments, Iron Mountain InSight® DXP deploys AI agents to help automate document-intensive workflows for businesses and unlock contextual insights from your data.
By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.

Accuracy is based on internal AI and human-verified benchmarks. Results vary by document quality, format, and setup.
Frequently asked questions
How does Iron Mountain provide security and governance of enterprise data within its AI platform?
Iron Mountain’s InSight DXP platform leverages 75 years of information protection expertise to provide enterprise-grade AI governance.
- Ring-fenced security: Your enterprise data is isolated within a secure perimeter and not used to train public models. We utilize Retrieval-Augmented Generation (RAG) for factual accuracy, tethering all AI outputs to your verified documents with clickable source citations.
- Automated workflows: Our platform handles complex, multi-step processes—from document classification and redaction to exception handling—under automated supervision. For highly regulated industries, we pair this automation with human-in-the-loop validation to maintain a ~99% accuracy rate.
- Unified trust & accountability: The platform enables you to apply consistent legal holds, retention schedules, and destruction policies across both physical and digital assets. Every agent action is logged in an audit trail, and access remains strictly governed by your existing enterprise security hierarchies (RBAC).
How does the AI workflow automation software work?
InSight DXP’s workflow automation software acts as an intelligent data transformation layer. It starts by ingesting unstructured data (such as emails, PDFs, audio, video, or scanned physical files) and uses built-in Intelligent Document Processing (IDP) to split, classify, and extract critical metadata. Once transformed into a structured, reliable format, this data is routed through automated logic pathways where a coordinated blend of AI agents, human reviewers, and API connections execute multi-step business processes, significantly reducing the need for manual data cleanup or application-hopping.
What makes agentic AI different from traditional, rules-based workflow automation?
Traditional rules-based automation relies on rigid "if/then" statements and strict templates; if a document format changes or a data type is irregular, the workflow breaks. Agentic AI is context-aware and goal-oriented. Instead of following a fixed script, our AI agents use large language models (LLMs) and advanced machine learning to analyze the layout, language, and context of an unstructured document. They can identify anomalies, handle complex multi-page tables or handwriting, and collaborate sequentially as a team to address variations in a workflow that often interrupt traditional software.
What key business processes do the AI agents help automate in InSight DXP?
InSight DXP features ready-made, pre-packaged industry suites alongside custom low-code tools to help automate high-volume, document-intensive operations:
- Digital Mailroom: Automating return-mail handling, document sorting, and routing physical checks directly into operational queues.
- Invoice Processing: Matching global e-invoices, purchase orders, and receipts directly against core accounting ledgers.
- Digital HR: Automatically updating workforce data matching in employee platforms while tracking compliance document exceptions.
- Trade Finance & Commercial Lending: Accelerating transaction verification and portfolio document splitting for global banking institutions.
How does the AI agent learn to improve its accuracy over time?
The platform is designed to maintain high precision through a continuous, closed feedback loop powered by a Human-in-the-Loop (HITL) agent. During document processing, the platform calculates a composite confidence score for extracted data points. If a field falls below your predefined threshold due to an anomaly, an unreadable font, or a signature discrepancy, the agent flags it and routes it directly to a human operator for validation. Every time a human corrects or validates a field, the machine learning models ingest that specific training annotation, adaptively refining customer-specific algorithms to support improvements in extraction accuracy over time.
How is InSight DXP’s agentic AI different from traditional agentic AI?
| Feature / Capability | Traditional AI | InSight DXP Agentic AI |
|---|---|---|
| Primary Objective | Data Extraction: Focuses on extracting key-value pairs and structured fields from documents based on rigid templates. | Holistic Automation: Focuses on end-to-end document automation. Agents understand context, reason over data, and act by triggering workflows, while operating under established governance, compliance, and human-in-the-loop guardrails. |
| Workflow Execution & Orchestration | Static & Sequential: Workflows follow pre-defined, linear rules. If an exception occurs, the process halts and waits for human intervention. | Dynamic & Orchestrated: Uses a multi-agent runtime with self-reflection and planning loops. Agents can generate explicit execution blueprints that users can review, edit, or approve before execution. |
| Decision Making & Reasoning | Rules-Based & Isolated: Relies on hard-coded logic and deterministic rules. Cannot easily analyze context across multiple documents. | Multi-Document Reasoning: Agents evaluate conflicting data, analyze context across a cluster of documents, and execute complex, multi-step logic. |
| Learning & Adaptability | Manual Retraining Required: Data scientists or IT teams must manually collect new sample documents, label data, and retrain models when document layouts change. | Continuous & Self-Healing: A background HITL Feedback Agent continuously observes human corrections in real-time, detects error patterns, and proactively suggests new rules or prompt updates without engineering involvement. |
| Governance & Oversight | Reactive QA: Relies on manual quality control after data is extracted. Security is generally limited to basic role-based access. | Bounded Autonomy & Tracing: Agents operate with infrastructure-enforced guardrails (Plan -> Review -> Execute -> Observe -> Audit). Every agent's "thought process", tool usage, and execution cost is recorded in a tamper-proof audit history with rollback mechanisms. |

