Data Management Glossary
Agentic AI
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously perceive their environment, plan multi-step tasks, make decisions, and take actions to complete goals without requiring human intervention at each step. Unlike conversational AI, which responds to a single prompt and waits for the next input, agentic AI operates in a continuous loop: it receives a goal, breaks it into subtasks, retrieves the information it needs from available tools and data sources, executes actions, evaluates the results, and adjusts its approach until the goal is achieved.
Agentic AI systems are typically built on large language models extended with tool use, memory, and planning capabilities. They can call external APIs, query databases, access file systems, run code, send communications, and interact with other AI agents as part of a coordinated workflow. This ability to act autonomously across multiple systems and data sources is what distinguishes agentic AI from earlier AI assistants that could answer questions but could not independently execute tasks.
Gartner predicts that by 2028, one-third of enterprise storage administration and support tasks will be governed by SLA-based outcomes and agentic AI automated platforms, up from less than 5% in 2025. Gartner also notes that by 2028, the proliferation of agentic AI with multimillion token context windows will render traditional enterprise storage systems inefficient unless they optimize for AI inference requirements.
Source: Gartner SLA as Code report, January 2026 and Agentic AI Storage Blueprint, May 2026 (subscription required)
What is the difference between agentic AI and AI agents?
AI agents are individual software programs with defined capabilities, such as a customer service bot or an IT operations assistant. Agentic AI is the broader architectural pattern in which one or more AI agents operate autonomously, orchestrate other agents, and complete complex multi-step workflows without human intervention at each decision point.
An AI agent is a component. Agentic AI is the system and the operating model. In an enterprise context, agentic AI typically involves a coordinating agent that receives a high-level goal, decomposes it into subtasks, delegates to specialized agents, manages tool calls and data retrieval, and synthesizes results into a final output. The individual agents handle discrete functions but the agentic AI system manages the overall workflow.
Why does agentic AI depend so heavily on unstructured enterprise data?
Agentic AI systems derive their business value from access to enterprise-specific context. A generic AI model trained on public data can answer general questions, but an enterprise AI agent that can complete real business tasks, such as preparing a research summary, identifying the right contract terms, or routing a clinical data request, needs access to the organization’s own unstructured data: documents, file shares, research archives, imaging data, project records, and operational logs.
With 80-90% of enterprise data unstructured according to Gartner, and most of it distributed across multi-vendor NAS and cloud storage environments, the challenge is not whether the data exists but whether AI agents can find it, access it, verify that it is authorized, and retrieve it in a form that is immediately usable. Most enterprise unstructured data estates were not built with agentic AI access in mind. Files lack consistent metadata, cold data is mixed with active working data, sensitive content is not classified, and there is no unified index across storage silos.
Without an intelligent data management layer underneath it, an agentic AI system is forced to work with whatever data it can find rather than with the right data. This leads to hallucinations, compliance violations from accessing unauthorized content, degraded output quality, and inflated inferencing costs from processing irrelevant files alongside relevant ones.
What data infrastructure does agentic AI require?
Gartner’s May 2026 report on agentic AI storage infrastructure identifies five core capabilities that storage platforms must provide to support agentic AI workloads at enterprise scale. Of these, Integrated Data Intelligence Services is particularly relevant to unstructured data management. Gartner states that storage platforms must provide automated metadata tagging, real-time visibility, federation across silos, and the ability to ensure data is searchable and relevant to AI agents immediately upon ingestion.
For enterprise unstructured data, this requires a platform that continuously indexes all file and object data across hybrid storage environments, captures and enriches metadata including custom business context, makes data searchable by any metadata or tag criteria, enforces governance and access controls so agents only retrieve authorized data, and delivers data to agents in native format without proprietary wrapping or rehydration overhead.
Komprise Intelligent Data Management provides exactly this layer. The Global Metadatabase continuously indexes all unstructured file and object data across multi-vendor NAS and cloud storage, capturing system metadata and custom tags enriched by KAPPA data services. Komprise Deep Analytics enables agents to query this index using any combination of metadata and tag criteria to locate precisely the data needed for a specific task. Komprise Smart Data Workflows deliver the retrieved data to AI platforms in native format, with sensitive data detected and excluded before any data reaches an agent. All access and delivery activity is tracked in the Global Metadatabase for a complete audit trail.
How does Komprise support governed agentic AI data access?
Agentic AI introduces a specific governance challenge that does not exist in supervised AI deployments: agents make autonomous data retrieval decisions that a human may never review. An agent that retrieves sensitive IP, regulated healthcare records, or confidential financial data without authorization creates compliance risk that is difficult to remediate after the fact, because the data has already entered the agent’s context window and influenced its output.
Komprise addresses this through governance that is built into the data access layer rather than applied after retrieval. Access controls provisioned through Active Directory group membership restrict what data each agent profile can query from the Global Metadatabase. Deep Analytics queries respect these boundaries, so an agent can only discover and retrieve data within its authorized scope. Komprise Smart Data Workflows include a sensitive data detection processor that identifies PII and content matching regex-based classification patterns before any data is delivered to an agent, automatically excluding files that should not be in scope. KAPPA data services can apply masking and transformation to sensitive fields within files, enabling agents to work with the non-sensitive portions of a document while protected content is withheld.
All agent data access activity is logged in the Global Metadatabase with full metadata about what was accessed, when, under what policy, and by which agent identity. This provides the immutable data lineage and sovereign governance controls that Gartner identifies as mandatory for agentic AI infrastructure, and gives compliance and security teams the audit trail needed to demonstrate that AI agents operated within defined governance boundaries.
How do KAPPA data services support agentic AI use cases?
KAPPA data services give agentic AI systems on-demand access to custom metadata enrichment that makes enterprise unstructured data actionable in ways that standard file system metadata cannot support. Standard metadata tells an agent that a file exists, who owns it, and when it was created. KAPPA-enriched metadata tells the agent what the file contains, which project or matter it belongs to, which clinical parameters are associated with it, or which business identifier connects it to other relevant files.
KAPPA functions are short Python-based operations that Komprise executes at petabyte scale using serverless compute, without requiring any infrastructure provisioning. Examples relevant to agentic AI workflows include extracting reservation or booking identifiers to help a customer service agent find all files related to a specific transaction, pulling project codes from ERP or Salesforce to help a research agent locate prior work, extracting clinical metadata from DICOM headers to help a healthcare agent curate imaging data for a specific study, and synchronizing MS Purview sensitivity labels to ensure agents have current classification state before retrieving a file.
Agentic AI workflows can invoke KAPPA functions directly as part of their tool use loop, enriching the metadata context available to the agent at retrieval time and storing the results in the Global Metadatabase for reuse in future queries. This means that as agentic AI workflows run, they progressively enrich the metadata estate, making the Global Metadatabase more useful over time rather than requiring a separate, one-time metadata preparation project.
Agentic AI Frequently Asked Questions
What is agentic AI in simple terms?
Agentic AI is an AI system that can complete complex, multi-step tasks autonomously by planning, retrieving information, taking actions, and adjusting its approach based on results, without a human directing each step. Unlike a chatbot that answers one question at a time, an agentic AI system receives a goal and works independently to achieve it, using available tools and data sources as needed.
What is the difference between agentic AI and generative AI?
Generative AI refers to AI models that generate text, images, code, or other content from a prompt. Agentic AI is a way of deploying and orchestrating AI systems to complete tasks autonomously. Most agentic AI systems are built on generative AI models but extend them with planning, memory, tool use, and the ability to take actions across multiple systems. Generative AI is the engine. Agentic AI is the operating model that puts it to work on enterprise tasks.
What are the biggest risks of agentic AI for enterprise IT?
The three most significant risks are unauthorized data access, where an agent retrieves sensitive or regulated content it was not authorized to see; data quality degradation, where an agent operates on stale, irrelevant, or poorly classified data and produces incorrect outputs; and audit gaps, where no record exists of what data an agent accessed and why, making compliance reporting impossible. All three risks are addressed at the data management layer rather than at the AI model or agent layer, which is why governed unstructured data management is a prerequisite for safe enterprise agentic AI deployment.
How does Komprise connect to agentic AI platforms?
Komprise connects to agentic AI platforms through Komprise Smart Data Workflows, which automate the discovery, curation, and delivery of governed unstructured datasets to any AI destination including S3 buckets, data lakehouses, vector databases, and AI model input pipelines. The Global Metadatabase can be queried by agents using metadata and tag criteria through the Komprise API, enabling agents to locate and request specific datasets without requiring IT involvement for each retrieval. All connections respect the access control policies and sensitive data detection rules configured in Komprise, ensuring agents receive only the data they are authorized to use.