AI Native vs. Agentic AI and how Prizm brings both to a self-driving platform?

AI Native refers to how something is built — a product, company, or system designed from the ground up with AI as a core component, not bolted on afterward. Think of it like “cloud-native” software. Key traits: 

• AI is central to the architecture, not an add-on feature 
• Workflows, data pipelines, and UX are designed around AI capabilities 
• Examples: Cursor (AI native code editor), Perplexity (AI native search), Claude.ai itself 

Agentic AI refers to how an AI behaves — specifically, AI that can take sequences of actions autonomously to complete goals, often using tools, memory, and decision-making loops. Key traits: 

• The AI acts as an agent that plans and executes multi-step tasks 
• It can use tools (web search, code execution, APIs, file systems) 
• It operates with varying degrees of autonomy, sometimes without constant human input 
• Examples: an AI that autonomously researches, writes, and sends a report; or Claude using tools to browse the web and run code 

The Key Distinction 

How They Relate 

They frequently go together but aren’t the same thing: 

• A product can be AI native but not agentic (e.g., an AI writing assistant that just generates text on demand) 
• A product can be agentic but not AI native (e.g., a legacy enterprise tool that added an autonomous AI workflow on top) 
• Many modern tools aim to be both — built around AI from day one, with agents that autonomously complete complex tasks 

In short: AI native is about design origins, while agentic is about autonomous action. 

Here’s a breakdown of Prizm by DQLabs and its core AI native functions: 

What is Prizm? 

Prizm by DQLabs is positioned as the industry’s first AI native platform that unifies context, data observability, and quality into a single control plane — continuously understanding data, evaluating its trustworthiness, and operating across the enterprise. It was recently recognized as a Visionary in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions for the second consecutive year. 

Core AI Native Functions 

1. Multi-Agent Architecture Prizm is built around autonomous, role-driven agents that continuously profile, prioritize, analyze, and remediate data issues — reducing manual intervention and enabling scalable data trust. This is the heart of what makes it truly AI native rather than just AI-assisted.
2. Unified Context Across Quality, Observability & Lineage Prizm connects observability signals, data quality metrics, lineage, usage, and business context into a single control plane — ensuring issues are understood in terms of their broader impact, not just as isolated anomalies. 
3. Criticality-Driven Prioritization Prizm automaticallyidentifies and prioritizes business-critical data assets, focusing monitoring depth and remediation efforts where impact is highest, rather than treating all data equally. This is a significant shift from traditional rule-based quality tools. 
4. AI Ready Data at Scale Prizm continuously evaluates data fitness for analytics, ML, and GenAI use cases — helping organizations scale AI initiatives with confidence, accountability, and reduced operational risk. 
5. Continuous Learning from Context Built with an agentic core, Prizm learns from metadata, lineage, usage patterns, and outcomes to gather context that helps prioritize what matters, monitor and detect issues early, and orchestrate resolution with minimal human intervention — while keeping humans in control through AI stewardship.

Why “AI Native” Matters Here 

Connecting back to our earlier discussion — Prizm exemplifies both concepts: 

• AI Native: It wasn’t built as a traditional data quality tool with AI added on. The entire architecture — agents, context engine, prioritization logic — was designed around AI from day one. 
• Agentic: Prizm uses autonomous, AI native, agentic intelligence to manage data observability and quality, meaning its agents act, decide, and remediate continuously without waiting for human instruction at each step. 

In short, Prizm represents the shift from reactive, rules-driven data quality management to a self-driving, continuously intelligent data platform.