What is the best AI architecture for eDiscovery solutions?

Why DWR AI Compass is the safest, most defensible way to save time and money using AI in eDiscovery.

Retrieval-Augmented Generation (RAG AI) retrieves and grounds information for accurate, defensible legal answers, while Agentic AI uses reasoning, planning, and tools to take multi‑step actions toward a goal. Some courts view Agentic AI use as replacing legal decisions, creating discoverable prompts and potentially waiving document privilege. Digital WarRoom believes the starting point for any defensible eDiscovery AI tool is a closed, non-public RAG AI model.

What RAG AI Does (Retrieval-Augmented Generation)

RAG is a retrieval technique that improves an AI model's accuracy by pulling relevant information from trusted sources before generating an answer. It is fundamentally about knowledge access and identifying fact patterns, not about generating actions or AI decisions.

Key RAG AI behaviors:

• Retrieves trusted documents and feeds them to the model before answering questions
• Grounds responses in approved, properly preserved, up‑to‑date information
•   Follows a bounded pipeline: retrieve → augment prompt → generate defensible return criteria
• Does not act on systems or make relevance decisions independently

Typical use cases:

• Large corpus topic and issue signal analysis
• Customer support knowledge and fact pattern lookups
• Legal, compliance or HR policy retrieval
• PII, Healthcare and finance information identification
• Specific, multi-document summaries

Why purely Agentic AI solutions do not support eDiscovery Obligations

Agentic AI is a goal-driven system that reasons (for the attorney), plans, uses tools, and performs actions across multiple steps. It is about workflow automation, not just answering questions with retrieved facts.

Better use cases:

• IT support automation (reset access, read logs)
• Finance operations (invoice matching)
• Supply chain adjustments
• CRM updates and sales workflows

Core Differences at a Glance

RAG AI retrieves and grounds answers in trusted documents, while Agentic AI plans, decides, and acts across multi‑step workflows—making the difference crucial for legal teams evaluating AI tools. Special attention should be paid to Agentic AI aspects in red when responding to eDiscovery legal obligations.

Why This Distinction Matters in Law

Legal work depends on accuracy, auditability, and controlled workflows. Understanding how RAG and Agentic AI differ helps firms choose the right architecture for research, drafting, compliance, and operational automation in eDiscovery.

What RAG AI Actually Does

DWR AI Compass uses Retrieval‑Augmented Generation designed to answer questions using verified, preserved external documents and fact sources. It retrieves relevant documents and feeds them to a Non-Public language model before generating a response.

• Purpose: Ground answers in approved content.
• DWR Workflow: Retrieve → Generate → Stop … Legal review … Iterate

Strengths:

• DWR Compass prevents hallucinations by citing real documents.

• Supports legal research, policy lookup, and document summarization

• Highly auditable—responses, filter criteria, and Boolean searches can be traced back to sources

For lawyers, RAG is ideal when the output is an answer, not an action—e.g., “How do I isolate all employment policy documents and discussions?” and then “What does our client's 2024 employment policy say about remote work?”

What Agentic AI Does Differently

Agentic AI is built for goal‑directed reasoning and action. Instead of answering a single question, it breaks a task into steps, chooses tools, acts, observes results, and adjusts its plan. For lawyers, Agentic AI is relevant when the system must act, such as updating a matter file, drafting an agreement, or coordinating deadlines. It may create documents on demand or for the requesting party's convenience. In eDiscovery, facts should not be created.

Key Differences for Legal Teams

For more information about DWR AI Compass tools, schedule a demonstration using the button on this page. 

See also AI Security in eDiscovery