AI Agents vs. RAG vs. Agentic AI, Explained for the C-Suite

Executive Summary

• AI Agents autonomously execute specific tasks within a defined context.
• Agentic AI refers to systems of multiple AI agents that collaborate to achieve complex goals.
• RAG combines search (information retrieval) with generation to answer complex queries using external data.
• AgentFlow platform uses all three to automate regulated workflows in finance and insurance.

Understanding The Key Terminology

AI Agents

According to the AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges paper, AI agents are software programs that autonomously execute specific tasks.

They don't only follow static instructions, but they can also observe, decide, and act based on goals, inputs, and changing environments.

Therefore, AI agents rely on context, memory, and goals to carry out operations like data extraction, classification, or report drafting.

AI agents interact through APIs, files, or databases and typically run inside enterprise systems.

They're designed to pursue objectives with minimal human intervention, resulting in chained actions that automate smaller tasks to reach desired results.

Example: Document AI is an AI agent trained on your schema to extract and organize retrieved information from policy documents or loan applications. Its output includes structured JSONs, confidence scores, and audit-ready logs.

Other examples include:

• Unstructured AI for preprocessing documents for RAG.
• Decision AI for automated approval in underwriting.
• Database AI to query internal databases.

AI agents are purpose-built, auditable, and often deployed privately to meet data governance requirements.

Agentic AI

AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges paper also defines agentic AI as systems that exhibit agentic behavior such as goal-setting, plan formulation, tool selection, and adaptive coordination across systems and environments.

Unlike isolated agents, agentic AI functions like a project manager, orchestrating tasks across multiple agents, enabling agents to collaborate and achieve business goals.

While related to AI agents, agentic AI emphasizes autonomy and initiative.

Example: AgentFlow is an agentic AI platform that manages dozens of specialized AI agents across processes like loan origination, claims adjudication, policy generation, and more.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a hybrid framework with two key components:

1. A retriever that selects relevant documents from a corpus.
2. A generator (typically a language model) that uses those documents to produce an informed answer.

According to this study, RAG retrieves relevant information from external sources and then uses that context to generate more accurate and grounded responses.

However, it’s not a decision-maker and it can't autonomously pursue goals.

It’s more of a research assistant ideal for question answering, search, and summarization tasks.

Example: Conversational AI combines RAG with Unstructured AI to power enterprise search across policy libraries, financial disclosures, or underwriting manuals.

Bringing It All Together

The AI space is rapidly evolving, with terminology often overlapping.

AI agents, agentic AI, and RAG are foundational blocks. However, distinction can seem subtle and often interchangeable.

Hybrids are emerging too, such as agentic RAG, systems that chain dynamic data retrieval and generation tasks across multiple agents.

To use any of these technologies effectively (individually or together), we need a clear understanding of the fundamentals.

AI Agents vs. Agentic AI

The main difference between AI agents and agentic AI is autonomy, initiative, and goal orientation.

AI agents perform a single task. Agentic AI coordinates many agents to achieve high-level, multi-step business goals.

Key Distinctions

• AI Agents: Task-driven, bounded scope, deployable independently.
• Agentic AI: Goal-driven, dynamic, and multi-agent orchestration.

Example – Finance:

• AI Agent: Document AI classifies bank statements.
• Agentic AI: AgentFlow uses Document AI, Database AI, and Decision AI to automate workflows fully.

Example – Insurance:

• AI Agent: Report AI drafts claims documentation.
• Agentic AI: AgentFlow orchestrates intake, validation, decision, and documentation across agents.

Graphic Recommendation: Swimlane diagram showing task handoffs between agents within AgentFlow. Caption: "AI Agents vs. Agentic AI in Workflow Execution" Alt Text: Comparison of isolated vs. orchestrated agent operations

AI Agents vs. RAG

The main difference between AI agents and RAG is the purpose and behavior.

AI agents are designed to take action, perform tasks, make decisions, and interact with systems and tools to achieve business goals.

RAG is a method for improving language models by grounding their outputs in externally retrieved data. It works by retrieving context and generating informed responses, but it can't act on its own.

AI Agents and RAG both support enterprise automation but differ in scope and reliability.

Agentic AI vs. RAG

Agentic AI and RAG serve very different roles.

While agentic AI is designed for autonomous and goal-directed behavior, RAG is a retrieval technique that helps improve the accuracy of generated text by pulling in relevant information. It doesn't have goals, plans, or autonomy.

It's best to understand the difference between agentic AI vs. RAG in the following way:

• RAG is a tool within an agent’s toolkit.
• Agentic AI decides when and how to invoke RAG or other tools.

RAG can be part of an agent, but agentic AI controls how and when it’s used.

Use Case – Insurance:

• AgentFlow coordinates Document AI to classify claims
• Uses RAG to fetch policy clause language
• Engages Decision AI for final approval
• Uses Report AI to generate audit-ready memos

AI Agents, Agentic AI, RAG In Practice

Example – Automated Loan Underwriting (Finance)

Specialized AI agents, coordinated by agentic AI, can streamline a complex workflow by combining data extraction, retrieval, and decision-making.

• Unstructured AI ingests and chunks PDF statements.
• Document AI extracts salary, risk factors.
• Database AI helps with diligence.
• RAG retrieves current rate sheets and policies.
• Decision AI computes risk scores.
• Report AI outputs approval memo for the underwriter.

Example – Claims Adjudication (Insurance)

In insurance, AI agents and traditional RAG can work together under an agentic AI framework, which helps accelerate the claims adjudication process.

This is a simplified example of how combined AI agents that collaborate together automate end-to-end workflow with minimal human intervention.

• Unstructured AI parses medical notes and accident reports.
• Document AI tags and validates treatment codes.
• Conversational AI supports adjuster Q&A.
• RAG references policy exclusions.
• Decision AI computes eligibility.
• Report AI documents the outcome.

Implementation Guidelines

Use AI Agents When...

• You need to automate repetitive, task-based workflows (data extraction, form filling, file routing)
• The task has clear user query (input) and outputs with minimal ambiguity
• You want to reduce manual effort in structured or semi-structured processes.

Use examples: Processing invoices, extracting data from claims, and uploading files to a CRM.

Use Agentic AI When...

• You need a system that can make decisions, adapt to changes, and coordinate multiple steps or agents
• The workflow involves dynamic goals, logic, or real-time prioritization
• You want an AI system that acts like a work colleague, not just a tool

Use examples: Managing a full loan approval workflow, triaging insurance claims, coordinating multiple departments or systems.

Use RAG When...

• You need to generate responses based on up-to-date or domain-specific internal and external knowledge
• The task requires grounding outputs in accurate information from internal or external sources
• You want to improve chatbot, assistant, or search functionality

Use examples: Referencing policy language, answering FAQs with internal documents, and generating summaries using real-time data.

Executives, Remember What’s At Stake

Choosing the right architecture affects:

• Profitability: AI agents replace 5+ FTEs per workflow and can help optimize entire workflows and reallocate resources based on real-time priorities.
• Speed: Agentic AI cuts process cycles from days to hours by eliminating bottlenecks and manual dependencies.
• Regulatory risk: Traceable AI agents ensure compliance with IFRS9, SOX, and HIPAA.
• Data governance: AI agents, agentic AI, and RAG work together to reduce risk and improve auditability.
• Data-driven decision-making: Structured insights from agents, combined with context-aware responses from RAG, empower faster, more confident decision-making, while agentic AI connects insights to high-level business levels.
• Strategic agility: Choosing agentic AI builds a foundation for adaptive enterprise workflows as priorities, regulations, or market conditions change.

Don’t be sold black-box large language models. Insist on traceability, orchestration, and domain specificity.

See Agentic AI in Action

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Please book a free demo call to see how our AI agents and AgentFlow, an agentic AI platform, can streamline your most complex workflows, reduce costs, and deliver tangible ROI after implementation of 90 days or less.