From Search and Summarize to Multi-step Reasoning: Understanding Agentic RAG

Executive Summary: In this article, we perform a detailed churn investigation scenario to illustrates the distinction between traditional RAG and Agentic RAG, finding that Agentic RAG is not a replacement for traditional RAG, but an architectural evolution that expands what retrieval-augmented systems can do. Where traditional RAG retrieves and generates, agentic RAG plans, investigates, and synthesizes. A search-and-summarize system would provide a list of themes while an agentic system conducts an investigation, identifies root causes, and delivers decision-ready recommendations. The question is therefore not which approach is better. It is which approach best matches the task. For FAQ systems and document search, traditional RAG is simpler and more cost-effective. For diagnostic workflows and multi-source investigations, agentic RAG enables capabilities that were previously the sole domain of manual analysts.

Your product team lead asks a question in the weekly review: “Paid churn jumped 40% in December. What is driving it and what should we do next?”

This is not a question you answer by searching documentation. It is not a single fact lookup. It is an investigation that requires synthesizing evidence from multiple sources, testing hypotheses, and connecting disparate signals into a coherent narrative. And it reveals something important about the limitations of traditional Retrieval-Augmented Generation systems.

Traditional RAG enhances large language models by retrieving relevant context from external sources before generating responses. It follows a straightforward pattern: receive query, retrieve relevant documents, feed context to the model, generate answer. This approach has proven effective for FAQ systems, document Q&A, and scenarios where answers live in well-indexed knowledge bases.

Agentic RAG evolves this foundation by embedding autonomous reasoning into the pipeline. Instead of a single retrieve-then-generate pass, agentic systems plan investigations, dynamically select data sources, iteratively refine their understanding, and use specialized tools to gather and analyze information. The shift is from “search and summarize” to “plan, retrieve, reason, and act.”

The churn question demonstrates why this evolution matters. Let us walk through how each approach would handle it.

“Retrieval-augmented generation (RAG) is a pivotal tool for enterprise adoption of generative and agentic AI: It enhances AI models by providing authoritative knowledge at inference time. RAG empowers AI systems to improve content quality, deliver domain expertise, and support agentic AI capabilities; however, organizations face mounting challenges related to technical complexity, infrastructural scalability, and conceptual clarity. The integration of agentic AI adds additional weight on this pressure, requiring RAG architecture to evolve beyond basic retrieval and generation into adaptive, problem-solving systems. For agentic AI to deliver an experience like no other, these RAG optimizations transform static retrieval mechanisms into autonomous systems capable of reasoning, adapting to new information, and solving complex problems effectively.”

• Charlie Dai, VP and Principal Analyst of Forrester in “How To Get Retrieval-Augmented Generation Right,” June 19, 2025

The scenario: Investigating a churn spike in RAG systems

The data ecosystem

Most SaaS organizations have scattered evidence across multiple systems:

• Product analytics tracking user behavior, feature adoption, session patterns
• Billing and subscriptions containing churn type, plan changes, payment failures
• Customer feedback from exit surveys, satisfaction scores, support tickets
• Support signals showing ticket volume, response patterns, issue categories
• Release context from changelogs, feature flags, and pricing announcements

These systems are not unified. They speak different languages, track different metrics, and require different access patterns. An exit survey might say “too expensive,” while the billing system shows the user downgraded before cancelling, and product analytics reveals they never adopted the feature that justified their original plan tier.

This is the reality we built agentic systems to navigate.

How traditional RAG approaches the question

A traditional RAG system treats this as a search-and-summarize task:

1. Retrieve documents matching “churn” and “December”
2. Pull recent exit survey responses
3. Search for related support tickets
4. Generate a summary of themes

The output tends to be generic: “Users mentioned pricing concerns and missing features. Support tickets increased for onboarding issues. Some customers cited the complexity of the platform.”

This answer is not wrong. But it is incomplete. It lacks the investigation strategy that would reveal why churn increased specifically in December, which customer segments were affected, and what specific changes triggered the spike. Traditional RAG provides a summary of available information. It does not conduct an investigation.

How agentic RAG works on the question

An agentic system recognizes this as an investigation requiring multi-step reasoning:

Step 1 — Frame the investigation

The agent translates an ambiguous question into testable sub-questions:

• What exactly changed? (Segment, plan tier, region, customer type)
• Is this voluntary churn or payment failure?
• What changed in the product or commercial terms?
• What patterns appear in customer feedback?

Step 2 — Establish ground truth from billing data

Source: Billing and subscription platform

The agent pulls a churn decomposition:

• Churn rate by month (baseline vs December)
• Voluntary cancellations vs involuntary churn
• Breakdown by plan tier

Finding: The spike consists primarily of voluntary cancellations, concentrated in Pro plan customers. This finding prevents a common failure mode, such as investigating product issues when the cause is actually billing-related, or vice versa.

Step 3 — Identify who is churning

Source: Product analytics + CRM segments

The agent segments churned customers:

• New customers (under 30 days) vs mature accounts
• Acquisition channel
• Industry and company size
• Geographic distribution

Finding: The spike is heavily concentrated in new customers who churned within 14 to 21 days after signup. Most came from a specific partner acquisition campaign.

The narrative shifts from “churn increased” to “activation failure in a specific cohort.”

Step 4 — Surface the why from qualitative signals

Source: Exit surveys + support tickets

The agent extracts recurring themes from unstructured feedback and correlates with support ticket categories:

Exit survey themes:

• “Too expensive for what I got”
• “Setup was confusing”
• “Missing integration with X”

Support patterns: Tickets tagged “onboarding” or “setup” increased 35% in the same period.

This is where traditional semantic search excels, but note that it is not the first step. It is informed by the segmentation work that came before it.

Step 5 — Correlate with timeline and produce decision-ready analysis

Source: Changelog, pricing announcements, feature rollout notes

The agent checks for temporal alignment:

• Did anything change immediately before the churn increase?

Finding: A packaging change moved “Integration X” from Pro to Enterprise tier on December 1. The partner campaign targeted users who specifically needed Integration X.

Conclusion: The churn spike is driven by new Pro customers from the partner campaign who encountered an early “value cliff” because Integration X is no longer included in their plan tier. This triggered feedback themes around cost and missing features, compounded by increased onboarding friction.

Recommendation: Either restore Integration X access for Pro customers from this partner cohort, adjust partner messaging to set correct expectations, or modify partner targeting to focus on Enterprise-tier buyers.

The agent activity log

Here is what the agentic process looked like in practice:

[Agent: Clarify scope]
→ "Breaking down: which customer segment, which churn type, what changed recently?"

[Agent: Query billing system]
→ Result: Voluntary churn ↑42%, concentrated in Pro plan

[Agent: Segment churned cohort]
→ Result: 73% are new customers (14-21 day window), 68% from partner channel

[Agent: Extract feedback themes]
→ Top reasons: "too expensive" (34%), "missing integration X" (28%), "confusing setup" (19%)

[Agent: Check support ticket trends]
→ "Onboarding/setup" tickets +35% in December

[Agent: Timeline correlation]
→ Integration X moved to Enterprise tier on Dec 1
→ Partner campaign launched Nov 15, targets Integration X users

[Agent: Synthesis]
→ Answer: Churn spike is a packaging mismatch + onboarding friction in partner-sourced Pro cohort
→ Suggest: Restore Integration X for Pro (partner cohort), or adjust partner targeting/messaging

This scenario represents what well-designed agentic RAG enables. In practice, building systems that reliably execute this type of investigation involves iterating on agent strategy, handling edge cases where data is missing or ambiguous, and tuning when to stop versus when to continue gathering evidence. The architectural patterns are proven: the engineering work centers on orchestration design and defining effective investigation strategies for your specific domain.

What makes agentic RAG different

The churn investigation demonstrates several key distinctions:

The architectural shift is from “retrieve-then-generate” to “plan-retrieve-reason-act.”

Visualizing the difference

Traditional RAG flow

User query: "Why did churn spike?"
    ↓
Retrieve relevant documents (exit surveys, support tickets)
    ↓
Feed context to LLM
    ↓
Generate summary: "Users mentioned pricing and features"

Agentic RAG flow

User query: "Why did churn spike?"
    ↓
Agent plans investigation
 ├─ Frame: What changed? Which segment? Churn type?
 ├─ Hypothesis: Need billing breakdown first
    ↓
Agent retrieves from billing API
 └─ Finding: Voluntary churn in Pro plan
    ↓
Agent refines strategy
 ├─ Next: Who are these Pro churners?
    ↓
Agent queries analytics + CRM
 └─ Finding: New customers, partner channel
    ↓
Agent gathers qualitative signals
 ├─ Exit surveys: "too expensive," "missing integration X"
 ├─ Support tickets: +35% onboarding issues
    ↓
Agent correlates with timeline
 └─ Integration X moved tiers Dec 1
    ↓
Agent synthesizes decision-ready output
 └─ Root cause identified + recommendation

(Note: Agentic flow includes feedback loops and adaptive strategy refinement)

When to use which approach

Traditional RAG excels when:

• Query type: Single fact lookups, defined questions with clear answers
• Data sources: One knowledge base or well-indexed document collection
• Reasoning: Retrieve and summarize is sufficient
• Constraints: Low latency requirements, cost sensitivity, predictable load

Examples: FAQ bots, internal documentation search, customer support for known issues

Agentic RAG enables scenarios in:

• Query type: Open-ended investigations, multi-part questions, root cause analysis
• Data sources: Multiple systems requiring different access patterns (APIs, databases, analytics platforms)
• Reasoning: Requires hypothesis testing, evidence correlation, iterative refinement
• Outcome: Decision-ready insights, not just information summaries

Examples: Business intelligence investigations, diagnostic workflows, research assistants, complex customer issue resolution

Decision criteria

When evaluating whether agentic RAG is appropriate, we consider four factors:

1. Query complexity — Can this be answered in one retrieval pass or does it require investigation?
2. Source diversity — Is the answer in one place or scattered across systems?
3. Reasoning depth — Do we summarize information or synthesize evidence into conclusions?
4. Value of outcome — Does the use case justify the additional orchestration complexity and cost?

For the churn scenario, all four factors point toward agentic RAG. A generic summary would not have revealed that the spike was driven by a specific cohort encountering a packaging mismatch triggered by a recent change. That insight requires an investigation only agentic RAG is capable of.

Implications for teams

Architecturally

Traditional RAG systems are simpler to deploy and maintain. The flow is predictable: embed documents, store vectors, retrieve on query, generate response. Performance characteristics are well understood.

Agentic RAG demands more robust orchestration. You need state management across investigation steps, error handling for multiple API calls, strategies for when to stop iterating, and mechanisms to prevent runaway costs. If your agent can invoke five different data sources, you need to handle partial failures gracefully.

For decision-makers

Traditional RAG is the right choice when the task is straightforward retrieval. Agentic RAG becomes valuable when the alternative is either manual investigation by analysts or accepting incomplete answers.

The question is not “should we always use agentic RAG?” It is “where does investigation complexity justify the orchestration investment?”

For developers

Building agentic RAG systems requires skills beyond traditional RAG pipelines. You are orchestrating multi-step workflows, designing agent strategies, managing state across reasoning steps, and integrating diverse tools and APIs. The patterns resemble building autonomous systems more than building search interfaces.

Frameworks like PydanticAI or LangGraph provide orchestration primitives. But the hard work is in designing the agent strategy: when to retrieve, when to reason, when to call tools, and when to stop.

Conclusion

Agentic RAG is not a replacement for traditional RAG. It is an architectural evolution that expands what retrieval-augmented systems can do. Where traditional RAG retrieves and generates, agentic RAG plans, investigates, and synthesizes.

The churn investigation scenario illustrates this distinction clearly. A search-and-summarize system would provide a list of themes. An agentic system conducts an investigation, identifies root causes, and delivers decision-ready recommendations.

The question is not which approach is better. It is which approach matches the task. For FAQ systems and document search, traditional RAG is simpler and more cost-effective. For diagnostic workflows and multi-source investigations, agentic RAG enables capabilities that were previously the sole domain of manual analysts.

At Vstorm, we have built agentic systems that integrate AI with clients’ domain knowledge and legacy systems—where traditional RAG components (vector databases, semantic search) serve as tools the agent uses autonomously alongside APIs, structured databases, and specialized integrations. The shift from traditional to agentic RAG is less about the retrieval technology itself and more about orchestrating multiple capabilities to match problem complexity. Start with the simplest approach that solves the problem. Introduce agentic patterns when investigation workflows justify the orchestration investment.

The goal is not to build the most sophisticated system. It is to build the right system for the task at hand.