Agentic RAG: Building Smarter, Self-Correcting Workflows with Swix AI
- Jason Swick
- Sep 10
- 4 min read
Updated: Sep 10
Introduction
Information overload is one of the toughest challenges for DMOs. Staff are asked questions daily:
“What events are happening this weekend?”
“Do we have updated partner packages for summer?”
“How did last quarter’s campaign perform?”
Too often, the answers are buried in scattered reports, shared drives, or outdated PDFs. Traditional search often returns irrelevant results, wasting time and eroding trust.
Agentic RAG solves this by combining retrieval-augmented generation (RAG) with agent-based orchestration. It doesn’t just fetch information; it validates relevance, self-corrects when results miss the mark, and ensures the final answer is contextual and accurate. For DMOs, this means board members, partners, and travelers always get fast, trustworthy answers.
What Is Agentic RAG?
Agentic RAG is an agent-driven approach to RAG where multiple AI steps work together to:
Validate and categorize incoming queries.
Generate optimized search queries for a vector database.
Evaluate whether retrieved documents are relevant.
Self-correct and re-query if results aren’t good enough.
Deliver a contextual, accurate response.
Why It Matters for DMOs
Agentic RAG is especially useful for DMOs that manage:
Event calendars – ensuring visitors get current, accurate event details.
Partner resources – surfacing the right co-op guidelines or sponsorship info.
Campaign performance data – providing executives with timely, relevant reports.
Traveler FAQs – answering common visitor questions without staff hunting through files.
Unlike static search, Agentic RAG ensures responses are relevant, verified, and continually improving.
Step-by-Step Guide to Building an Agentic RAG Flow
Step 1: Start Node
Add a Start Node as the entry point.
Input Type: “Chat Input” to capture user questions.
Flow State: Initialize with a key query and empty value.
DMO example: A visitor types “What’s happening downtown this weekend?” → stored as query.
[Insert Image Placeholder – Start node screenshot]
Step 2: Query Validation
Use a Condition Agent Node to determine if the query is AI-related (requires retrieval) or general (can be answered directly).
Instructions: “Check if user is asking about events, campaigns, or partners, or just a general query.”
Scenarios:
Scenario 1: DMO/AI Related
Scenario 2: General
DMO example:
“How did our CPC trend last month?” → AI Related.
“What’s the weather today?” → General.
[Insert Image Placeholder – Condition Agent screenshot]
Step 3: General Response Branch
For general queries, connect an LLM Node or Direct Reply Node to give a simple answer.
DMO example: If someone asks “What’s your office address?” → reply with a static or quick answer.
[Insert Image Placeholder – General branch node screenshot]
Step 4: Query Generation
For AI-related queries, add an LLM Node that transforms the user’s natural question into a clean, searchable query.
Example:
Question: “What are the events happening this weekend?”
Query: “downtown weekend events”
Update Flow State: Store output as query.
DMO example: A board member asks, “Can I see Q2 partner impressions?” → query becomes “Q2 partner campaign impressions.”
[Insert Image Placeholder – Query generation node screenshot]
Step 5: Document Retrieval
Add a Retriever Node to connect to your vector database or document store.
Input: {{ $flow.state.query }}
Output: Relevant document chunks.
DMO example: Retrieve this weekend’s events from your event database or partner listings from a shared document store.
[Insert Image Placeholder – Retriever screenshot]
Step 6: Relevance Check
Add another Condition Agent Node to check whether retrieved documents are relevant.
Instructions: “Determine if these documents answer the user’s question.”
Scenarios: Relevant / Irrelevant.
DMO example: If a user asks about “fall festival schedule” but only hotel data is returned → flagged as Irrelevant.
[Insert Image Placeholder – Relevance check node screenshot]
Step 7: Generate Final Response
If documents are relevant, connect an LLM Node to generate the final answer.
Input Message: Combine the user’s question and retrieved documents into a contextual response.
DMO example: User asks, “What events are happening this weekend?” → Answer pulls verified events with times, locations, and highlights.
[Insert Image Placeholder – Final response node screenshot]
Step 8: Self-Correction
If results are irrelevant, add an LLM Node to regenerate a better query.
Instruction: “Refine the query to capture the underlying intent.”
Update Flow State: Store new query.
DMO example: If “events” returned lodging listings, the query regenerates to “downtown events schedule.”
[Insert Image Placeholder – Query regeneration screenshot]
Step 9: Loop Back
Use a Loop Node to send the regenerated query back to the Retriever.
Max Loop Count: 5 (prevents infinite retries).
This creates a feedback loop until relevant documents are found.
[Insert Image Placeholder – Loop node screenshot]
Complete Flow Structure
Start → Query Validation
General Query → Direct Response
AI-Related Query → Generate Query → Retriever
Retriever → Relevance Check
Relevant → Generate Final Response
Irrelevant → Regenerate Query → Loop Back
[Insert Image Placeholder – Complete flow diagram]
Testing Agentic RAG
Try queries such as:
AI-related: “What events are happening this weekend?”
General: “What’s your office phone number?”
Complex: “Can I see board metrics from Q2 co-op campaigns?”
DMO Example Outcome:
Visitors get the latest event details.
Staff retrieve partner packages instantly.
Executives get accurate campaign data without manual digging.
[Insert Image Placeholder – Example test screenshot]
Conclusion: Smarter Answers for Smarter Destinations
Agentic RAG isn’t just about retrieving documents—it’s about building a self-correcting system that improves as it runs. For DMOs, this means:
Faster responses to visitor and partner questions.
Accurate board reports pulled on demand.
Reduced staff time wasted searching for information.
Greater trust in the answers provided by your systems.
By implementing Agentic RAG with Swix AI, DMOs can ensure that every answer—whether to a visitor, partner, or board member—is relevant, timely, and reliable.
Next Step: Pilot an Agentic RAG flow using your event calendars, campaign reports, or partner resources, and see how quickly your team gains back valuable hours.
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