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Exploring the Future of AI-Driven Agentic Search Architecture and RAG

An in-depth look at Retrieval Augmented Generation (RAG), agentic search evolution, context graphs, and SEO strategies for AI-powered search and commerce in 2026 and beyond.

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Exploring the New Architecture of Search Dawn Anderson – Bertey SMX Advanced Boston 2026 RAG, Context Graphs & Agents
One might assume a vision of a future of agentic search...
Where agents autonomously collaborate to... REDUCE THE VOLUME ON ALL HOME DEVICES SIMULTANEOUSLY RESEARCH SERVICES ONLINE SHORTLIST BASED ON REVIEWS AND RATINGS BOOK APPOINTMENTS CHECK DIARIES
Could be Sundar Pichai on 'The Future of Search'... in April 2026
But that was Sir Tim Berners- Lee who wrote of this 'Agentic Search Future'... in 2001
25 years later... Agentic web is getting there as the next stage of AI search...
Who is Dawn Anderson? • SEO practitioner for almost 20 years • International SEO conference speaker since 2017 • Boutique agency owner - Bertey • Enterprise SEO consultant • Information retrieval & AI search world interloper • Pomeranian lover and avid runner ;P • Commissioned to write a book on AI SEO currently
The current scenario is an architecturally hybrid blend of classic, agentic & AI Search
The misconception • LLMs don't search • They generate (and guess)
Since LLMs are simple 'next word' guessing machines
Currently LLM & AI Search answers can feel intelligent until they're confidently wrong...
And an LLM alone is like a very shaky and wobbly jelly
LLMs alone... • Stale data • Hallucinations • Bias risk • Guessing • Overly generic • Mistrust • Reputation loss • Legal claims
Reliable Agentic & AI Search MUST have a 'rock solid' foundation
The 'Disambiguating Difference' is required
Hello... Retrieval Augmented Generation (RAG). Bridging gaps in knowledge and context for LLMs
RAG (originally introduced by Facebook Research Team) is evolving quickly... since 2020
Evolution of RAG The RAG Paradigm Naive RAG • Keyword matching • Lexical • Lacks context • Cheap and cheerful • Poor on intent Advanced RAG • Vector database • Includes pre- retrieval • Includes post- retrieval • Semantic context • Still stuck in time • Greater context but not recent Modular RAG • Hybrid model • Cost control (modular model switching) • Can combine lexical RAG and semantic RAG • Reranking is optional Graph RAG • Traverses knowledge graphs • Maps entity relationships • Context / intent Agentic RAG • Reasoning • Iterative • Multi-turn • Learns when to stop / when enough info has been received to decide
From simple 'Naive RAG'… Keywordesque
Simple RAG Flow To more 'Modular RAG'...
RAG uses adaptations of classic information retrieval approaches
Meaning... To some VERY large extent... It's still just search engines and SEO
With Nuances...
Pre-Retrieval RAG Techniques TECHNIQUE PURPOSE HOW IT WORKS Query Expansion Improves recall by capturing synonyms. Uses an LLM to generate multiple versions or related terms for the user’s query to ensure no relevant documents are missed. Query Transformation Clarifies intent for better matching. Re-writes a complex or poorly phrased user query into a more "search-friendly" format that aligns with the vector database. Hypothetical Document Embeddings (HyDE) Bridges the gap between query and document. The LLM generates a "fake" ideal answer to the query. This fake answer is then used to search the database, as it shares more semantic similarity with real answers than a short question does. Multi-Query Retrieval Reduces sensitivity to phrasing. Generates several variations of a query from different perspectives and retrieves documents for all of them, aggregating the results. Step-Back Prompting Handles complex reasoning. The LLM generates a broader, more "high-level" query (a step back) to retrieve context about underlying concepts before addressing the specific question. Query Routing Directs the query to the right source. A classifier determines which specific database, tool, or index (e.g., "Technical Docs" vs. "HR Policy") should be searched based on the query. Data Cleaning & Enrichment Removes "noise" from the source. Involves stripping HTML tags, resolving acronyms, and adding metadata (like headers or summaries) to chunks before they are ever indexed.
Query Fan Out is a Form of Pre-Retrieval Technique
Post-Retrieval RAG Techniques Technique Purpose How it Works Reranking Optimizes relevance order. Uses a more powerful (but slower) cross-encoder model to re-score the initial search results, moving the most "semantically rich" documents to the top. Context Compression Reduces noise and saves tokens. Identifies and extracts only the specific sentences or paragraphs within a document that are relevant to the query, discarding the "fluff." Prompt Compression Maximizes efficiency. Uses specialized algorithms to remove redundant tokens from the retrieved context without losing the core meaning, helping fit more info into the context window. Context Filtering Removes low-quality matches. Sets a similarity threshold; any retrieved document that doesn't meet a certain "confidence score" is discarded to prevent irrelevant data from confusing the LLM. Context Reordering Combats "Lost in the Middle." Strategically places the most important documents at the very beginning and very end of the prompt, as LLMs tend to ignore information placed in the center of long contexts. Summarization Distills large volumes of data. If too many documents are retrieved, an LLM creates a concise summary of all findings to provide a "bird's eye view" instead of raw text chunks.
Reality - Hybrid retrieval – Combined strength via dense + sparse + rules
BUT... RAG is a process, NOT magic
RAG & LLMs STILL have MANY limitations
Documents Vs Chunks (PRECISION VS NOISE BALANCES)
'Lost in The Middle' - Positional Bias Challenge in RAG & LLMs
Freshness & Recency Bias LLMs Don't Know 'Now' – RAG is also sensitive to latency
Most AI search failures are still retrieval failures
You've got to be in it to win it... You can't generate what you can't retrieve
Relies on fast and relevant indexing and updated content indexed
Bloated index could possibly lead to... Curse of dimensionality
Possibly calls for increased 'dynamic index pruning'
Stale Retrieval == Wrong Answers
And... Semantic blur - Embeddings Aren't Magic - Similarity is NOT relevance
… You can't CONFIDENTLY generate without STRUCTURE
Structure as signal can Help Architecture, Headings, lists, tables, strong internal structures & focused intent
Structure Aligns with Graph RAG
Structure is the Input to Context Graphs CLEAR ENTITIES EXPLICIT ATTRIBUTES DEFINED RELATIONSHIPS UNAMBIGUOUS BOUNDARIES
Relationships, Context & Entities Mapped to Augment
Contextual Relevance – Same Query, Different Need • Could this mean? • DIY guides • Find a plumber locally • Parts needed for a leaky faucet?
Context Graphs vs Knowledge Graphs
Knowledge Graphs + Context Graphs – The 'What' + The 'Why'
Why Context Graphs Matter in AI Search Context is not a single document Chunking fragments meaning Retrieval returns partial views Context graphs restore coherence Context graphs enable agentic action through structured understanding Context graphs power AI commerce Meaning emerges across entities, attributes and relationships
Agentic RAG is the Glue... Agents don't just answer. They ACT, collaborate on user intent... Then reflect and improve
Universal Commerce Protocol (UCP) & Agentic Commerce Protocol Enable Agent-Merchant Interaction at Scale... Discover, compare, complete purchases...
Why UCP and ACP Matter to SEO? Visibility now includes agent accessibility, not just rankings
SEO in An Agentic Commerce World Optimizing for agentic-commerce readiness Structured, agent- friendly product meta-data Capability meta-data (eg available actions) High precision feed, timeliness & accuracy Protocol compliance (UCP, ACP, MCP)
Structure optimization is no longer optional • Structured data is retrieval infrastructure • Schema is NOT rich snippets • Structured data feeds retrieval • Enables graph construction • Tabular data, connected data is structured data too • Additional product schema is NO coincidence • Don't forget semi-structured data • Context and knowledge graph building with internal link graphs aligned with intent and context
Key Actions
THIS is the new search stack
In reality... Hybrid Search is Likely The Norm... "It Depends" is sooooo true ;P
AI Search via Agentic RAG is closer than we think... BUT AI doesn't fail loudly, it fails fluently
The 'Disambiguating Difference' is required
The AI SEO Playbook on pre-order
Thank you!