Agentic Search for Petabyte-Scale Data: Complete Implementation Guide
By Braincuber Team
Published on February 2, 2026
Traditional data analysis is painfully slow. Users spend hours searching through thousands of data assets, writing complex SQL queries, and trying to extract actionable insights. When you're dealing with petabytes of structured and unstructured data across multiple languages and teams, these challenges compound dramatically. Agentic search changes this equation by letting users query massive datasets using natural language, with an AI agent automatically selecting the optimal search strategy.
This guide walks through building an agentic search solution that combines three complementary search approaches—hybrid semantic search, exhaustive AI evaluation, and direct SQL—within a unified conversational interface. We'll cover architecture, implementation patterns, and the reasoning behind each design decision.
- Why traditional data analysis fails at scale
- Three complementary search strategies and when to use each
- How to build hybrid semantic + SQL search
- AI-powered exhaustive search for complete coverage
- Agent orchestration and intelligent tool selection
The Challenge: Bridging Data and Insights
Enterprise data analysis follows a predictable, frustrating pattern:
Discovery
Search through dozens, hundreds, or thousands of data assets to find the right sources
Query Writing
Write and execute SQL queries, requiring schema knowledge for complex joins and aggregations
Interpretation
Convert raw tabular output into actionable insights—often requires domain expertise
These barriers become severe when combining structured and unstructured data, especially across multiple languages and when semantically similar concepts use different terminology across teams.
Solution Architecture
The agentic search solution addresses these challenges by combining three specialized tools, each designed for specific search patterns:
Hybrid Search
Combines semantic similarity with SQL filtering. First performs vector-based semantic search, then applies SQL filters for precise refinement.
Exhaustive Search
Uses AI-powered evaluation to analyze all matching records when semantic search might miss results due to terminology variations.
SQL Query
Direct structured query capabilities for precise data retrieval when no semantic analysis is needed.
An AI agent analyzes each user query to determine the most appropriate search strategy. The agent automatically routes between semantic search for conceptual queries, exhaustive search for comprehensive analysis, and direct SQL for structured analytics—all through a single conversational interface.
Architecture Components
Core Infrastructure
- Vector Search: Enables semantic similarity search on embeddings, supporting efficient nearest-neighbor queries across millions of data points
- SQL Engine: Provides serverless query execution for ad-hoc analytics and structured filtering
- LLMs: Embedding model for vector generation, reasoning model for agent orchestration, cost-effective model for classification
- Agent Framework: Orchestrates tool selection, conversation flow, and model interactions
Data Ingestion Pipeline
Before the search solution can answer queries, source data must be processed and indexed for semantic search. Here's the ingestion workflow:
Data Extraction
Query source databases, retrieving records including problem descriptions, titles, and categorizations across languages.
Text Preparation
Concatenate semantically relevant fields (names, titles, descriptions) into unified text representations suitable for embedding.
Embedding Generation
Generate 1,024-dimensional vector embeddings that capture semantic meaning of each record.
Vector Storage
Store embeddings in vector index format, tagged with record IDs for retrieval during search.
Metadata Persistence
Original structured data remains queryable via SQL, enabling combined semantic + structured filtering.
This architecture processes data incrementally, enabling continuous updates as new records are added without reprocessing the entire dataset.
Hybrid Search Deep Dive
Hybrid search combines semantic similarity with SQL filtering, enabling users to find conceptually related records while applying precise business constraints.
"Find brake system feedback in F09 vehicles from the last quarter"
This requires both semantic understanding (what counts as "brake system feedback") and structured filtering (specific vehicle model and time range).
Hybrid Search Workflow
Semantic Search Phase
Send semantic query to embedding model, search vector index for top-k similar records based on cosine similarity.
Input: "brake system feedback"
Output: IDs ranked by similarity
[12847, 9203, 15634, 8821, ...]
(top 100 records about brake pad wear, brake fluid checks,
brake performance, etc.)SQL Filtering Phase
Inject semantic IDs into SQL query template. Execute with additional WHERE clauses, JOINs, or aggregations.
-- Template
SELECT * FROM quality_records
WHERE record_id IN ({semantic_ids})
AND vehicle_model = 'F09'
AND report_date >= DATE '2025-10-01'
-- Executed Query
SELECT * FROM quality_records
WHERE record_id IN (12847, 9203, 15634, 8821, ...)
AND vehicle_model = 'F09'
AND report_date >= DATE '2025-10-01'
-- Result: 7 records matching semantic + structured filtersResult Synthesis
LLM synthesizes natural language response with relevant details and insights.
"Found 7 brake-related records in F09 vehicles from Q4 2024. Most common: brake pad inspections (3 cases), brake fluid service (2 cases), brake performance checks (2 cases)."
Exhaustive Search Deep Dive
While hybrid search excels at finding semantically similar records, some queries require comprehensive analysis. Questions like "How many brake-related issues occurred on the F00 model?" demand exhaustive evaluation because terminology variations might cause semantic search to miss relevant cases.
Terminology Variation Problem
The term "brake-related" might appear as:
Semantic search might miss some of these variations. Exhaustive search evaluates every candidate.
Exhaustive Search Workflow
Candidate Retrieval
Generate SQL query using only structured filters—not semantic terms—to avoid terminology mismatches.
-- Retrieves ALL records matching structured criteria
-- (potentially thousands of candidates)
SELECT * FROM quality_records
WHERE vehicle_model = 'F00'The tool includes a retry mechanism: if SQL returns an error, the error message returns to the agent, which can regenerate a corrected query. This prevents hallucinated SQL from silently failing.
Batched LLM Classification
Divide candidates into batches of ~20 records for efficient processing. Each batch is evaluated by a smaller, faster LLM.
This is a tradeoff between number of model invocations and context rot (as tokens in the context window increase, the model's ability to accurately recall information decreases).
Result Aggregation
Aggregate relevant results, enriching each record with a _reason_for_match field containing the LLM's justification.
{
"record_id": 15634,
"description": "ABS warning light during highway driving",
"vehicle_model": "F00",
"_reason_for_match": "ABS is a brake-related system.
The issue describes a malfunction in the anti-lock
braking system, which directly relates to brake
functionality."
}This transparency helps users understand why specific records were included and builds trust in the LLM-powered filtering.
Key Benefits
Unified Data Access
Handle both structured and unstructured data within a single conversational interface. Ask conceptual questions, apply precise filters, or run comprehensive analysis—without switching tools.
Cost-Effective Architecture
Serverless components scale to zero when not in use. Pay only for actual usage, no operational overhead of traditional infrastructure.
Democratized Insights
All users can generate data insights through natural language, reducing barriers between users and valuable information. No SQL expertise required.
Intelligent Routing
Agent automatically selects optimal search strategy based on query characteristics. Users get relevant results without understanding underlying technical complexity.
Agentic search transforms how users interact with their data. By reducing traditional barriers between users and insights—discovery, query writing, interpretation—this approach enables all users to extract value from enterprise data assets. As organizations generate increasing volumes of data across structured and unstructured formats, intelligent search solutions become essential for unlocking full data value.
Frequently Asked Questions
Agentic search uses an AI agent to automatically select the optimal search strategy based on query characteristics. Unlike traditional search which requires users to manually write SQL queries or understand search syntax, agentic search accepts natural language queries and routes them to the appropriate tool—semantic search for conceptual queries, exhaustive AI evaluation for comprehensive analysis, or direct SQL for structured analytics. The agent handles tool selection, query optimization, and result synthesis automatically.
Use hybrid search when you need to find conceptually similar records with structured constraints—for example, 'find brake system feedback in F09 vehicles from last quarter.' It's fast because it first narrows results via semantic similarity, then applies SQL filters. Use exhaustive search when complete coverage is essential and terminology varies widely—for example, 'count all brake-related issues.' Exhaustive search evaluates every candidate record using an LLM to catch terminology variations that semantic search might miss.
Exhaustive search divides candidate records into batches of approximately 20 records each. Each batch is formatted as a markdown table and sent to a cost-effective LLM for relevance classification. The model evaluates each record against the search criteria and provides a relevance determination with justification. Batches are processed in parallel, enabling evaluation of thousands of records in seconds. The batch size of 20 balances between minimizing model invocations and avoiding context rot (accuracy degradation as context length increases).
The solution handles terminology variations through two mechanisms. First, semantic search uses vector embeddings that capture conceptual similarity—so 'brake pad wear' and 'brake system degradation' are recognized as related even though they use different words. Second, exhaustive search uses LLM evaluation to explicitly reason about whether each record matches the search criteria, catching variations that semantic similarity might miss. For multilingual data, embeddings are generated from multilingual models that understand semantic relationships across languages.
Core infrastructure includes: (1) a vector store for semantic similarity search across embeddings, (2) a SQL engine for structured queries and filtering, (3) LLMs for embedding generation, agent orchestration, and classification tasks, and (4) an agent framework for tool selection and conversation management. A serverless architecture is recommended—components scale to zero when not in use, and you pay only for actual queries. You'll also need a data ingestion pipeline to generate embeddings from source data and keep the vector index updated.