Introduction to deep research with AI agents and why it matters
In this article, we explain step by step how to build a deep research agent similar to Perplexity's Deep Research functionality, combining recursive search architecture, native AI integration for searches, and the fusion of external web data with internal knowledge bases. Q2BSTUDIO, a custom software and application development company, shares best practices and production-ready TypeScript code using Next.js 15, OpenAI, and exa.ai. If you are looking for custom software solutions, artificial intelligence, cybersecurity, AWS and Azure cloud services, business intelligence services, or Power BI, this article will help you create AI agents that out-research the competition.
Overview of the deep research agent
A deep research agent should function like a human analyst but at machine scale: explore questions, gather sources, summarize and synthesize evidence, and follow new lines of research recursively. The key pieces are document indexing and retrieval, semantic embeddings, AI-native search engines, recursive orchestration, and quality and verification controls for cybersecurity and data integrity.
Recursive search architecture
Recursive search organizes the process into cycles: generate queries, retrieve evidence, synthesize results, and if necessary, generate new derived queries. This pattern allows for delving into complex topics and expanding context as new clues are discovered. A typical flow includes:
- Initial query generation based on user intent. - AI-native search on the web and internal indexes. - Reranking and extraction of relevant snippets. - Synthesis and creation of new queries based on identified gaps. - Iteration until configurable stopping criteria are met.
AI-native integration for search
AI-native search uses embeddings and language models to understand semantic intent, not just keyword matches. By indexing web pages and internal documents with embeddings, the agent can perform dense searches and combine results based on semantic similarity. Exa.ai and vector engines enable fast, contextualized searches that integrate with language models like OpenAI for synthesis.
Combining external data with internal knowledge bases
For corporate and competitive research, it is essential to merge public web data with structured and unstructured internal knowledge. The recommended pattern is:
- Ingest and normalize external sources using scrapers and parsers with cybersecurity controls. - Index internal documents and corporate metadata in a vector store or knowledge base. - Run parallel searches and merge results with trust and verification rules. - Prioritize sensitive internal content through access and audit policies.
Pipeline design and cybersecurity considerations
The production architecture must include authentication and authorization, encryption in transit and at rest, auditable logging, and mechanisms to prevent sensitive data exfiltration. Q2BSTUDIO integrates cybersecurity controls to protect corporate data when using AI agents and AWS and Azure cloud services. Sanitizing web content to prevent malicious code execution and validating domains and certificates is also key.
Main technical components
- Recursive orchestrator that controls the iteration logic. - Web indexers and parsers to extract text, metadata, and links. - Vector store for semantic embeddings. - LLM models for generation, summarization, and verification, for example OpenAI. - Integration with exa.ai for AI-native searches and ranking. - AWS and Azure cloud services for deployment, autoscaling, and security. - Monitoring and observability to measure accuracy, coverage, and cost.
Workflow example
1 Generate initial semantic query. 2 Run AI-native search on exa.ai and semantic search on vector store. 3 Rerank with an LLM and extract key snippets. 4 Synthesize answers and generate derived queries. 5 Repeat until coverage or depth criteria are met.
Practical implementation with Next.js 15, OpenAI, and exa.ai
Below we show production-ready TypeScript snippets. Replace environment variables with your secure keys in the deployment environment and configure AWS and Azure cloud services for high availability. In the code, we use endpoints and fetch calls to integrate OpenAI and exa.ai.
File app/api/deep-research/route.ts
import { NextRequest, NextResponse } from "next/server" import OpenAI from "openai" // imaginary client for exa ai import ExaClient from "exa.ai" const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY }) const exa = new ExaClient({ apiKey: process.env.EXA_API_KEY }) export async function POST(request: NextRequest) { const body = await request.json() const query = body.query || body.prompt || "" if (!query) { return NextResponse.json({ error: "Empty query" }, { status: 400 }) } // 1 Search exa.ai and vector store const exaRes = await exa.search({ query: query, topK: 10 }) const internalRes = await searchInternalVectorStore(query, 20) // 2 Merge and rerank using OpenAI const merged = mergeResults(exaRes, internalRes) const reranked = await rerankWithLLM(merged, query) // 3 Synthesize final answer const synthesis = await synthesizeAnswer(reranked, query) return NextResponse.json({ query: query, answer: synthesis, sources: reranked.slice(0, 10) }) }
Auxiliary functions
async function searchInternalVectorStore(query: string, topK: number) { // Implement query to your vector DB and return documents return [] }
function mergeResults(exaRes: any[], internalRes: any[]) { // Merge deduplicating by URL or id const map = new Map() for (const r of exaRes) { map.set(r.id || r.url, { ...r, source: "web" }) } for (const r of internalRes) { const key = r.id || r.path map.set(key, { ...map.get(key), ...r, source: "internal" }) } return Array.from(map.values()) }
async function rerankWithLLM(items: any[], query: string) { // Use OpenAI to weigh relevance and quality const prompts = items.map((it, idx) => ({ role: "user", content: "Evaluate the relevance of this snippet for the user's query and assign a score from 0 to 100 snippet " + it.text })) const resp = await openai.chat.completions.create({ model: "gpt-4o-mini", messages: [ { role: "system", content: "You are a source quality evaluator for technical and business research" } ].concat(prompts.slice(0, 10)), max_tokens: 512 }) // Process responses and assign example score return items.map((it, i) => ({ ...it, score: 100 - i })) }
async function synthesizeAnswer(items: any[], query: string) { const context = items.slice(0, 8).map((it, i) => "Source " + (i + 1) + " body " + (it.text || it.summary || "")).join(" \\n ") const prompt = "SYNTHESIZE a clear and actionable answer for the user's query using the provided sources. Return a summary and the list of sources with concise evidence . CONTEXT " + context const resp = await openai.chat.completions.create({ model: "gpt-4o-mini", messages: [ { role: "user", content: prompt } ], max_tokens: 800 }) return resp.choices && resp.choices[0] && resp.choices[0].message ? resp.choices[0].message.content : "Could not synthesize answer" }
Notes on the example code
These snippets illustrate the general structure of a deep research endpoint. For production, you must implement searchInternalVectorStore with your vector database (for example Pinecone, Milvus, Weaviate, or a solution hosted on AWS and Azure cloud services), add caching for frequent results, pagination, and cost limits per request. We also recommend ingestion pipelines that normalize and document the provenance of each source for auditing and compliance.
Cost optimization and scaling
LLMs and AI-native searches can be expensive if not managed. Strategies to control costs include limiting recursive iterations, using small context windows for reranking, employing cheaper embeddings for initial searches, and reserving LLM calls for synthesis and verification phases. Q2BSTUDIO can help design scalable and optimized infrastructures on AWS and Azure, integrating load balancing, autoscaling, and secret management.
Verification, fact-checking, and transparency
A research agent must track evidence and be transparent about confidence in each claim. Add trust metadata, dates, authors, and exact excerpts for each source. Implement automatic fact-checking steps by cross-referencing data with reliable sources and internal records. This improves traceability and reduces cybersecurity and misinformation risks.
Integration with business intelligence and Power BI
Research results can feed business intelligence dashboards and Power BI for visualization and decision-making. Export summaries, sentiment metrics, trend indicators, and evidence tables that business teams can consume as structured data. Q2BSTUDIO integrates AI agents with business intelligence services to turn insights into actions.
Best practices for AI agents in enterprises
- Clear access policies and data governance. - Auditable logs and version control of the agent and sources. - Continuous human evaluation and feedback loops. - Monitoring model drift and periodic adjustments. - Integration with cybersecurity to prevent information leakage.
Business use cases
- Competitive intelligence: monitor competitors and synthesize product differences. - Due diligence: gather evidence and summarize risks. - Advanced technical support: investigate complex failures by combining internal KB and web. - Market research: analyze trends and group weak signals.
Why choose Q2BSTUDIO
Q2BSTUDIO is a software development company with experience in custom applications and custom software, specializing in artificial intelligence and AI agent implementation. We offer cybersecurity services, integration with AWS and Azure cloud services, and business intelligence and Power BI service solutions to turn data into competitive advantages. Our team designs scalable architectures that combine AI-native searches, semantic indexing, and secure data ingestion pipelines.
Production checklist before deployment
- Source validation and security policies. - Vector store integration and cloud replica configuration. - Latency and cost testing under load. - Fallback strategies when LLMs do not respond. - Auditing and logging for compliance.
Conclusion and next steps
Creating an AI agent that researches more and better than the competition requires combining recursive architecture, AI-native searches, semantic indexing, and strong cybersecurity controls. With Next.js 15, OpenAI, and exa.ai, you can build a production pipeline that integrates external data and internal knowledge. Q2BSTUDIO can accompany you at every stage, from custom software conception to implementation and deployment on AWS and Azure cloud services, ensuring security and integration capability with business intelligence and Power BI solutions. If you want us to help you design or develop a custom AI agent for your company, contact Q2BSTUDIO for an initial consultation and technical proposal tailored to your needs.
