Ever spent hours on Google to find the relevant answers? The struggle doesn’t end here. Just to get more accurate results, we keep switching between different tabs and spreadsheets. To get out of this endless loop, Agentic Search comes into the picture. Unlike traditional search engines, it not only presents links but uses AI-based agents to analyze, cross-reference, and perform actions on your behalf.
This Agentic Search system can be most effective for business professionals who are often juggling between competitive intelligence, market research, and quick decision-making. This search system helps to reduce chaos and manual efforts. This helps to get more reliable and sharper insights. After diving deeper into it and testing dozens of tools, I have compiled everything in this detailed guide. Hope it helps you with better insight.
Agentic search vs traditional SEO - A key differentiation
Agentic Search and traditional SEO-based search majorly differ in terms of delivery and intent. Here’s how: Traditional SEO purely focuses on optimizing web pages so that they rank higher on the search engines. This leaves the entire burden on the user to find the best website, check its credibility, and put all insights together.
On the other hand, Agentic Search works either way. It won’t just show ranked results, but also make use of AI agents that can quickly interpret queries, collect information from different sources, analyze the context properly, and then deliver well-synthesized answers or perform follow-up actions automatically.
If we look from a technical point of view, traditional search (SEO-based) depends highly on keywords, content structure, and backlinks. This allows users to discover the results on a surface level. Whereas Agentic Search uses multi-agent orchestration, natural language understanding, and also integrates well with API and other tools. This helps users access beyond static search results.
The main advantage of using Agentic Search is that it can tap strategically into live databases, enterprise tools, and market feeds to provide businesses with better and accurate context-based intelligence.
In a nutshell, SEO-based traditional search is great for exploring surface-level results and marketing purposes, while Agentic Search focuses merely on accurate and latest outcomes, rather than links. Using this, you can make better and accurate business decisions.
How does Agentic Search impact SEO strategy?
Agentic Search is turning SEO from a ‘visibility point’ to a ‘trust and usability point’. Since AI agents often prioritize depth, credibility, and actionable value, keyword-based optimization is slowly becoming more meaningful. As a businessperson, you thus need to adjust your SEO strategy. Now, you don’t just have to focus on getting a high rank, but on establishing your business as a trustworthy authority with accurate and verifiable data and offering utility in a way that AI agents can confidently provide information to users.
Also, data interoperability is gaining importance. For this, Agentic Search connects with various knowledge bases, APIs, and structured datasets to provide better insights. So, what do you or your SEO team need to do here? You just need to go beyond optimizing your website. You need to ensure that your content aligns well with structured data formats, smoothly integrates with external tools, and that your brand information is easily accessible to machines.
How does Agentic Search work? (mechanics)
Usually, Agentic Search works through a network of autonomous AI agents that often collaborate to interpret data, process it, and execute queries. Rather than retrieving ranking website links from an index, Agentic Search starts with Natural Language Understanding (NLU) to break down the whole intent of the search query into small sub-tasks. After that, these small tasks are equally distributed to the specialized agents, like reasoning agents, data retrieval agents, or tool-based agents. These agents either work sequentially or in parallel, based on the complexity of the user’s request.
The system of Agentic Search deeply relies on the LLMs, involving multi-agent frameworks. LLMs usually perform as a reasoning layer and decide which agents are to be evoked and how to structure a particular workflow. Then, the retrieval agents might connect with some real-time APIs, enterprise-level databases, knowledge graphs, or even web indexes. This will help in collecting structured and unstructured data. After the data is collected, reasoning agents will then validate, analyze, and even cross-reference the entire information and remove all the inconsistent and invalid information (if any).
The best part of Agentic Search systems is its ability to connect with external tools. For example, if you are browsing ‘ what is the difference between Q2 competitor earnings and market forecasts’ and you are unable to find relevant articles, then this system quickly trigger different connectors to finance-based spreadsheets, APIs, and then analyze reports and converts the output into an actionable and understandable summary. This whole orchestration is created using vector-based databases and tool-use protocols, which in turn, help agents to collect context-based relevant links for precise output.
Also, Agentic Search focuses on explainability and traceability. This means that each step of agent-based workflow can be easily logged and audited to show how results were derived (this is important for large businesses). By combining adaptive ranking mechanisms, access controls and compliance filters, these systems ensure that provided insights are not only delivered fast, but also contextual, transparent, secure and trustworthy.
How to choose the best Agentic Search platforms?
- Check the reliability and accuracy of the results - You must choose Agentic Search platforms that not only retrieve data but also validate it properly and provide relevant, credible sources for cross-checking.
- Capacity to handle complex queries - Look for systems that can break down multi-layered or industry-based queries and provide insights with better context.
- Ability to integrate with other tools - Check if the platform integrates well with tools like BI dashboards, CRM, databases, and APIs. It will help you in handling workflows better.
- Compliance and data security features - Cross-verify that your platform adheres to data privacy-based regulations like HIPAA, CCPA (whatever is relevant), and also high-end security.
- Customization features - Ensure that your platform has some collaborative features like report generation, shared workspaces. This will help you to easily work and share projects across teams.
- Check its speed and cost efficiency - Check how well the platform performs and processes requests, especially the ones that are required to make important business decisions.
- Offer transparency with explainable insights - Look for Agentic Search platforms that show how a result was derived. This transparency builds trust and credibility.
Best 8 Agentic Search providers
1. Agenticsearch.ai
Best for - Companies that require intelligent search for unstructured or internal data across different domains like education, healthcare and corporate knowledge.
What it does - This tool offers a dedicated agentic knowledge retrieval system, which is purely built to handle enterprise and institutional data at a large scale. It mainly uses multi-agent orchestration in which one agent silently parses query intent, while another one finds and selects the best data source and the final agent keeps on validating the accuracy of the retrieved data provided by these agents. For this, this platform majorly focuses on context-based search with learning loops, instead of keyword matching. This helps to improve the search results.
Apart from that, this tool also performs great at handling unstructured knowledge bases, government documents and academic repositories. This is beneficial to convert static and boring data into simple, conversational knowledge.
Best features - Context-based retrieval system, multi-agent query routing feature, feedback loops that keep on learning, and domain-based optimization.
Pros:
- Strong ability to understand the context of the query better
- Capable of working across industries like healthcare, education, etc.
- Improves the accuracy of the search results with its automation source selection
Cons:
- Not suitable for beginners; technical setup is required.
- Often requires normalization of data and upfront ingestion.
Use case - The government can use this tool to quickly retrieve important policies with cited and verified results across numerous documents.
2. Unleash
Best for - Large businesses using SaaS-based heavy ecosystems for areas like HR, CRM, and customer support.
What it does - This tool simply extends the enterprise search into the agentic layer of SaaS platforms. Rather than just searching documents in Slack, Salesforce, or Confluence, it just interprets the user's intent and context of the task. After that, it routes it to the right knowledge source and can even execute several actions like updating tickets, drafting replies, and sending data to CRM platforms. IT combines RAG, vector search, and workflow triggers that can easily fit inside the enterprise-based applications.
This makes the search more focused on completing tasks directly within the workflows, instead of just ‘finding files’.
Best features - Context-based awareness system, conversational SaaS-specific search, role-based access controls, and integrated workflow execution system.
Pros:
- Can be used to search and perform activities in one place
- Reduces the hustle to switch contexts manually
- Works smoothly with high-volume customer support
Cons:
- Highly depends on the SaaS-based ecosystem
- API management is a bit complex for IT professionals
- Licensing costs can be a bit higher.
Use case - A telecom-based customer support team can search ‘refund eligibility’, and this tool can retrieve the relevant policy and even auto-generate a response for the customers. This can save a large chunk of time.
3. Azure AI Search
Best for - Businesses that are already using Microsoft Azure and require an explainable, scalable, and multi-step search system.
What it does - This tool simply integrates agentic retrieval orchestration directly into Microsoft's enterprise system. So, whenever a query comes into the system, it breaks it into small sub-queries and then routes them across various hybrid indices (like keyword + vector embeddings). After that, it re-ranks search results with the help of a grounding agent. It performs all these actions before even providing a relevant and explainable answer to the user. As compared to other tools that are quite opaque, this one provides retrieval transparency by logging reasoning steps, performing query decomposition, and then ranking logic.
This tool is specifically designed for regulated industries like healthcare, finance, and government, often where compliance and audits are important.
Best features - Decompose query planning management, hybrid retrieval feature, enterprise-level security, insightful dashboards, etc.
Pros:
- Offers better search accuracy as compared to traditional search.
- Provide complete transparency for compliance-heavy use cases.
- Integrates deeply with Microsoft 365+ Copilot.
Cons:
- Has a vendor lock-in to Azure
- Initial configuration is a little complex (such as indices and pipelines)
- For large-scale rollouts, it is expensive
- Not suitable for SMBs.
Use case - A hospital system uses this tool to answer queries like ‘FDA-approved drugs for pediatric epilepsy since 2022’. For this, this system will quickly decompose the query into data, drug class, and FDA source, and then provide traceable and precise results.
4. LlamaIndex (formerly GPT Index)
Best for - Startups, developers, and large enterprises that require open-source and customizable agentic search solutions.
What it does - This tool offers a developer-focused agentic search framework that can help businesses build customized retrieval pipelines. As compared to closed systems, it is quite modular and works better as an open-source tool. This simply means that teams can swiftly manage and design the flow of the queries. From embeddings, retrieval to synthesis, you have the flexibility to structure the flow as you want. The best part is its RAG (retrieval-augmentation generation) pipelines, agent orchestration, and graph-based indexing. Using this, search agents can easily select index and knowledge graphs as per the requirement.
Also, this tool supports multi-modal data ingestion like SQL, PDFs, Cloud drives, and APIs, so you can easily customize enterprise-level setups too.
Best features - Highly modular in nature, supports hybrid retrieval process, rapid prototyping system for custom use cases.
Pros:
- Offer full transparency as an open-source tool
- Highly customizable for unique business workflows
- Has a large developer ecosystem
Cons:
- Require engineers to deploy this system
- For high-volume search, you may require scaling infrastructure
- As compared to other popular SaaS-based providers, it is less preferred.
Use case - A mid-size law firm can use this tool to integrate all statutes, case law, and firm memos into a full custom RAG pipeline. Attorneys can even ask queries like ‘precedents on digital privacy for California’ and then the system can help to retrieve context-based, structured results.
5. Onyx
Best for - Companies that want a privacy-focused and custom search agent that can function across systems.
What it does - This tool provides an agent-based enterprise search assistant that can easily integrate with tools like Salesforce, Google Drive, and GitHub. It performs both intelligent retrieval and analytics, all by using LLM-based knowledge graphs. This helps to deeply answer ‘why’ queries (like why a sales process is successful) and deliver required recommendations.
Besides that, it helps to reduce hallucinations using RAG strategies and even offers flexibility to choose their LLM and tailor agent behaviours as per their usage.
Best features - Feature to reduce hallucination rates, has LLM-based knowledge graphs, and deep integration with enterprise tools.
Pros:
- Quite a transparent offering, modifiable codebase
- Offers recommendation-based search
- Deeply integrates with internal tools
Cons:
- Scaling is less proven; it matures at an early stage
- High investment in development is required
- UI is not much polished to scale at a smaller level
Use case - A tech-based company can use this tool to search ‘best-performing sales workflows’ across Google Drive, Slack, Salesforce, and get not only real-time data, but also graph-based recommendations.
6. Airweave
Best for - Companies that want to offer agentic search services on live and cross-system data with minimal engineering.
What it does - This tool acts like a connective tissue for agentic search pipelines. Rather than being a standalone search engine, it smoothly feeds unstructured and structured data from various SaaS-based applications, APIs, and databases directly into knowledge graphs or vector stores in real time. With this, these agentic search agents can query synchronized and fresh data without depending on the engineering teams to build ETL pipelines. This tool ensures that every connected data source, like Notion, Slack, GitHub, Shopify, is easily searchable.
By running on incremental syncs with event triggers, this platform makes sure that changes in one system are quickly available for query reasoning.
Best features - Offers 100+ connectors, no-code data sync, real-time embeddings, and graph DB pipelines/scalable vectors.
Pros:
- Supports broad data integration
- Can be quickly deployed with no-code setup
- Ensure that AI agents provide up-to-date information
Cons:
- Requires a complementary agentic search layer
- For unique data models, you may need to customize them.
Use case - A retail company uses this tool to sync tools like Slack, Shopify, Notion, and support tickets into their agentic search pipeline.
7. Microsoft Copilot Search
Best for - Organizations using Microsoft 365 looking for AI-based search across collaboration and productivity tools.
What it does - This tool integrates agentic search directly into Microsoft 365 and Bing. It combines graph-based retrieval from hybrid semantic search, Microsoft Graph and task orchestration with LLM reasoning. For large businesses, this can help to search across Microsoft Teams, Outlook, OneDrive, and SharePoint with context-based responses. For example, it can help you summarize project files, extract important action points from meetings, find related conversations, and even initiate workflows.
Also, this tool acts as an AI-augmented knowledge assistant and makes cross-app discovery, analysis, and execution seamless for large businesses.
Best features - Companies that require enterprise-level agentic search across the Microsoft ecosystem.
Pros:
- Offer permission-aware and secure search
- Deeply integrates with enterprise tools
- Helps to avoid data leakage
Cons:
- Designed primarily across Microsoft apps.
- To scale, this tool can be a bit expensive
- For non-MS tools, flexibility is limited
Use case - A lawyer can browse queries to pull clauses from 50 contracts stored in OneDrive. They even highlight differences and create a comparative report automatically.
8. Google Search AI Mode
Best for - Mid-to-large scale companies looking for AI-based search in order to automate decision-making, research, and workflow tasks across systems.
What it does - This tool transforms traditional Google Search into a full-fledged autonomous task execution engine. Rather than just returning links, it makes use of semantic embeddings, multi-step planning agents, multimodal grounding, and hybrid retrieval to break complex search queries into sub-tasks. With this, it can not only find nearby services but also compare pricing, execute bookings, and cross-check reviews automatically.
It connects well with Gmail, Google Maps, and Workspace. This makes it convenient for both personal and professional use. Also, it shifts Google from an information portal to an action-focused agentic system.
Best features - Smoothly integrates with the Google ecosystem, delivers multi-step queries, and offers AI-based search results with real-time task execution.
Pros:
- Covers massive data
- Can be seamlessly adopted by the customers
- Offer multi-step reasoning and execute tasks smoothly
- Strongly integrates with the Google ecosystem.
Cons:
- There are some privacy concerns
- Offer limited customization options
- Excessively depends on the Google ecosystem
Use case - A business owner can ask Google AI Mode to search 3 local marketing agencies, compare their pricing models, and schedule a consultation with the most effective and efficient option.
Feature comparison
Agentic Search - Market trends and research
As per a report by Precedence Research, the global Agentic AI market is expected to reach USD 199.05 billion by 2034, with a CAGR of 43.84% from 2025 to 2034. This means that many businesses are now realizing the importance of Agentic AI for their workflow automation, and the demand is increasing.
Many large companies like Microsoft, Google, AWS, Salesforce, IBM and AWS have already started building orchestration platforms. At the same time, many mid-size companies are deploying domain-based agentic systems in different areas like supply chain, BFSI, healthcare, and consumer goods.
At present, Agentic Search is not only evolving but also swiftly moving from experimental pilots to becoming an essential part of businesses across industries. This states the broader trend of investing in systems that can not just provide information, but also reason, validate, and perform on their behalf.
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