What Is Agentic Search? Breaking Down the Concept and Definition

People’s searches for “agentic definition” have jumped by over 640% in the past six months. This dramatic rise shows how the tech world has embraced this concept.

But what does it really mean?

Agentic AI systems can pursue complex goals, make decisions, and run multi-step processes without constant human oversight. This technology reshapes the way users interact with online information through search. Traditional search engines wait for specific queries, but agentic search works proactively for users.

The effects are clear and measurable. Beta tests reveal that agentic search can improve manual search efficiency by up to 70%. Experts project that 33% of enterprise software applications will use agentic AI by 2028, compared to less than 1% in 2024.

This piece explains agentic search’s core concepts and how it is different from regular search engines. You’ll learn how this technology thinks, plans, acts, and evolves – changing your connection with the internet forever.

Understanding the Term ‘Agentic’ in AI Context

The term “agentic” has grown across many fields before making its way into artificial intelligence. Learning about its roots helps you understand how it’s used in today’s search technology.

Agentic meaning in psychology and education

Psychology introduced “agentic” to describe people who take charge of their actions and decisions. Stanley Milgram’s Agency Theory explains how the agentic state happens when people give up their freedom to authority figures. This differs from the autonomous state where people keep control of their actions.

Stanford psychologist Albert Bandura expanded this idea in the late 1980s through his social cognitive theory. He described agentic people as “self-organizing, proactive, self-reflecting and self-regulated“. This view shows people shape their experiences rather than just react to them.

The agentic approach has caught on in education. Agentic learning means “self-directed actions aimed at personal growth and development based on self-chosen goals”. Students who take charge of their learning show:

• Better engagement and deeper learning
• Better problem-solving and critical thinking
• More resilience and self-confidence
• Stronger inner motivation

Research shows that “Students with agency and control over their learning… are more likely to learn deeply and demonstrate persistence”.

Define agentic in AI systems

AI uses “agentic” to describe a system’s capacity to act independently and purposefully. Agentic AI builds on generative AI by adding features that let systems make decisions on their own with little human oversight.

These systems can chase complex goals, make choices, and complete multi-step tasks. They process huge amounts of data from many sources to analyze problems, create strategies, and finish tasks by themselves.

Agentic AI gets better through feedback, which makes it more effective as time passes. This sets it apart from regular AI models that work within set limits and need humans to step in often.

The system combines various technologies like large language models, machine learning, natural language processing, and reinforcement learning. Unlike generative AI that just responds to users, agentic AI works actively toward set goals.

What does agentic mean in search?

Search technology has changed because of “agentic” features. These systems now understand complex questions, do research on their own, plan tasks, and give detailed results.

Users no longer need to hunt for information. They can now focus on making sense of the organized content that the search agent provides.

Agentic search does more than answer simple questions by:

• Getting what users really want
• Doing multi-step tasks on its own
• Using logic to find information
• Adjusting to changes during searches

This marks a big step forward from old search engines that needed exact queries and manual sorting. An agentic search system works more like a research helper than a simple search tool. It handles complex research tasks on its own while backing up findings with citations and references.

What Is Agentic Search and How It Works

       

Agentic search has revolutionized how AI systems retrieve and process information. These systems now actively perform complex information tasks on your behalf, unlike traditional passive query responses.

Autonomous task execution in agentic search

Agentic search systems work independently. They make decisions and complete tasks with minimal human oversight. You won’t need to write code or manually connect searches – these systems can access, process, and visualize operational data.

The power of agentic search lies in its autonomous nature. An agentic system’s core includes four vital capabilities:

• Persistence: It keeps state and memory across interactions
• Goal-direction: It pursues specific objectives rather than just responding
• Initiative: It spots needed information or actions proactively
• Adaptability: It learns from feedback to improve

Let’s look at a real example. You need information about laptops for graphic design within a budget. Traditional search would need multiple queries. An agentic search might ask, “What are the best laptops for graphic design?” then automatically refine with “Which of these is under USD 1000.00?”. The system handles inventory management, data reconciliation, and combines content without constant guidance.

Multi-step reasoning and planning

Agentic search uses sophisticated reasoning to break complex queries into manageable steps. The multi-step reasoning process works like this:

1. First exploration: The agent maps information, spots key document types and gaps
2. Contextual retrieval: It ranks sources by their relevance
3. Multi-hop reasoning: The system links information across sources
4. Contradiction detection: It spots conflicts and potential risks
5. Explanation generation: The agent shows its reasoning with evidence

The system tackles complex questions through a chain-of-clarifications approach. It improves understanding through self-generated questions and contextual groundings. The system breaks down tasks dynamically, assigns them to worker models, and combines their results.

Microsoft shows this capability in Azure AI Search by analyzing entire chat threads to find basic information needs. The model creates focused subqueries that run at the same time, rather than single queries.

Contextual understanding of user intent

Agentic search goes beyond simple keyword matching. It reads nuanced intent based on your history, priorities, and contextual cues. This creates a transformation from reactive to proactive operations.

The agent looks at more than just the current query.

It processes:

• Previous conversation history
• Your known priorities
• Related entities and concepts
• Basic goals behind the question

This awareness helps agentic search deliver custom, individual-specific results. Microsoft’s system reads chat history as input, rewrites original queries into multiple subqueries, and fixes spelling mistakes.

The agentic search process follows a dynamic loop: Plan → Search → Act → Monitor → Repeat. This continuous cycle helps the system refine its understanding and adapt to changes or new information. The result is much more powerful than traditional single-query search approaches.

Agentic Search vs Traditional Search Engines

     

The difference between traditional search engines and agentic search systems shows a vital change in how we find information online. These changes run deeper than what meets the eye and show a complete transformation in the way people access and use online information.

Manual query-based search vs proactive agents

Traditional search engines work reactively. You type specific keywords, get matching results, and then must look through them to find what matters. People often need multiple searches with better keywords to find exactly what they need.

Agentic search systems work like autonomous assistants that help solve your information needs. They don’t just wait for exact queries. The system reads your intent, splits complex questions, and runs multiple searches at once.

The main difference lies in how they work. Traditional search tries to solve problems in one step. Agentic search uses a recurring system of watching, thinking, and acting through multiple steps. This leads to smarter information retrieval.

To name just one example, see climate change research through a traditional search engine. You might need separate searches for each factor. An agentic search would split this big topic into smaller searches about greenhouse gasses, deforestation, and industrial activities. Then it would show you one complete answer.

Summarization and synthesis of multiple sources

Agentic search shines in its ability to combine information from many sources into clear, unified answers. Traditional search engines just show a list of results. Users must extract and combine information from different websites and articles by themselves.

Agentic systems can:

• Visit many relevant sources on their own
• Pull important information from each
• Spot contradictions between sources
• Mix findings into a complete answer
• Show one unified result that combines all collected information

Complex questions need multi-step reasoning. Traditional RAG (Retrieval-Augmented Generation) follows set steps: query → retrieve → generate. Agentic search makes smart decisions at every step. When comparing rocket launch dates, an agentic system gets dates for each rocket and compares them in order without needing separate searches.

Real-time information retrieval and citation

The way agentic systems handle current information and source tracking marks another key difference. Traditional search results might show old information because they mostly use indexed content without live checks.

Agentic AI actively fetches live information, gets updates, and pulls specific data points needed for decisions. These systems start tasks like data logging, monitoring, and trend analysis without constant user input.

Business and research users benefit greatly from citation features. Agentic search provides references to source materials automatically, which builds confidence in the information’s accuracy. Citation generation works in several ways:

1. Tool-calling to cite document IDs
2. Tool-calling to provide text snippets
3. Direct prompting for citation generation
4. Retrieval post-processing for source identification

This feature creates a clear information trail. Users can check facts and explore sources more quickly than with traditional search engines that need manual checking.

Agentic search changes more than just speed. It transforms how people work with online information, moving from simple searching to directing smart information gathering systems.

Comet Browser as a Case Study in Agentic Search

Perplexity AI’s Comet browser shows agentic search principles at work. This innovative platform transforms theoretical ideas into practical tools that change how we browse the internet.

Agentic search implementation in Comet

Comet goes beyond regular browsers by completing tasks on its own. The system books flights, compares hotels, and creates detailed travel plans – tasks that usually need multiple searches. It understands complex instructions and makes smart decisions within set boundaries.

Comet shines brightest when handling multi-stage research. To name just one example, see how it researches scientific topics. It doesn’t just find simple articles – it discovers in-depth studies, expert views, and the latest research findings. This creates new knowledge rather than just finding information.

Deep AI integration and personalization

Regular browsers add AI through extensions, but Comet builds AI into its core. This key difference creates a unique browsing experience where AI naturally blends into every interaction.

The browser studies your work habits, interests, and what information you need. It suggests relevant content and automates tasks you do often. This adaptive feature creates an accessible interface that matches your needs better over time.

Cross-platform support and app integrations

Built on the Chromium framework, Comet provides stability and works on different platforms right from the start. You can browse on your desktop and continue naturally on mobile devices without losing data. Your bookmarks, settings, and AI features stay in sync across all devices.

Comet plans to work with over 800 apps, making it a hub for all your digital tasks. This extensive integration means you won’t need to switch between apps and websites. You can do everything right in your browser.

This case study shows how agentic search moves from theory to practice. It creates tools that work independently while adapting to your needs and work style.

Implications of Agentic Search for SEO and User Behavior

AI-powered search is changing the digital world for website owners and users. This fundamental change means we need new ways to create and optimize content as AI agents step between users and information.

Impact on content discoverability

The rise of AI-powered search has made technical SEO more vital than ever. Websites must be easy for AI systems to read through better metadata, quick loading times, and clear sitemaps. Content strategy needs to focus on what users want rather than just keywords.

Websites that use structured data and schema markup get better results as AI models heavily rely on these elements to understand content. Data shows a 20-30% boost in organic search traffic for sites using structured data. Fresh content gives sites an edge because AI systems prefer recent updates and often rank articles updated within days higher.

Changes in user behavior

People will likely visit fewer websites as AI agents handle their initial research. Users now get direct, conversational answers that match their needs instead of clicking through multiple search results. When users click through to a website from AI results, they stay longer and engage better with the content.

The way people search is changing. Many users skip traditional search and rely on AI agents built into their devices, apps, or platforms. This creates new competition where showing up in AI responses is vital for being seen.

Making content work for AI agents

AI agents read information differently than humans. They value clarity, reliability, and relevance more than attractive graphics or clever headlines. Some technical hurdles include:

• Page speed: AI systems often quit if pages take longer than 1-5 seconds to load
• JavaScript limitations: AI crawlers often prefer plain HTML over JavaScript
• Bot protection: Strong security settings can block AI systems completely

Bias creates another big challenge. AI algorithms learn from old data and might spread biases or wrong information without careful monitoring. Websites need both automated tools and human oversight to stay accurate and trustworthy.

Success in this new landscape comes from focusing on quality rather than tricks. Create valuable content that answers user questions accurately and thoroughly.

Conclusion

Agentic search is reshaping our relationship with information. This piece explores how this technology surpasses traditional search by working actively on your behalf instead of just answering queries. The move from passive information retrieval to autonomous digital assistants is one of the most important technological advances since search engines began.

This change means much more than just convenience. Systems like Comet Browser show how agentic search changes the way you find, process, and use information. You’ll spend less time collecting data and more time making decisions based on combined findings. Content creators need to adapt their strategies too. They must focus on substance, clarity, and well-laid-out data rather than keyword tricks.

The best part? Agentic search connects raw information to useful knowledge. While traditional search engines give you links, agentic systems provide answers. This change will speed up as AI gets better. But challenges exist, especially when you have questions about transparency, bias, and the balance between automation and human control.

Agentic search marks both a technological and cognitive advance. These digital partners understand context, link different sources, and learn your specific needs over time. Your role will move from hunting information to directing, evaluating, and using the knowledge these systems collect. This blend of human intent and AI execution creates a future where finding information becomes a strategic edge rather than a burden.