4 Reasons Agentic AI Is Reshaping Enterprise Search

Generative AI has been the cutting-edge technology that greatly reshaped the enterprise search landscape. But now, artificial intelligence (AI) development communities are delving into a new industry-leading innovation — Agentic AI.

Agentic AI is a system that exhibits a high degree of autonomy. It designs workflow and uses available tools to take action independently on behalf of the users and solve complex problems that require multi-step solutions. It also interacts with external environments and goes beyond the data on which the system's machine learning models were trained.

AI agents, powered by advanced machine learning techniques such as reinforcement learning, learn from user behavior and improve over time. These agents use multiple tools that enable them to work effectively in dynamic conditions.

This blog explains the key problems that Agentic AI resolves in enterprise search.

Critical Challenges in Enterprise Search That Agentic AI Addresses

Ambiguity in User Queries

Users usually search with certain keywords only, avoiding typing search queries. Due to the vague nature of the query, it becomes challenging for traditional AI models to comprehend the intent and deliver relevant results.

However, AI agents take the decision to rephrase or augment the query. They have a query rephrase tool that autonomously refines or rephrases search terms when they are invalid by analyzing historical data and previous query contexts to refine the query.

Consider a user who searches for "watches," but this query is ambiguous and incomplete and doesn't give the idea of what kind of watches the user is looking for, smart or regular. Now, suppose the user previously searched for "tracking burn calories." AI agents' query rephrase tool will rephrase the query based on the user's browsing history and previous query context and deliver search results for "Smartwatches."

Inconsistent Sentiment Analysis

Sentiments are a range of emotions that customers experience throughout their brand journey. Deciphering those sentiments is one crucial aspect of boosting customer satisfaction scores (CSAT).

Traditional AI models fall short of understanding user query sentiments in many scenarios. Moreover, you have to leverage certain approaches that rely on pre-made dictionaries with words and their sentiment scores (positive, negative, or neutral)  and redefine rules to determine the text sentiments.

However, AI agents autonomously analyze the query sentiment and take action further based on that without human help. Its sentiment analyzer tool captures the overall sentiment of complex sentences, goes beyond just positive or negative sentiment, and distinguishes fine-grained sentiment expressions. 

Suppose a customer searched for "I tried everything but did not get my answers, feeling frustrated."  An AI agent interprets the query sentiment, "the user is frustrated," and suggests something can aggravate their anger. So, it will either create a support ticket for the customer or directly connect with a live support agent to resolve their query.

Identifying Key Entities in Data

Earlier exact match and regex methods were used to find string values to tag the data. However, these methods miss the mark when it comes to contextual tagging and synonyms with the same lemma and stem.

However, AI agents can perform Named Entity Recognition (NER) independently. The tool identifies and extracts key entities such as name, date, location, organization, or product from unstructured data without the need for manual tagging.

This capability of agentic AI enhances the customer experience by making support service faster and more efficient.

Imagine a customer raising a support ticket mentioning, "I haven't received my iPhone 16 pro, which I ordered on September 30." The AI agent tool autonomously performs NER and identifies key entities from the query, such as iPhone 16 pro (product) and (date) through NER. Then, it automatically cross-checks the information from the order database to find the reason for the delay. 

Based on this analysis, AI agents take further action to inform customers of the reason for the delay, initiate refunds directly, or escalate to live support agents directly. Therefore, agentic AI reduces resolution time and enhances customer satisfaction.

Irrelevant Search Results

Users, both customers and support agents, usually desire relevant, accurate, and contextual results for solving their queries. However, traditional models struggle when it comes to capturing evolving user query intent and proactive situation analysis in such a nuanced context. These limitations make traditional models lag behind in improving user satisfaction and efficiency.

AI agents, on the contrary, rerank and refine search results. They automatically adapt to changing user inputs, analyze the past interaction of that user, decipher the evolving users' query intent, keep the previous context in their memory, and then refine and rerank the search result based on these analyses. 

Picture this: When a user searches for "best laptops for gaming," agentic AI goes in-depth for query intent interpretation and considers various factors such as gaming performance, affordability, and customer reviews. Then, results are reranked to bring the most relevant ones before others.

This ability of agentic AI to autonomously fine-tune and prioritize relevant search results improves the user experience.

How the Tools Integrate Seamlessly for Better Efficiency

When a search query comes in, LLMs can determine whether it's related to a previous query or not. Based on this, it comprehends how to integrate previous conversations into this and rephrase a search query if it's incomplete or vague. Using NER, it automatically selects facets.

Simultaneously, it analyzes user sentiment, whether they are happy, neutral, or frustrated, and if they need to escalate the ticket to the support agent. If you give autonomy to the agent, it will figure out to whom to assign the case.

Conclusion

To sum up, AI agents can enhance search accuracy, perform complex reasoning tasks, improve user experience, and complete tasks autonomously without human intervention.