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Agentic AI
Explore how AI research agents revolutionize enterprise research by automating market analysis, competitor tracking, and sentiment insights.
Enterprise research is a high-stakes process. Decision-makers rely on accurate data to drive strategy when tracking market trends, analyzing competitors, or evaluating customer sentiment. However, traditional research methods are slow, expensive, and often struggle to keep up with the volume of information available.
An AI agent for research changes that.
Instead of manually combing through reports or scattered sources, AI research agents automate data collection, analysis, and synthesis. They can provide insights that would take human researchers hours, even days, to uncover. With 51% of enterprises already using AI agents in production and 78% planning to adopt them, it’s clear that businesses are embracing AI-driven research to stay competitive.
For organizations handling compute-intensive workloads, these agents act as research analysts that continuously pull in relevant information, refine their outputs, and deliver structured reports that teams can act on immediately.
In this guide, we’ll explore how AI research agents work, where they provide the most value, and why enterprises are increasingly using them.
An AI agent for research is an autonomous system designed to gather, analyze, and interpret data from various sources. Unlike traditional research tools, which require manual input and predefined queries, AI research agents operate with a level of adaptability that allows them to refine their process over time. It's important to understand that numerous AI research agents exist, each configured for specific research goals and domains. These specialized agents can vary significantly in their capabilities, methodologies, and areas of focus.
These agents use machine learning (ML), which enables them to learn from data and improve over time, and natural language processing (NLP), which helps them to understand and interpret human language. This allows them to sift through massive datasets, recognize patterns, and generate insights. They’re valuable for areas like market research, competitive analysis, finance, and academia because they can handle complex research across many topics.
There are different types of AI research agents, each optimized for specific tasks:
By automating these tasks, AI research agents allow organizations to scale their analysis, reduce manual workloads, and improve accuracy in decision-making.
AI research agents automate complex data tasks so enterprises can analyze huge amounts of information quickly and accurately. Here’s how they operate:
AI research agents pull data from diverse sources, including databases, reports, web pages, and internal documentation. For instance, AI agents have been reported to reduce the time required for manual data tasks, such as conversion and migration, by up to 80%.
Using natural language processing (NLP) and machine learning (ML), AI categorizes and extracts key insights from unstructured data. The financial services industry is leading the charge, with 69% of firms using AI for analytics and 57% for data processing. These capabilities enable AI agents to perform deep research and identify trends and correlations faster than human analysts.
Once the data is analyzed, AI presents findings in the form of summaries, reports, visualized data, or tailored recommendations. This helps organizations act on insights immediately. Companies that integrate AI agents extensively report a 72% productivity boost, compared to 55% in organizations with limited AI use.
AI agents continuously learn and improve through user interactions and historical data to refine their processes for future performance. This adaptability ensures alignment with organizational goals and leads to sustained efficiency gains. For example, AI agents have been shown to increase task completion speed by 126% for programmers.
By continuously refining their outputs, AI research agents become more accurate, responsive, and aligned with business needs over time. This ability to learn and adapt makes them invaluable across industries, but where do they deliver the most impact? Let’s explore their best use cases.
AI research agents are transforming how enterprises conduct research, offering faster insights and automating complex analysis. Here are some of the most impactful applications:
A web research AI agent automatically searches online sources to collect and organize information. Instead of manually browsing sites and copying data, the agent handles it for you, working continuously, accurately, and at scale.
Here’s a real example: a simple web search AI agent built with Zapier that automates the process of sourcing leads from a list of Australian AI startups. The agent starts with just the company name and follows these specific instructions:
Goal: Automate lead sourcing by finding contact information based on the company name.
Zapier leverages large language model technology to streamline task execution, facilitate data transfer between integrated tools, and generate your desired outputs. However, it's worth noting that Zapier Agents operate within the constraints of Zapier's existing automation integrations and offer a limited selection of AI models to choose from.
Market research is essential for staying ahead of industry trends, understanding customer behavior, and tracking competitors. However, manual research can be time-consuming and difficult to scale. AI research agents like those built in Arcee Orchestra can automate the process.
Here’s how it works:
You can learn more about how it works below:
Build a Market Research Automation in Arcee Orchestra
By using AI research agents in Arcee Orchestra, enterprises can reduce manual effort, access real-time market intelligence, and improve research efficiency, freeing up analysts to focus on strategy rather than data collection. Unlike Zapier, Arcee Orchestra goes beyond standard integrations by featuring code nodes within its workflow builder, providing greater flexibility for connecting with existing systems.
This approach enables organizations to leverage their current infrastructure while using AI to drive business value. Additionally, Arcee Orchestra offers a diverse range of models from small to large, accommodating various use case scenarios.
Product research AI agents streamline the discovery process by searching for relevant products tailored to your needs, while analyzing feedback, reviews, and competitive offerings to support more informed decision-making. These agents also handle time-intensive tasks automatically, including price analysis, feature comparisons, and sentiment evaluation across customer feedback.
In Arcee Orchestra, you can build a custom AI agent for product research focused on sentiment analysis by connecting data collection, translation, and language model components in one automated flow.
It helps teams understand how users feel about specific products in real time. They’re especially useful for monitoring product launches, evaluating feature adoption, and keeping an eye on competitor offerings.
Here’s an example workflow you can build with Arcee Orchestra:
This agent helps teams quickly understand what’s working, what’s not, and how users are reacting—without spending hours gathering and interpreting data manually.
You can learn more about automating sentiment analysis using Arcee Orchestra here: Automate Twitter Sentiment Analysis in Arcee Orchestra.
AI agents improve research workflows across various domains and offer numerous benefits for efficiency, accuracy, and innovation.
Artificial intelligence (AI) is significantly accelerating the data collection process for research. In the medical field, traditionally, developing a new drug can take up to 16 years, with clinical trials alone lasting an average of 60 months. However, AI models have the potential to reduce clinical trial durations to about 36 months, effectively cutting the overall development timeline nearly in half.
Another example: Johnson & Johnson employs AI agents to improve their drug discovery processes. These agents analyze complex chemical data to optimize chemical synthesis and accelerate the identification of potential drug candidates. This approach uncovers insights that might be missed through conventional analysis.
By automating routine tasks, AI agents free up human researchers to focus on more complex and creative aspects of their work. For example, a biopharma company used AI agents for lead generation, reducing cycle time by 25% and improving time efficiency by 35% when drafting clinical study reports.
Implementing AI agents in research can lead to significant cost savings. For example, the Agent Laboratory framework, an autonomous system designed to streamline the research process, has demonstrated an 84% decrease in research expenses compared to previous autonomous research methods. This reduction is achieved by automating tasks such as literature review, experimentation, and report writing.
AI research agents transform organizational decision-making by processing and analyzing vast datasets that could overwhelm human teams. This capability enables more informed, data-driven decisions while significantly reducing reliance on intuition or guesswork.
What makes these agents particularly valuable is their ability to scale research operations efficiently without requiring proportional increases in human resources. Organizations can expand their research scope, analyze more data sources, and generate insights across multiple domains simultaneously—all while maintaining consistent quality and methodology.
Yes. AI can automate data collection, analyze large datasets, summarize findings, and surface trends—making research workflows faster, more scalable, and less error-prone.
AI research agents are autonomous systems that use language models and tools like web search, APIs, or CRMs to gather and analyze information based on a specific research goal.
AI agents can handle market analysis, competitor tracking, product sentiment, lead scoring, academic research, and more, depending on the data sources and tools they’re connected to.
They reduce errors by using verified sources, repeating tasks when needed, and applying consistent logic. Some platforms allow you to customize prompts and add source verification steps.
Industries like biopharma, finance, e-commerce, and SaaS are seeing the most impact from AI-powered research—especially in use cases where fast decisions, competitive insights, or large datasets are involved.
AI research agents represent a shift in how modern teams gather, analyze, and apply knowledge. From product development to sales enablement, these agents take on the repetitive, time-consuming parts of research so you can prioritize high-impact work.
They don't just save time—they improve the quality of insights, reduce human error, and uncover patterns that might otherwise be missed. As a growing number of industries embrace AI-powered research, teams that adopt early will have a distinct competitive edge.
Want to see how AI agents can transform research across your organization?
Book a personalized demo with Arcee AI to explore how enterprise-grade AI agents can fit into your workflow, integrate with your data, and drive better decisions.
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