What You Will Learn

• Build a company research agent on Relevance AI without coding
• Define clear roles, step-by-step instructions, and reusable variables
• Integrate tools like Google Search, website extractor, LinkedIn, and PDF reader
• Design structured report templates and custom qualification scoring
• Implement error-handling, self-healing logic and scale with CSVs and triggers

Study Guide

Introduction: Unlocking the Power of Automated Company Research with Relevance AI Agents

Imagine having your own digital research assistant,a tireless, structured agent that can gather, analyze, and present company intelligence at your command, all without the need for complex coding. This is the promise of Relevance AI, a platform designed to bring the capabilities of Agentic AI to your fingertips. In this comprehensive course, you'll learn not just how to build an AI agent that researches anything (with a focus on company research), but how to wield every feature, tool, and best practice Relevance AI offers. You'll walk away with the confidence to automate business research tasks, scale your insights, and drive smarter decisions using AI agents.

This isn't just about the mechanics of building an agent. It's about understanding the underlying logic, optimizing for robust and error-resistant workflows, and applying automation to real business scenarios. We'll start from the basics and progress to advanced topics, unveiling how variables, roles, tools, and self-healing logic transform a simple chatbot into a powerful, flexible research machine.

What is an AI Agent? Understanding the Foundation

An AI agent, in the context of Relevance AI, is an entity that acts on your behalf to perform complex tasks. Think of it as a digital team member,one you can train, instruct, and equip with tools to navigate information sources, synthesize findings, and deliver results with consistency and speed.

Example 1: You want to know everything about a competitor’s growth,recent funding, hiring trends, and executive changes. Instead of spending hours, your AI agent collects this data, scores the competitor, and outputs a structured report.
Example 2: Your sales team needs fresh insights on a list of target accounts. Instead of manual research, your AI agent processes a CSV of company names and prepares detailed profiles for each,automatically.

Getting Started with Relevance AI: The Platform at a Glance

Relevance AI is built for accessibility. You don't need to be a developer to create powerful agents. The platform guides you through agent creation, tool integration, variable management, and execution,all through a clean, intuitive interface.

Three main ways to begin building an agent:
1. Templates: Start with a pre-built agent configuration tailored for a specific use case, such as company research, and customize it as needed.
2. From Scratch: Build your own agent by defining its role, instructions, variables, and tool integrations.
3. Prompt-Driven: Type a detailed prompt describing your agent's function, and let Relevance AI generate the initial setup for you.

Best Practice Tip: If you’re new, start with templates to get familiar with the structure. As you gain confidence, experiment with building from scratch or using creative prompts to tailor agents to your unique workflows.

Example 1: You select a “Company Research” template and adjust only the qualification criteria variable to suit your industry.
Example 2: You type, “An agent that finds the latest news, funding, and hiring activity for any company,” and let Relevance AI scaffold the agent for you.

Defining the Agent’s Role: Setting Identity and Context

The “role” is more than a label,it’s the core identity and operational lens for your AI agent. It’s akin to a job description for a new hire.

Why is it important? A clear role guides the agent’s behavior, ensuring it operates with the right focus and tone. For company research, the role might be: “You are a research agent specializing in collecting, analyzing, and reporting on company information.”

Example 1: Role: “You are a competitive intelligence analyst for a B2B SaaS company. Your job is to produce structured research reports on potential competitors.”
Example 2: Role: “You are a due diligence assistant, tasked with verifying company backgrounds for investment decisions.”

Best Practice Tip: Make the role explicit and specific. The more context you provide, the more targeted and reliable your agent’s outputs will be.

Instructions: The Agent’s Standard Operating Procedure

Instructions detail the step-by-step logic your agent follows,just like training an employee. Each instruction can reference tools, variables, and desired output structures.

Example 1: “First, find the official website of the company using Google Search. Next, extract the About Us page content. Then, search for the latest funding news. Finally, compile findings into a structured report.”
Example 2: “For each company, retrieve LinkedIn profile data, extract recent headlines, and assess hiring activity via job postings. Present a summary and qualification score.”

Best Practice Tip: Write instructions like a process checklist. Clear, sequential, and unambiguous steps ensure predictable outputs and make troubleshooting easier.

Variables: Keeping Your Prompts Flexible and Clean

Variables are placeholders that reference external information,like company names, qualification criteria, or report structures. They keep your main prompt uncluttered and make editing or sharing templates effortless.

Why use variables? Editing a variable updates every instance where it’s referenced, which is invaluable when scaling or customizing agents.

Example 1: Variable: company_name. Instead of rewriting the prompt for each company, you update the variable.
Example 2: Variable: qualification_criteria. Easily adjust what “qualified” means without touching core instructions.

Best Practice Tip: Store all frequently changing or user-specific data in variables. This future-proofs your agent and makes bulk processing simple.

Tool Integration: Giving Your Agent Superpowers

A Large Language Model (LLM) can understand and generate text, but it can’t search the web or extract content from websites,unless you equip it with tools. Tool integration is what transforms your agent from a theorist to a practitioner.

How tools are added: Relevance AI allows you to add various tools to your agent’s configuration. Once added, you reference them in your prompt using a command, such as /Google Search or /Extract content from website.

Common tools for company research:
- Google Search: For finding company websites, news, funding, revenue, etc.
- Website Scraper: To extract information from company pages, such as mission, products, or leadership.
- LinkedIn Profile Fetcher: To retrieve company or executive profiles.
- LinkedIn Jobs: To assess current hiring activity.
- PDF Reader: To extract data from publicly available documents.

Example 1: The agent uses /Google Search to find “Acme Corp official website,” then uses /Extract content from website on the found URL.
Example 2: The agent performs /Google Search “Acme Corp recent funding” and /Find LinkedIn jobs for “Acme Corp.”

Best Practice Tip: Only add tools you need. Too many tools can cause confusion or inefficiency. Reference each tool explicitly in your instructions for clarity.

Variables in Action: Structuring Qualification Criteria and Report Output

Two critical variables give your research structure: qualification criteria and report structure. These ensure your agent scores, analyzes, and presents findings in a consistent, actionable way.

Qualification Criteria: Defines what “qualified” means for your context (e.g., revenue above a threshold, active hiring, recent growth indicators).
Report Structure: Dictates how findings are organized and presented (e.g., Executive Summary, Growth Indicators, Financials, News, Qualification Score).

Example 1: Qualification criteria might include: “Minimum 200 employees, $10M+ revenue, recent funding, active product hiring.”
Example 2: Report structure could be: 1. Company Overview, 2. Key Contacts, 3. Latest News, 4. LinkedIn Insights, 5. Qualification Score.

Best Practice Tip: Store these as variables so you can instantly update your scoring logic or report format for all future research runs.

Designing the Research Process: Step-by-Step Automation

A well-designed agent follows a logical, repeatable research process using tools and variables. Here’s how a typical company research agent operates:

1. Find the company website: /Google Search “[company_name] official website.”
2. Extract website content: /Extract content from website [found URL], focusing on About Us, products, and mission.
3. Search for financials and news: /Google Search for “[company_name] revenue,” “[company_name] funding rounds,” and “[company_name] news [past two months].”
4. Fetch LinkedIn data: /Get LinkedIn profile for [company_name], /Find LinkedIn jobs for hiring trends.
5. Extract from PDFs (optional): /Read PDF or /Extract data from PDF found via Google Search.
6. Compile findings: Draft a report using the report structure variable, scoring the company according to qualification criteria.

Example 1: The agent researches “Globex Corporation,” finds its website and LinkedIn, discovers a recent funding round, and notes an uptick in job postings.
Example 2: The agent processes “BetaTech Ltd,” but finds a broken website link; it corrects itself by using the next search result and continues extracting data.

Best Practice Tip: Map out your research process as a checklist before building the agent. This ensures you capture all necessary steps and know which tools and variables to use.

Structured Output: Ensuring Consistent, Actionable Reports

A core strength of Relevance AI agents is producing structured, repeatable outputs. The agent is instructed to draft a research report following your specified format, so every company analyzed is presented in a consistent way.

Why it matters: Consistency enables easy comparison between companies, faster decision-making, and seamless integration with downstream systems like a CRM.

Example 1: Every report includes “Company Overview,” “Financials,” “Latest News,” and a “Qualification Score.”
Example 2: Reports for 100 companies are generated via CSV, each following the same format, ready for upload to your CRM or BI tool.

Best Practice Tip: Use clearly defined headers and bullet points in your report structure for readability and ease of parsing.

Qualification and Scoring: Evaluating Companies with Custom Logic

Automated qualification is a game-changer. By defining your scoring logic in a variable, your agent can instantly assess each company against your business’s needs. This removes bias and accelerates screening.

Example 1: Qualification: “Award 1 point for each of: 200+ employees, B2B focus, recent product launch, active hiring, revenue > $5M. Score out of 5.”
Example 2: For investment research: “Score based on team strength, recent funding, IP portfolio, and growth signals.”

Best Practice Tip: Regularly review and update your qualification criteria as your market or business needs evolve. Store in a variable for instant updates across all reports.

Self-Healing and Error Correction: The Agent’s Resilience

No process is perfect. Sometimes a link is broken, or a search yields no results. Relevance AI agents are designed with self-healing logic: if an error occurs, the agent can recognize it, adjust its approach, and retry the step.

Example 1: The agent tries to scrape a company website and gets a 404 error. It acknowledges the failure, then uses the next best Google Search result.
Example 2: The agent pulls an incorrect LinkedIn profile, realizes the mismatch, and refines its search terms before retrying.

Best Practice Tip: Include explicit instructions for handling common errors in your prompt, such as: “If the website cannot be found, try the next top Google result.”

Bulk Research and Automation: Scaling Your Insights

Manual research is a bottleneck. With Relevance AI, agents can process lists of companies (using CSV files) or run continuously in response to triggers,such as new leads added to your CRM.

Bulk Processing: Upload a CSV file of company names, and the agent runs its research process for each entry, saving hours or days of manual effort.
Autopilot: Set up triggers so that whenever a new company appears in your business workflow (for example, in HubSpot), the agent automatically researches it and updates your database.

Example 1: A marketing analyst uploads a CSV of 200 target accounts. The agent returns 200 structured research reports within an hour.
Example 2: A sales team sets up a trigger: whenever a new lead is added in HubSpot, the agent researches the company and attaches the report to the lead record automatically.

Best Practice Tip: Start with small batches to validate your agent’s logic, then scale up. Always review initial outputs for edge cases or errors before automating at full scale.

Templates and Customization: Building Your Agent Library

Templates are pre-built agent configurations that can be cloned and adapted for your needs. They save time and standardize best practices across your team or organization.

Example 1: Use a “Basic Company Research” template for your sales team, and a “Deep Due Diligence” template for M&A analysts.
Example 2: Clone a public template, then customize the variables (qualification criteria, report structure) to fit your industry or use case.

Best Practice Tip: Build a library of templates for common workflows (e.g., competitor analysis, partnership screening, investor research). Update templates centrally to propagate improvements to all users.

Monitoring, Execution, and Task Management

Once your agent is configured, you can run tasks,each task being an execution of the agent for a specific input. Relevance AI lets you monitor which tools are used, in what order, and track errors or bottlenecks.

Example 1: You launch a task for “Acme Corp,” and watch as the agent sequentially uses Google Search, website extraction, LinkedIn lookup, and report drafting.
Example 2: You identify that the agent is repeatedly failing at the “Extract content from website” step for certain companies, prompting you to refine the extraction logic or add error handling instructions.

Best Practice Tip: Use the execution log to troubleshoot and iterate on your agent. Regular monitoring leads to continuous improvement.

Advanced Automation: Triggers and Integration with Business Systems

Agents can be set to run in response to triggers,events like new data entering your CRM or files appearing in cloud storage. This brings your research workflow as close to real-time as possible.

Example 1: Whenever a new lead is captured via your website form, a trigger fires the agent to research the lead’s company and update your CRM.
Example 2: A new investor list is uploaded to cloud storage; a trigger launches the agent to profile each investor’s firm.

Best Practice Tip: Start with one integration (e.g., CRM) and expand as you validate the value and reliability of the agent’s outputs.

Comparing Basic vs. Advanced Agents: Complexity and Capabilities

A basic agent might perform a simple Google Search and extract a website summary, while a more advanced agent chains multiple tools, applies qualification logic, and structures a detailed report.

Key differences:
- Basic Agent: Simple prompt, few tools, minimal structure. Faster setup, but limited insights.
- Advanced Agent: Detailed prompt, many tools, variables for flexibility, error handling, qualification scoring, and structured output. More setup, but scalable and robust.

Example 1: Basic: “Find Acme Corp website and summarize it.”
Example 2: Advanced: “Research Acme Corp using Google, extract website and LinkedIn data, analyze financials and news, score based on criteria, and draft a multi-section report.”

Best Practice Tip: Start with a basic agent to validate your workflow. Gradually layer in complexity,additional tools, variables, and logic,as your needs expand.

Real-World Applications: Transforming Business Workflows

Automated research agents have broad business implications. They can revolutionize how you qualify leads, assess competitors, onboard partners, or perform due diligence.

Example 1: A sales operations team uses the agent to pre-qualify leads at scale, focusing their time only on accounts with high qualification scores.
Example 2: A VC firm automates startup screening, ensuring every pitch deck submitted is paired with a detailed, unbiased background report.

Best Practice Tip: Identify repetitive research tasks in your organization. Build or adapt an agent to automate those first, then iterate and expand.

Best Practices and Tips for Building Robust, Scalable Agents

- Always define a clear role and instructions.
- Use variables for all dynamic or frequently updated information.
- Integrate only the tools you need, and reference them explicitly in instructions.
- Structure your report output for consistency and readability.
- Design your agent to handle common errors and self-correct.
- Start small, validate, and scale up to bulk or automated processing.
- Build a library of templates for common use cases.
- Regularly review agent performance and iterate for improvements.

Glossary: Key Terms Explained

- Agent: An AI entity trained and equipped to execute specific tasks.
- Relevance AI: The platform for building and deploying AI agents.
- LLM (Large Language Model): The language engine powering the agent’s understanding and text generation.
- Prompt: Instructions and context defining agent behavior.
- Tools: Functional add-ons enabling real-world actions (e.g., search, scraping, PDF reading).
- Variables: Placeholders for dynamic or reusable data.
- Templates: Pre-configured agent setups for rapid deployment.
- Role: The agent’s defined identity and purpose.
- Instructions: Step-by-step logic guiding the agent’s task.
- Report Structure: Variable defining the output format.
- Qualification Criteria: Variable for scoring or evaluating research targets.
- Tasks: Individual executions of the agent.
- Self-healing: The agent’s ability to spot and correct its own errors.
- Agentic AI: AI agents capable of planning, acting, and adapting.
- CRM: Customer relationship management software.
- HubSpot: A CRM platform, often integrated.
- CSV List: File format for bulk input.
- On Autopilot: Automatic, trigger-based agent execution.
- Triggers: Events that initiate agent tasks.

Conclusion: Mastering the Future of Automated Research

You now have the blueprint for building AI agents that research anything with precision, scale, and resilience. By fusing clear roles, structured instructions, variables, and the right tool integrations, you can automate some of the most time-consuming, repetitive business research tasks,and do so at a quality that rivals manual effort.

The real value comes not just in building an agent that works, but in deploying it where it matters: qualifying leads, evaluating partners, tracking competitors, or surfacing intelligence for decision-makers. The skills and frameworks you’ve learned here are widely applicable,adapt them, iterate, and apply them to your own business needs.

Remember: Start simple, iterate fast, automate wisely. The future of research is agentic, and you’re now equipped to lead the way.

Frequently Asked Questions

This FAQ section is designed to address the most common questions, concerns, and practical details about using an AI agent for research tasks on the Relevance AI platform. Whether you're just getting started or considering advanced automation for business workflows, these answers aim to clarify concepts, provide actionable guidance, and help you avoid common mistakes.

What is the core function of the AI agent demonstrated in the video?

The AI agent’s core function is to conduct thorough research on companies and generate structured research reports.
It automates the process of gathering information from online sources, analyzes multiple data points, and presents findings in a usable report format. This helps users save time and ensures consistency in company research, which is especially valuable for sales, marketing, or investment teams seeking actionable insights.

How is the AI agent built and configured on the Relevance AI platform?

Building the AI agent starts by defining its role, providing company context with variables, and adding step-by-step instructions.
On Relevance AI, you configure the agent by specifying what it should do (the role), what information it needs (variables), and how it should perform research (instructions). You then connect external tools like Google Search, website content extractors, PDF readers, and LinkedIn search. These integrations allow the agent to access and process real-time data, making it more effective than a standard language model.

What are "variables" in the context of building this AI agent, and why are they useful?

Variables are references to essential data items,like company name or report structure,used throughout the agent’s instructions.
They make the agent easier to manage by decluttering the main prompt, simplifying updates, and encouraging template use. For example, you can change the company being researched just by updating a variable, rather than editing the entire prompt. This approach is ideal for scaling and maintaining consistency.

How does the AI agent perform its research process?

The agent follows a set of instructions and uses connected tools to collect and organize information.
After receiving a company name, it uses Google Search to find key details, extracts content from websites, may review PDFs, and pulls data from LinkedIn. The agent is designed to run these tools as many times as necessary, ensuring it retrieves comprehensive, accurate information before generating the final report.

What kind of information is included in the research report generated by the agent?

The report typically includes a company overview, qualification score, growth indicators, AI initiatives, synergies, conversation levers, and a summary with source links.
The exact structure is determined by how you configure the agent, and can be tailored to your business needs. For instance, a sales team might prioritize hiring indicators and funding rounds, while investors may focus on growth and innovation signals.

How can users initiate the research process with the AI agent?

Users start research by simply typing the target company’s name into the Relevance AI interface.
This input triggers the agent to begin its step-by-step process, using the configured tools and variables to gather and process information automatically.

What happens if the AI agent encounters an error during the research process?

The agent is designed to be “self-healing”,if it makes a mistake (like using a wrong URL), it can recognize the error and correct itself.
This is possible because the agent’s instructions include error-checking steps, allowing it to retry actions or select alternative sources when issues arise. This reduces the need for manual intervention and increases overall reliability.

How can the built AI agent be scaled or automated for research on multiple companies?

The agent supports bulk research by accepting CSV uploads and can be set to run automatically based on triggers.
You can upload a list of companies, and the agent will generate separate reports for each. Integration with business tools like CRMs allows for automatic research whenever a new record is created, supporting seamless workflows for sales or marketing teams.

What are the main ways to begin building an agent on Relevance AI?

You can start building an agent from templates, from scratch, or by describing the agent’s function in plain language to generate a starter configuration.
Templates are useful for common research tasks, while starting from scratch or using a prompt-based approach gives more flexibility for custom workflows.

What is the purpose of defining a "role" for the AI agent?

The role gives the agent a specific identity and context, such as "research agent" or "company analyst".
This shapes its behavior, ensuring it interprets instructions correctly and delivers outputs relevant to your business objectives. For example, an agent defined as a “company research expert” will focus on accuracy and depth, while a “sales intelligence assistant” may prioritize qualifying information.

How are "variables" used in the agent's prompt, and what is their advantage?

Variables are referenced within the prompt using placeholders, making it easy to update key information without editing the whole instruction set.
This modular approach means you can adapt the agent for different research targets or report styles in seconds,just swap out the variable values.

How are tools integrated into the agent's workflow?

Tools are connected to the agent and referenced within instructions using commands (e.g., /Google search).
This lets the agent perform specific tasks,like searching the web or extracting website content,that extend beyond the capabilities of a language model alone. The instructions specify when and how to use each tool to maximize efficiency and precision.

What are examples of Google searches the agent can perform?

Common searches include finding the official company website, revenue data, funding rounds, and recent news headlines.
For example, the agent might search “Acme Corp annual revenue” or “Acme Corp latest funding round” to pull critical business insights.

Besides Google search, what other tools can the agent use?

The agent can extract content from websites, read PDF documents, retrieve LinkedIn profiles, and search for LinkedIn job listings.
These tools help gather information from official company pages, investor reports, and employment trends, resulting in richer research outputs.

What is the purpose of the "qualification criteria" variable?

Qualification criteria define what makes a company attractive or suitable for your objectives, such as sales size, hiring, or growth metrics.
The agent uses these standards to evaluate companies and assign scores, making it easier to prioritize leads or investment opportunities.

How does the "report structure" variable influence the agent's output?

The report structure variable determines the format, sections, and type of information included in the final report.
This ensures every report is consistent, easy to read, and tailored to your business needs. For example, you can require every report to end with a summary and a list of source links.

What does “self-healing” mean in the context of Agentic AI?

Self-healing refers to the agent’s ability to recognize and correct its own mistakes during a research task.
If a tool fails or an action produces an error, the agent can adjust its approach and try again automatically,minimizing disruption and improving reliability.

Why is connecting external tools (like Google Search or LinkedIn) so important for AI agents?

These tools allow the agent to access real-time data and perform actions that a language model alone cannot.
For example, a language model can answer questions from its training data, but only external tools can fetch the latest news or open a company’s current website. This enables accurate, up-to-date research that’s actionable for business decisions.

What are the key steps in building a company research agent on Relevance AI?

The process involves defining the agent’s role, setting up variables, writing clear step-by-step instructions, and integrating tools for web and document research.
Each step ensures the agent understands what to do, how to do it, and how to adapt its process when new requirements arise. For example, you might first instruct it to find the company website, then gather revenue data, and finally summarize findings in a set format.

What are the potential drawbacks of using variables in agent prompts?

Variables can make troubleshooting more complex and may introduce errors if not updated consistently.
If a variable is missing or contains incorrect data, the agent’s outputs can be inaccurate. However, the benefits,like template sharing and easy updates,generally outweigh these risks, especially when variables are managed carefully.

How does a basic agent compare to a more complex agent with extra steps and tools?

A basic agent might only run a few Google searches, while a complex agent integrates several tools, runs multiple research steps, and creates more comprehensive reports.
The complex agent provides richer insights, can handle more varied data types (PDFs, job listings, etc.), and is better suited for professional or enterprise needs.

How can automating research with AI agents transform business workflows?

Automated agents can handle repetitive tasks at scale, freeing up human teams for higher-value work.
For example, integrating the agent with a CRM can trigger research every time a new lead is added, ensuring sales teams always have the latest company information without manual effort.

How can AI agents send research results to a CRM like HubSpot?

The agent can be configured to send its findings directly into CRM records using pre-built integrations or APIs.
This keeps your customer database up-to-date and supports more personalized outreach. For instance, when a new company is qualified, the agent can attach a detailed research report to its CRM profile automatically.

What makes a good prompt or set of instructions for an AI agent?

Clear, specific, and step-by-step instructions are critical for reliable agent performance.
Define each action, specify which tool to use, and outline the desired format of the output. For example, tell the agent: “First, use Google Search to find the official website. Then, extract the About Us page content. Next, search for the latest funding round.”

What are common challenges when building or using research agents?

Challenges include tool misconfigurations, incomplete variables, vague instructions, and changing web content.
Testing the agent thoroughly and updating instructions regularly helps ensure accurate, consistent results. For example, if a company website changes its structure, the extraction step may need reworking.

Can you provide a real-world example of using an AI agent for business research?

A sales team uploads a list of potential clients to the agent, which generates a detailed profile for each company,including size, recent news, and hiring trends.
This enables the team to prioritize outreach to companies showing growth signals, saving hours of manual research per lead.

How does Relevance AI handle data privacy when agents access online information?

Relevance AI agents only access publicly available information, and user-uploaded data is processed securely.
Sensitive information should not be included in variables or prompts unless you are sure it complies with your company’s data policies. Always review the privacy settings of connected tools and integrations.

How customizable are the reports generated by the agent?

Reports are highly customizable,users control the sections, data points, and formatting through the report structure variable and prompt instructions.
You can adapt outputs for different stakeholders by adjusting the template, such as focusing on financial data for investors or product news for marketing teams.

Can agents built on Relevance AI be shared across an organization?

Yes, agents can be saved as templates and shared with other users or teams.
This makes it easy to standardize research processes, ensure consistency, and enable non-technical users to benefit from AI-driven workflows.

What are some limitations of AI agents for company research?

Limitations include reliance on publicly available data, challenges with rapidly changing online content, and occasional inaccuracies in web scraping or document parsing.
Regular updates and manual review of critical reports are recommended to ensure accuracy, especially for high-stakes business decisions.

How does bulk research work with CSV uploads?

Users upload a CSV file containing a list of company names, and the agent processes each entry individually to create separate research reports.
This approach is ideal for prospecting, market analysis, or competitor benchmarking at scale.

What are “autopilot” and “triggers,” and how do they work?

Autopilot means the agent runs automatically in response to external events, while triggers are the specific conditions that start a task.
For example, a trigger could be “new company added to CRM,” which prompts the agent to research that company and update its profile.

How does an agent differ from a standard Large Language Model (LLM) chatbot?

An agent is empowered with tools and clear instructions, enabling it to interact with the web and external systems,unlike an LLM chatbot, which can only answer based on its training data.
This means agents can perform real-time research and automate workflows, while chatbots are limited to conversation and general knowledge.

What are best practices for maintaining and updating AI agents over time?

Regularly review tool integrations, update variables, and refine instructions to reflect changes in business needs or data sources.
Solicit feedback from users, monitor report accuracy, and iterate on the agent’s logic as your workflows and data sources evolve.

What are some examples of integrating Relevance AI agents with other business platforms?

Common integrations include CRMs (like HubSpot), marketing automation tools, and internal dashboards via APIs or direct connectors.
For instance, an agent can research a new sales lead and automatically enrich its CRM record, or sync findings to a business intelligence tool for further analysis.

How can users troubleshoot if an agent produces incomplete or inaccurate reports?

Check tool configurations, review the latest variable values, and test each step of the instructions for clarity and accuracy.
Often, refining prompts or updating tool settings resolves most issues. Reviewing logs and sample outputs helps pinpoint where the process broke down.

What future applications are possible with AI research agents?

Possible applications include automated competitor monitoring, personalized client briefings, investor due diligence, and real-time market intelligence.
As agents become more sophisticated and integrated, they can handle increasingly complex tasks,such as scheduling research tasks based on calendar events or analyzing unstructured data from multiple channels.

Where can users learn more about building and configuring agents on Relevance AI?

Relevance AI offers documentation, tutorials, and community forums where users share best practices, templates, and troubleshooting tips.
Participating in these resources can accelerate your understanding and help you stay current with new features.

What does “Agentic AI” mean?

Agentic AI refers to agents that plan, execute, and adjust their tasks,often using tools and interacting with external environments,rather than just responding to prompts.
This enables more autonomous, reliable, and flexible automation compared to simple chatbots or scripts.