What Are the Benefits of AI Agents

Recent data from LangChain's survey of 1,300 professionals shows three main ways companies are getting real value from AI agents, even if the technology isn't perfect yet:

Research and data extraction is where AI agents are proving most useful, with 58% of companies using them to handle information-heavy tasks. Unlike regular search tools, these agents can pull information from different sources, break down complex topics, and create organized summaries. What makes them different is they can work through multiple steps—like starting with a broad search, spotting important themes, and then digging deeper into specific areas based on what they find.

Personal productivity is the second most common use, with about 53% of surveyed companies trying it out. These agents help streamline everyday work by connecting different tools and handling repetitive tasks. Take scheduling meetings as an example—an agent can check calendars, suggest times, send invites, and even draft basic agendas. While it's not perfect, it cuts down on the time-consuming back-and-forth that fills up people's days.

Customer service is the third main area, with 46% of companies testing AI agent-based support. Unlike basic rule-based chatbots that just follow scripts, these agents can understand context and work through multiple steps to solve problems. They gather initial details, check help documents, and either fix simple issues or send complex ones to the right human expert with all the background information ready.

But here's the reality check—AI agents still need careful watching and often make mistakes. Most companies find they need strict guidelines and human oversight to keep their agents working properly. While the technology shows promise, we're only seeing real benefits in specific, controlled situations, not the kind of broad independence that many people initially hoped for.

How AI Agents Work: Examples

We've already established that AI agents can take a single prompt as input and determine a complete multi-step process with subtasks. But what does that look like in practice? Can ChatGPT do it out of the box? Is ChatGPT an AI agent at all?

ChatGPT typically operates on a single prompt-response system. While features like chain-of-thought reasoning might resemble agentic AI—where the AI takes multiple steps or actions—ChatGPT doesn't orchestrate external tasks or tools as an agentic AI would. It's primarily designed for generating responses to prompts.Additionally, LLM-based chat products like ChatGPT do have context windows during conversation, but the windows are limited and not necessarily oriented toward a specific goal. The most "agentic" features available in ChatGPT right now are its AI web search functionality and the ability to upload custom documents, but these are still quite limited compared to truly agentic AI frameworks like V7 Go.

This quote from Ece Kamar, director of Microsoft’s AI Frontiers Lab, nails down the problem and suggests a great solution too:

Imagine you have a difficult problem to solve that an LLM cannot do alone. If I can have an agent—we call it an orchestrator—that’s kind of the reasoner agent, the problem-solver agent that can take that [problem] and divide it into steps, and then identify which existing agent has the right capabilities to do that part of the task and delegate and bring the pieces together, that turns out to be a really useful way of doing more with these agents.

Now, let’s see this exact approach in action. Here is an example of agentic AI for data extraction from complex documents.

We start with a general request, and the AI suggests available pre-designed agents.

Then, we can pick a file from our knowledge hub or upload it directly via the chat interface.

After extracting all relevant information, the AI agent will pre-process it and perform an analysis. What happens behind the scenes is adding a new row to a spreadsheet-like workflow. After processing the information column by column and calculating each step, the agent comes up with the final triage result.

All of the insights are linked to specific sections of the document. You can navigate back to the original information source to verify its accuracy.

Now, you might be wondering where the agentic part comes in. In fact, returning this information to us was the final step of a multi-step process. Here is another view that allows us to take a closer look at what happens under the hood:

This AI workflow uses multiple generative AI models combined with specialized prompts, as well as additional scripts and tools like Python code and OCR. Notice how different parts of the analysis occur in sequence, with new information being used as inputs to the next model or tool—all of them chained together using logic. On top of that, all of this information can be processed in a structured format such as JSON, referenced, or used by other AI agents.

So, how do you build something like that?

One of the best parts is that, in some cases, you don't have to—it's agentic and can sometimes design itself. In other cases, you may want to use a pre-configured agent template and tweak it to your liking. Let’s discuss specific solutions and frameworks for using AI agents.

Best Agentic AI Software

When it comes to implementing AI agents in your business or workflow, you have several options ranging from no-code platforms to developer-focused frameworks. The right choice depends on your technical expertise, specific use case, and how much customization you need.

The agentic AI landscape can be broadly divided into two categories. On one hand, no-code platforms offer intuitive interfaces where you can build and deploy AI agents with minimal technical knowledge. These solutions typically provide pre-built templates, drag-and-drop workflow builders, and visual interfaces for connecting different tools and data sources. They're ideal for business users who need quick implementation without dedicated development resources.

For organizations that need deeper customization or have specific technical requirements, developer-focused frameworks offer more flexibility and control. These code-first approaches require programming knowledge but enable more sophisticated agent behaviors, custom integrations, and specialized workflows.

Let's explore some of the leading platforms in both categories, each offering unique approaches to implementing agentic AI in real-world business scenarios.

V7 Go: AI Agents for Data Extraction & Analysis

V7 Go is a knowledge work automation platform that puts agentic AI at the center of complex document processing. It can also be used for data enrichment, AI-powered web search, and other scenarios where a large amount of unstructured information needs to be analyzed and researched within a domain-specific context. V7 Go lets you both create custom AI agents and access a growing library of pre-built agents designed for specific business needs, use cases, and document types.

V7 Go’s agents can process everything from financial statements and legal contracts to audio files and technical manuals. For example, a financial reporting agent can extract key metrics from quarterly reports, analyze trends across multiple documents, and present the findings in a structured format—all without manual data entry.

For companies drowning in paperwork, V7 Go offers a practical entry point into agentic AI without requiring deep technical expertise. Users can start with pre-built agents for common document types and gradually customize them for specific workflows. This balance of accessibility and power has made it particularly popular in industries like finance, legal, and healthcare, where document processing has traditionally been both time-consuming and error-prone.

Explore use cases: