5 Best AI Agent Tools
- May 31, 2025
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As artificial intelligence rapidly evolves, AI agent tools are making a difference across many domains—from businesses to individual users. Thanks to their capabilities in automation, decision-making, and natural language understanding, AI agents go beyond traditional digital assistants and can independently carry out complex tasks.
In this article, we’ll take a closer look at the different types of AI agents and highlight the top 5 AI agent tools in detail.
Top 5 AI Agent Tools
1. Vivollo
Vivollo is an all-in-one platform that enables businesses to build and deploy AI-powered agents to enhance customer support processes and increase revenue. With its user-friendly tools and multichannel support features, Vivollo offers a comprehensive solution for companies aiming to optimize the customer experience.
Key Features of Vivollo:
- AI-Powered Crawling: Vivollo enables you to develop a custom AI model by crawling your website or other resources, allowing the AI agent to provide accurate support based on collected business-specific knowledge.
- Ready-Made Templates: Get started quickly with pre-designed flows tailored to specific AI agent functions such as product recommendations, promotional offers, and lead generation.
- Flow Builder: Design and customize your AI agent workflows with a no-code, drag-and-drop interface. Easily add, rearrange, and personalize blocks to suit your needs.
- Seamless Handoff to Human Agents: While Vivollo handles routine queries with AI, it ensures a smooth transition to human agents via the Grispi application for more complex requests.
- Multichannel Customer Support: Deploy your AI agent across platforms like your website, Shopify, Messenger, or WhatsApp to manage customer support requests 24/7 with ease.
- Boost Sales with Targeted Offers: Proactively engage website visitors with personalized product suggestions to increase engagement and conversions.
- Capture Contact Info via the AI Chat Widget: Collect user data through your AI chat widget to generate leads more effectively. Interact with visitors in real time, capturing key details such as names, emails, and preferences to build a valuable database for personalized follow-ups and marketing campaigns.
Vivollo empowers businesses to automate their customer support processes, increasing efficiency and customer satisfaction. With its intuitive interface and extensive feature set, it’s an ideal platform for companies seeking AI-powered customer service solutions.
2. Auto-GPT
Auto-GPT is an open-source AI agent that uses the GPT model to plan and execute tasks autonomously. It is considered one of the pioneers in the “autonomous AI agent” category.
How It Works
- Receives a goal from the user (e.g., “Create a digital marketing strategy for beginners”).
- Breaks the goal into subtasks.
- Plans each step sequentially and may gather external data (including web scraping).
- Generates new tasks independently and proceeds in a continuous loop.
Advantages
- Fully automated: Completes tasks without human intervention.
- Can generate its own prompts.
- Built on Python, making it developer-friendly.
Limitations
- Task success depends on interaction with the external world, so it’s not 100% reliable.
- Its high degree of autonomy may lead to “prompt drift” (deviation from the original goal).
LangChain Agents
LangChain is a framework designed for building LLM-powered applications. Its “agents” module allows AI to solve specific tasks by integrating with external tools.
What It Does
- Transforms the AI model from a passive responder to an active decision-maker and executor.
- Enables capabilities like web browsing, file reading, and computations via external tools.
- Operates through the triad of “Tool + Memory + Reasoning.”
Advantages
- Supports multiple LLMs and tools (OpenAI, HuggingFace, Google Tools, etc.)
- Context-aware responses thanks to session memory.
- Breaks limits with “tool calling” capabilities.
OpenAgents (by OpenAI - Experimental)
OpenAgents is an experimental AI agent system developed by OpenAI, built on top of GPT-4o. It is designed to break down and complete complex user tasks.
What It Can Do
- Conducts research on the web.
- Reads and analyzes file content.
- Writes code, creates spreadsheets, summarizes information.
- Creates goal-based “roadmaps” for users.
Limitations
- Currently in limited beta, available only to GPT-4o users.
- As an experimental tool, stability issues may arise.
ReAct Agents
ReAct (Reasoning + Acting) is both a methodology and agent architecture that enables LLMs to first “think” and then “act,” clearly separating these stages.
How It’s Different
- Doesn’t just aim for an outcome; it explicitly answers the question “Why am I doing this?” during execution.
- Makes decision-making traceable by generating reasoning logs.
- Reduces error rates in complex tasks through a “think-act-think” cycle.
Advantages
- More reliable results due to explicit reasoning.
- Valuable for technical teams performing error analysis.
- Improved observability of agent actions.
What Are the Types of AI Agents?
AI agents are systems designed to pursue specific goals by gathering data from their environment, processing it, making decisions, and acting accordingly. Based on their use cases, AI agents can be categorized into:
- Reflex-Based Agents: React instantly based on predefined rules. Used in simple scenarios.
- Model-Based Agents: Build an internal model of the environment and make decisions based on past experiences.
- Goal-Oriented Agents: Develop strategies to achieve a defined goal. Operate with planning algorithms.
- Learning Agents: Continuously improve by learning from their environment. Often powered by machine learning.
LLM-based agents, in particular, stand out in understanding and executing text-based tasks, making them increasingly popular among developers and businesses alike.
Are AI Agents Risky?
While AI agent systems offer numerous benefits, they also come with certain risks:
- Uncontrolled Automation: Poorly configured agents may generate faulty outputs or trigger harmful operations.
- Security Vulnerabilities: Agents with access to data may be exploited by malicious actors.
- Misleading Outputs: If trained on unreliable data, LLM-based agents can provide inaccurate guidance.
Therefore, it’s essential to implement scenario testing, authorization systems, and human oversight before deploying AI agents.
AI Agents Are Shaping the Future
AI agents have moved far beyond traditional automation, evolving into systems capable of making and executing decisions on their own. With applications ranging from customer support to internal process automation, these tools—when properly configured—can significantly boost efficiency and reduce labor costs.
However, like all powerful technologies, AI agents must be used with caution and control. Responsible implementation with clear scenarios, ethical boundaries, and transparency will unlock their full potential.
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