From Mainstream to Experimental: 20 Agentic AI Tools You Should Know

Agentic AI is moving from concept to reality, with tools that can plan, act, and adapt across industries.

In this guide, we spotlight 20 standout platforms: 10 that are already shaping how organizations work; and 10 experimental newcomers pushing the boundaries of what’s possible.

Together, they offer a glimpse into both the present and future of autonomous AI.

What Are Agentic AI Tools?

Agentic AI tools are essentially AI systems that act as autonomous agents–like digital assistants that can make decisions and carry out tasks on their own with minimal human guidance.

Instead of just responding to a single prompt, these tools can perceive their environment or context, reason about what needs to be done, and then take actions to achieve a goal. In simple terms, an agentic AI is “goal-driven” software: you ask it to do something, and it figures out the steps and does it for you, much like a human employee might handle an assignment. For example, an agentic AI might plan your travel itinerary and actually book flights and hotels, not just tell you the best time to travel.

These AI agents work by breaking down complex tasks into smaller steps and executing them in sequence without constant human intervention. They often use advanced AI techniques (like large language models or other AI modules) to decide on actions and can interface with external tools or systems. This means an agentic AI tool can do things like browse the web, use software applications, or send messages as part of completing a task.

Crucially, they maintain agency. That is, the capacity to act independently and adapt to new information. Unlike a simple chatbot that waits for each user query, an agentic AI tool can proactively move through a workflow, adjusting its plan as needed to meet the goal.

Why Do They Matter (vs. Traditional AI)?

Agentic AI is important because it represents a shift from AI as a passive assistant to AI as an autonomous problem-solver.

Traditional AI systems (like standard chatbots or prediction models) typically operate within narrow, pre-defined constraints and require humans to initiate every step or decision. For instance, a basic customer service chatbot can answer questions from a fixed script, but it won’t take it upon itself to resolve an issue fully.

Agentic AI tools, on the other hand, can handle entire workflows end-to-end. They don’t just generate an answer or insight; they can take action on that insight. This means they can maintain long-term objectives, perform multi-step tasks across different systems, and adjust their approach based on real-time data–all with far less hand-holding.

The difference matters because it leads to big efficiency gains and new capabilities. An agentic AI can proactively solve problems and not just provide information. For example, in customer service, instead of simply telling an agent how to fix an issue, an AI agent could autonomously resolve a customer’s request from start to finish.

Gartner analysts predict that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human help, potentially cutting operational costs by 30%.

In healthcare, agentic AI systems can automate complex administrative workflows (like scheduling, coordinating care, and managing insurance claims) which frees up staff to focus on patient care.

Overall, these tools are seen as a “game-changer”, because they combine the intelligence of AI with the initiative to act, opening the door to more autonomous and efficient business processes.

10 Most Popular Agentic AI Tools

Below are ten of the most popular and influential agentic AI tools as of mid-2025, each explained in accessible terms:

OpenAI’s ChatGPT (Agent Mode) – In 2025, ChatGPT gained “agentic” abilities, enabling it to not only converse but also take actions on your behalfopenai.com. It can browse the web, run code, use plugins, and work with apps to complete complex tasks. For instance, you could ask it to plan a budget-friendly marketing event, and it would research vendors, draft emails, build a schedule, and present the plan–all in one workflow, while confirming key steps to keep you in control.

Auto-GPT – Launched in early 2023, Auto-GPT was one of the first open-source autonomous AI agentsakka.io. You give it a high-level goal, and it breaks it into sub-tasks, executes them, and adapts with minimal human input, using OpenAI’s GPT models to reason and decide. For example, tasked with “research and write a report on renewable energy trends,” it could gather data, analyze it, and produce a structured report. Though it began as a demo, Auto-GPT proved how AI can coordinate multi-step projects, and remains free to use with an API.

Microsoft 365 Copilot – Integrated into Office 365 apps like Word, Excel, Outlook, and Teams, Copilot is evolving into a more agentic AI for businesses. It can summarize emails, draft replies, create PowerPoint decks, or schedule meetings based on your work context. Behind the scenes, a “constellation of agents” handles specialized domains (sales, service, finance), coordinating to deliver results–like having multiple expert assistants accessible through natural language.

GitHub Copilot (Coding Agent) – In 2025, GitHub Copilot added an autonomous coding mode. Beyond suggesting snippets, it can review codebases, refactor code, write new functions, and create tests to fix bugs. Acting like a junior programmer, it handles tasks such as optimizing code or updating functions, then proposes changes for approval. Working inside tools like Visual Studio Code, it can also collaborate with other AI agents, all while keeping the developer in the loop.

Google’s Conversational AI Agents (e.g. Project Mariner) – Google is bringing agentic AI into its products, starting with Project Mariner, a concierge-like agent that can find flights, book hotels, shop for items, or suggest recipes in one conversational flow. In Google Cloud, the Conversational Agents Console lets businesses build advanced chatbots that blend generative AI with rules for natural yet compliant customer service. Powered by models like Gemini, these agents can even detect tone and emotion, delivering realistic, “do it for me” experiences across Google apps.

IBM watsonx Orchestrate (and AskIAM) – IBM’s watsonx Orchestrate lets enterprises build AI agents to automate business processes, acting like virtual team members that can log into systems and complete tasks. AskIAM, built on this platform, automates identity and access management, provisioning accounts or approving requests without IT staff. With open architecture, it integrates with existing AI models and data, streamlining workflows across HR, finance, and more to cut tedious work and save time.

Salesforce “Agentforce” – Salesforce’s Agentforce lets companies deploy AI agents in their CRM to automate customer service, sales support, and routine business tasks. An agent can handle inquiries end-to-end–from looking up data to opening tickets and providing solutions–without human intervention for common issues. The 2025 Agentforce 3 release added a Command Center for oversight, 100+ pre-built actions, and open standards like the Model Context Protocol for integration, giving businesses customizable AI helpers to speed responses and keep customers supported around the clock.

ServiceNow AI Agent Orchestrator – ServiceNow’s AI Agent Orchestrator acts as a “control tower” for coordinating multiple AI agents across IT, HR, customer service, and more. With thousands of pre-built agents for common business tasks, it ensures they can communicate, hand off work, and be monitored from a single dashboard. The goal is seamless, cross-department workflows–from a chatbot starting a customer request to IT fixing the issue and another agent sending follow-up– all with governance to manage a growing digital workforce.

AWS “Strands” Agents – Launched in 2025, AWS Strands Agents is an open-source SDK that makes it easier for developers to build AI agents. Developers define the agent’s goal and tools, and Strands handles planning, tool use, and self-reflection to improve results. It can scale from simple local agents to complex cloud deployments, enabling organizations to embed autonomous helpers into systems without reinventing the wheel.

NVIDIA NeMo Agent Toolkit – NVIDIA’s NeMo Agent Toolkit helps companies monitor and optimize AI agents at scale. Compatible with frameworks like LangChain and Microsoft’s tools, it profiles resource use, coordination, and performance to spot bottlenecks and suggest improvements–often leveraging NVIDIA’s accelerated computing. It’s essentially an analytics and tuning suite to keep large AI agent systems reliable, fast, and cost-effective.

10 Experimental and Emerging Agentic AI Tools

As of mid-2025, a number of ambitious and unconventional AI agent tools are gaining attention.

These go beyond basic chatbots. They plan actions, use other software, collaborate with other AIs, and autonomously execute tasks.

Below are ten emerging agentic AI tools, each pushing the envelope in unique ways:

1. BabyAGI – The Task-List Tinkerer

BabyAGI began as Yohei Nakajima’s experiment to let AI manage its own to-do list. Described as an “autonomous AI agent with an AI task manager,” it mimicked how a human might tackle daily tasks–creating, executing, and reprioritizing them as needed.

Originally built to automate Nakajima’s VC research workflow, it was a minimalist Python program (~100 lines) that looped through picking a task, executing it with GPT-4, and updating the list based on new objectives. Though simple, BabyAGI showed how even lightweight agents can self-organize toward complex goals, inspiring many variants and advancing the idea of agentic AI.

2. AgentGPT – Agents for Everyone via Browser

AgentGPT, launched in 2023 as an open-source web app, made autonomous AI agents accessible to anyone. Users can name an agent, set a goal, and watch it generate and execute tasks in a continuous loop. No coding required! Inspired by Auto-GPT/BabyAGI, its simple interface let non-technical users automate tasks like trip planning or market research, attracting 100k users in its first week.

3. Hugging Face’s Open Computer Agent – A Virtual “Robot” on Your Desktop

In 2025, Hugging Face unveiled its Open Computer Agent, an AI that uses a computer like a human–moving a mouse, opening apps, and completing tasks via a cloud-based Windows/Linux desktop. Users give it a goal, and it operates through the standard interface (e.g., finding an address on Google Maps) without special APIs. Though still slow and prone to errors like CAPTCHA traps, it showcased how open-source models could one day handle any PC task end-to-end, much like a digital intern.

4. MetaGPT – A Team of AIs Building Software

MetaGPT is an open-source framework that simulates a software company by assigning AI agents roles like product manager, architect, coder, and tester. These agents collaborate via natural language to plan and build software, with MetaGPT orchestrating the process. Launched in 2023, it’s seen as a promising experiment in breaking complex projects into specialized AI “workers” that can achieve more together than a single agent.

5. Generative Agents (“AI Sims”) – Living Virtual Characters

“Generative agents” are AI characters that mimic human behavior in virtual worlds. In a 2023 Stanford study, 25 such agents–each with a backstory–improvised daily routines, remembered interactions, and even coordinated a Valentine’s Day party without human input. The experiment showed believable, unscripted social behavior, with potential applications in gaming, simulations, and virtual societies.

6. Voyager – The AI That Taught Itself Minecraft

Voyager, developed in 2023, is the first “lifelong learning” agent for Minecraft. Using GPT-4, it autonomously explores, writes its own code, and stores new skills in a growing library, enabling it to craft tools, fight monsters, and build structures. Operating in a plan–act–learn loop, Voyager solved 3× more quests than other AI bots, showing how agents can self-improve and tackle open-ended goals.

7. HuggingGPT (Multi-Model Orchestrator) – “ChatGPT, Assemble!”

HuggingGPT, proposed in 2023, uses ChatGPT as a “manager” that coordinates multiple specialized models from Hugging Face. Given a complex request, ChatGPT breaks it into sub-tasks, assigns each to the best model, and compiles the results (like an AI assembly line). This orchestration allows it to handle multi-modal, complex tasks better than any single model could.

8. Microsoft AutoGen – LLMs Working in Teams

AutoGen, a Microsoft initiative, is a developer toolkit for building collaborative, multi-agent systems. It enables LLM-powered agents–like a “coder” and “tester”–to converse, share tasks, and integrate with real-world tools. By simplifying setup and coordination, AutoGen explores how multiple AI agents can outperform a single one, signaling a more mature phase of agentic AI.

9. SuperAGI – Autonomous Agent Framework (Open-Source)

SuperAGI is an open-source, developer-focused framework for building, running, and managing autonomous AI agents. It provides backend infrastructure—memory, tools, workflows—and supports safe, controllable deployment at scale, from sales outreach automation to custom business processes. By 2025, it’s part of a broader push to turn agentic AI from demos into practical, customizable “AI coworkers.”

10. Camel – Multi-Agent Role-Playing for Complex Tasks

Camel is an open-source multi-agent framework that uses a role-playing design to help AI agents collaborate on tasks. Starting with a human-provided idea, it uses an LLM-based task specifier to create detailed instructions and assign roles, then orchestrates a cooperative “role-play” dialogue between agents to solve the problem. This approach makes Camel a testbed for studying how AI “teams” break down complex challenges, coordinate autonomously, and potentially scale in larger AI ecosystems.

How TextExpander Enhances Agentic AI Workflows

TextExpander is a text expansion tool that stores snippets of frequently used text and allows you to insert them anywhere with a brief shortcut.

In agentic AI workflows–where AI systems autonomously draft emails, compile reports, or generate content–TextExpander acts as a productivity booster for both humans and AI assistants. By using short abbreviations to expand common phrases or templates, it speeds up repetitive writing tasks and ensures consistency.

For example, instead of retyping the same instructions or email replies over and over, a user (or AI agent) can type a shortcode and instantly “plop” in a pre-written answer. This content reuse not only saves time but also maintains a uniform tone and format in communications.

In broader AI-driven processes, TextExpander complements AI by providing reliable, pre-vetted text snippets. An AI agent can focus on the creative or analytical parts of a task, while TextExpander handles the boilerplate. This leads to faster task execution and fewer typos or inconsistencies.

In fact, text expansion tools help standardize outputs. For instance, ensuring every AI-generated prompt or message follows a consistent format and wording. They can rapidly produce a diverse set of prompts or documents with just a few keystrokes, all formatted correctly and error-free, significantly reducing editing time.

In essence, TextExpander streamlines communication by marrying human-approved content with AI efficiency. The result is an agentic workflow where repetitive text tasks are handled instantly and accurately, freeing up humans (and AI) to tackle more complex or creative work.