I tested over 25 AI agents for business, daily planning, coding, personal use and so much more. These 12 actually stuck. If you want tools that save time and get things done without fuss, start with this list.
What is an AI agent?
An AI agent is a software system that uses artificial intelligence to interact with its environment, collect data, and perform tasks on behalf of a user or another system.
Aside from AI it uses natural language processing (NLP), and decision-making logic to understand your intent and carry out tasks across different tools and platforms.
Unlike simple AI tools that generate content or answer queries, AI agents go further. They can book meetings, summarize documents, fill out forms, update CRM records, or even follow multi-step workflows based on your goals.
You can think of them as digital coworkers who don’t get tired, don’t need reminders, and never miss a step.
Whether it’s managing your inbox, scheduling your day, or automating repetitive work, a good AI agent saves time, reduces friction, and keeps your focus where it matters most.
Top AI Agents in 2025: TL;DR
- Lindy: Best for no-code multi-agent workflows
- IBM watsonx: Best for enterprise GenAI automations
- CrewAI: Best for devs building AI agent teams
- 11x: Best for full-cycle AI sales agents
- Decagon: Best for large-scale AI customer support
- Harvey: Best for automating legal workflows
- Bland: Best for realistic AI phone agents
- Observe.AI: Best for real-time voice agent assist
- Dialogflow: Best for multilingual chatbot creation
- AgentGPT: Best for task-chaining autonomous agents
- Kore.ai: Best for enterprise-wide conversational automation
- AutoGen: Best for building multi-agent AI systems
1. Lindy: Best for no-code multi-agent workflows
What does it do? Lindy lets you build AI agents called Lindies. They automate everything from emails to meeting notes, custom workflows, and beyond.
Who is it for? Perfect for sales, marketing, support teams, founders, or anyone tired of repetitive work.
We built Lindy to be the AI teammate you actually want to work with.
You don’t need to be technical or spend hours learning a new tool.
Just log in, describe what you want done, and a Lindy agent can be up and running in minutes.
You can choose from dozens of ready-made templates or drag-and-drop your custom workflows with zero coding required.
One of our favorite things? Lindy is a master at multitasking.
Lindies can collaborate with other Lindies. This multi-agent approach means you can run complex processes without any hassle.
Like scanning multiple records before a sales call, where one Lindy fetches notes, another can create a pitch deck, and a third tags relevant email threads.
It’s like an internal AI swarm, and everything just works in sync.
You can embed Lindy on your website, let it handle document-based queries with doc chat, or give an email to triage your inbox.
And if you ever want a say before action, you can add a human-in-the-loop step to approve, edit, or oversee.
From calendaring and Slack notifications to full-blown knowledge base automation, Lindy makes AI feel less like a tool and more like a team.
All these extensive features make Lindy the best AI agent for enterprises, businesses, and even personal users.
Pros
- Build no-code AI agents for any task
- Over 2,500 integrations via Pipedream
- Connects more than 4,000 data sources via Apify
- 24/7 support with step-by-step Lindy Academy lessons
Cons
- Some templates still need customization
Pricing
- Free plan: 400 tasks/month, 1M character knowledge base
- Pro ($49.99/month): 5,000 tasks/month, access to call features, 20M character knowledge base
- Business ($299.99/month): 30,000 tasks/month, premium phone call automation, priority support
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2. IBM watsonx: Best for enterprise GenAI automations
What does it do? IBM watsonx builds AI agents to automate workflows, generate responses, and perform tasks using GenAI, ML, and APIs.
Who is it for? Ideal for enterprises automating HR, IT, sales, or sustainability workflows.
I tried Watsonx Assistant while exploring AI agents that do more than just chat. These agents ACTUALLY get work done.
Think of it as a visual workflow builder fused with GenAI brains and a backend engine that plugs into your stack (Salesforce, Slack, Gmail, you name it).
One thing I liked was how quickly you can set up a complete assistant.
Like, I built a carbon footprint estimator in under an hour by dragging in pre-built actions like invoice lookup, data classification, and even auto-generated emails for internal reporting.
Each “skill” felt like a puzzle piece. You snap it in and it does its job, whether it's fetching data, doing calculations, or calling an external API.
You can also add your APIs if they’re not already built-in, which is helpful when working with custom systems.
I like how Watsonx is not just for devs either, once connected, the no-code UI handles most of the logic.
That said, it’s not perfect.
Error messages aren’t always helpful, and if two people work on the same flow, things break. It also lacks power-user tools like array/object manipulation that seasoned devs might want.
But for structured automation + GenAI tasks, it gets the job done.
Pros
- 99% Uptime SLA
- Phone and SMS integration included
- Pipeline comparisons and leaderboards
- GenAI model used: IBM granite-13b-chat
- Voice customization with multi-language support
Cons
- Learning Curve
- Tailored enterprise use can be costly for small teams
Pricing
- Lite (Free): Includes up to 1,000 monthly active users, 3 assistants, 7-day data retention, 1 published version per assistant, and 5-minute session timeout. Access to NLP, webchat, SMS/MMS, and messaging integrations.
- Plus (From $140/month): 1,000+ monthly users, 10 assistants, 30-day data retention, 10 published versions, 24-hour session timeout
3. CrewAI: Best for devs building AI agent teams
What does it do? CrewAI lets you build autonomous AI agents using Python to collaborate on complex tasks.
Who is it for? Ideal for developers and tech-savvy teams building AI-powered task automation.
CrewAI is like assembling a dream team of AI agents, each with its own job title, goal, and tools. But instead of hiring people, you're coding these agents in
When I tested CrewAI, I was honestly impressed by how well it handled complex, multi-step tasks, like building a full Instagram ad campaign.
I simply fed it a product URL (the Galaxy Watch 6), and it spun up two different “crews” of agents.
One handled product research, competitor analysis, and marketing strategy. The other took over with photo planning and review.
You can even run CrewAI agents with access to live web data, scraping tools, and even integrate your own APIs or local LLMs.
Since it's modular, I also defined each agent’s role, skills, and responsibilities. This gives them almost REAL personalities and objectives.
The best part?
Agents can delegate tasks, form feedback loops, and remember past steps using short- and long-term memory.
But this power comes with a learning curve. It’s Python-based, which makes it tricky for non-technical users.
And if you’re running proprietary workflows, you might hesitate since it's open-source and not ideal for handling confidential IP.
Still, for developers who want real agentic AI, this tool feels like magic.
Pros
- Open-source and highly customizable with Python
- Supports hierarchical agent processes and teamwork
- Great for long-form, research-heavy, or marketing automation tasks
Cons
- Requires basic Python knowledge
- Interface can be complex for non-tech teams
Pricing
As of now, CrewAI doesn’t list pricing directly on its official website. There's no dedicated pricing page or public breakdown available. You can still take a free trial or a demo.
4. 11x: Best for full-cycle AI sales agents
What does it do? 11x automates outbound and inbound sales using AI agents for prospecting, lead revival, and appointment booking.
Who is it for? Ideal for enterprise sales teams looking to scale outreach and handle leads 24/7.
11x feels less like software and more like hiring two elite sales reps, Alice and Julian.
Alice handles outbound like a pro.
As an agent, it combs through data, identifies high-intent leads, sends personalized messages, revives cold deals, and books meetings.
You don’t need to build a massive SDR team, and Alice does it all on autopilot.
Julian takes care of inbound leads.
Anytime someone fills a form or shows interest, Julian jumps in instantly. It qualifies them in seconds (literally), handles objections, and routes qualified leads straight to your calendar.
I was surprised at how quickly he responds, FASTER than most human reps.
Together, Alice and Julian create a full-loop sales engine.
You’re not just getting help writing emails; you’re automating the entire sales motion. But it’s priced for serious teams.
If you’re a startup or SMB, the cost might sting a bit. And while the tech’s solid, the platform doesn’t offer a lot of transparency upfront. Most of the specifics are gated behind demos.
Still, if you’re a mid-to-large org tired of chasing leads manually, 11x can be your round-the-clock sales team.
Pros
- Identifies ideal-fit leads by analyzing millions of data signals
- Pulls real-time insights from social and public engagement data
- Personalizes outreach dynamically to match each prospect’s style
Cons
- Lacks resources for newbies
- Limited pricing visibility on the site, gated behind demos
Pricing
11x does not list any public pricing on its official website. Instead, access to pricing details is available only through a personalized demo.
5. Decagon: Best for large-scale AI customer support
What does it do? Automates customer support workflows using AI agents trained on your business knowledge.
Who is it for? Ideal for large enterprises looking to scale customer service without growing headcount.
When I tried out Decagon, it didn’t feel like just another AI support bot.
It felt more like handing off my repetitive support load to a sharp, fast-learning assistant that UNDERSTANDS what’s going on.
Unlike typical chatbots that throw generic answers at customers, Decagon’s agents can fetch account data, follow multi-step flows, and escalate issues with context intact.
What really made it stand out during my testing was their Agent Operating Procedures( AOPs).
These are like blueprints you define with Decagon’s team to match how your real support team operates.
So instead of training the AI from scratch, I just had to walk them through what a typical ticket flow looks like.
The result?
The AI started handling real support tickets with decent accuracy. Even the tone was natural and consistent. I didn’t have to worry about it veering off-brand or giving wrong answers.
I tested it in a simulated high-volume environment and saw it reduce resolution times by almost half.
If your current support stack is messy or spread across multiple tools, Decagon fits in neatly.
Pros
- Easy to set up. No heavy tech skills needed
- Learns from your team and gets better over time
- Works smoothly with tools like Salesforce, Zendesk, and Stripe
- Solves customer issues on chat, email, and calls without human help
Cons
- Fast feature updates can make it hard to keep track
- Niche support scenarios may require custom workarounds
Pricing
Lacks transparent pricing. You can take a demo or go through the case studies to understand Decagon’s use case better.
6. Harvey: Best for automating legal workflows
What does it do? Harvey automates legal research, drafting, and analysis using advanced AI workflows.
Who is it for? Suits law firms and professional service teams handling complex legal work.
Harvey AI isn’t a classic chatbot, you can use it for personal use. At it’s core, Harvey’s bound by its use case in legal industries.
It’s built from the ground up for legal professionals like think law firms dealing with international regulations, contract reviews, or compliance-heavy cases.
When I tried Harvey, what impressed me was how much legal context it understood.
You can upload spreadsheets or client files, and Harvey starts analyzing, citing relevant laws, and even flagging missing information like a trained associate might.
They’ve recently rolled out what they call “workflows,” essentially AI-powered agents that can run legal tasks from start to finish.
I tested one built for global M&A filings.
Harvey INSTANTLY gave me suggestions. It even broke down filing requirements country by country, highlighted what data was missing, and drafted a ready-to-send RFI.
All I had to do was upload some basic spreadsheets.
Harvey isn’t built for general use, since it’s laser-focused on high-stakes legal work.
And it fits neatly into how legal teams already operate, with research built on curated knowledge and the ability to control how much human oversight you want baked in your system.
For firms, Harvey also supports collaborative work between legal teams and AI agents
Pros
- Integrates with Microsoft Word for faster drafting
- Supports over 50 languages and legal systems worldwide
- Uses domain-specific agents to run complex legal workflows
- Can handle questions across over 50 documents for insights
Cons
- Not much public info on features or pricing
- Might feel overwhelming without a structured onboarding
Pricing
Custom pricing based on firm size, usage, and workflow needs
7. Bland: Best for realistic AI phone agents
What does it do? Bland automates phone calls with ultra-realistic AI voice agents that handle customer conversations end-to-end.
Who is it for? Ideal for businesses that rely on outbound or inbound calls, like sales.
Using Bland AI feels like handing off your phone lines to a smart, reliable teammate who’s active 24x7.
I built a few agents myself, and what stood out immediately was how natural the conversations felt.
These AI agents don’t just follow scripts.
They understand context, pull live data from APIs, and respond INTELLIGENTLY when things go off track.
You can design call flows using their “Pathways” builder, which is visual and simple to use. I didn’t need to touch any code to get a fully working agent up and running.
I love how creating global nodes made things much easier.
Like, I set up reusable flows for appointment scheduling and FAQs that could be triggered anytime, without rebuilding them from scratch.
There’s also a useful confirmation step before live transfers. When someone says they want to talk to a human, the agent double-checks if that’s really what they need.
This helped me reduce unnecessary handoffs during testing.
Whether you're dealing with inbound support, outbound follow-ups, or lead qualification, Bland handles it consistently.
It scales well, handles complex logic, and lightens the load on your team without requiring constant oversight.
Pros
- 99.99% uptime
- 24/7 operation ensures constant availability
- Scalable to handle high volumes of concurrent calls
- Self-hosted infrastructure offers better control and security
Cons
- Requires some technical expertise for setup and customization
- Additional fees for features like multilingual support and voice cloning
Pricing
Bland costs you $0.09/minute for calls, billed to the second.
8. Observe AI: Best for real-time voice agent assist
What does it do? Observe.AI improves contact center operations with AI-driven voice agents and real-time agent assistance.
Who is it for? Best for enterprise contact centers aiming to improve customer experience, compliance, and operational efficiency.
I spent a couple of weeks working with Observe.AI, and what stood out immediately was how well it blends automation with real-time agent support.
Initially, I tested its AI voice agents on basic support workflows, and conversations went far better than I expected.
Observe’s AI agents don’t just follow the script.
They adjusted to changes in tone and topic and could guide a conversation to resolution without sounding robotic.
The real-time Agent Assist feature is where things got INTERESTING.
While I was on a mock sales call, it actively nudged me with reminders to mention key benefits, handle objections, and stay compliant.
After the call, the summary was ready almost instantly, with action items and call breakdowns already done.
That saved me the usual 10 minutes I’d spend writing notes and updates.
Observe.AI isn’t a plug-and-play tool, but you still get integrations for tools like Slack and Salesforce.
You’ll need to invest time setting up call flows and training the AI. But once it’s live, it makes a visible dent in how fast, compliant, and focused your team becomes.
Pros
- Handles complex calls with human-like AI agents
- Auto-generates call summaries and post-call notes
- Real-time coaching during live customer interactions
- Integrates with major CRMs and contact center tools
Cons
- Setup may be tricky for non-technical teams
- Requires updates to stay accurate and effective
Pricing
Pricing is custom and depends on your team’s needs. You’ll need to request a demo to get a tailored quote.
9. Dialogflow: Best for multilingual chatbot creation
What does it do? Dialogflow is a Google Cloud platform that enables the creation of conversational agents, such as chatbots and voice assistants, using natural language understanding.
Who is it for? Ideal for developers and businesses seeking to integrate conversational interfaces into their applications or services.
Last week, I built a few conversational agents using Google’s Dialogflow. You can use it to create both chatbots and voice assistants.
But I think these bots work best when you’ve got a clear idea of what each will handle. I had to be very precise with details while setting up my agents.
You define “intents” and “entities,” which means training the bot on what users might say and what details to extract.
There are two versions.
Dialogflow ES (Essentials) for simple bots, and Dialogflow CX (Customer Experience) for more complex, multi-turn interactions.
I’ve used both, and while ES is great for quick FAQ setups, CX is where you want to be if your flow has layers or depends on context.
What stood out to me was how EASILY it integrates with Google Cloud.
You don’t have to think too hard about scaling or uptime; Dialogflow handles it. I also tested it across languages, and the multi-language support held up without much tweaking.
While you get Google’s familiarity, the interface leans more toward technical users.
There’s a bit of a learning curve if you’re new, but once you're in, the control is worth it.
I relied heavily on webhooks to connect with real-time data, and the APIs helped me customize things better.
It’s not the most intuitive setup at first, but the documentation is thorough and the support forums are active.
Once you’ve built a few flows, it clicks.
Dialogflow may not be the flashiest on the surface, but it’s sturdy, flexible, and has serious depth when you need it.
Pros
- Generative fallback enhances response handling
- Visual flow builder simplifies complex conversation design
- $600 credit for a no-charge trial of Conversational Agents (Dialogflow CX)
Cons
- Steeper learning curve for Dialogflow CX compared to ES
- Some advanced features may require additional configuration
Pricing
- Dialogflow ES: Free for basic usage; $0.002 per text request beyond free tier.
- Dialogflow CX: $0.007 per text request; $0.001 per second for audio input/output.
- Generative AI Features: $0.012 per text request; $0.002 per second for audio.
10. AgentGPT: Best for task-chaining autonomous agents
What does it do? AgentGPT lets you create autonomous AI agents that can plan, research, and complete tasks step-by-step.
Who is it for? Great for professionals and teams looking to automate research, analysis, or workflow execution without writing code.
The first time I tried AgentGPT, I didn’t expect much.
It is free, runs in your browser, and doesn’t even ask for a credit card.
But within minutes, it pulled off a task that usually takes me hours: summarizing two public companies’ earnings reports and generating a full SWOT analysis.
I didn’t have to prompt it line by line either. I just gave it a goal: compare Verizon and AT&T, review their quarterly performance, and benchmark them against the telecom sector.
That’s it.
The agent broke down the steps, ran them in order, and came back with structured insights, all without me lifting a finger after the initial input.
Then I built a budget planner using a template. It had spending limits and saving tips ready in seconds.
SIMPLICITY is what makes AgentGPT different from your typical chatbot.
Instead of replying to prompts one at a time, it works toward a user-defined objective, chaining tasks together, like scraping reports, analyzing patterns, and drafting summaries.
For finance tasks, data research, or competitor breakdowns, it’s incredibly handy.
And let’s not forget the control you get.
I even tweaked the prompts mid-process, guided them with feedback, and saved agents to reuse later.
And it’s beginner-friendly: no setup, no waitlist, no nonsense.
Pros
- Automates multi-step tasks from a single prompt
- Great for market research, analysis, and reporting
- No sign-up or setup required to get started
Cons
- Can loop endlessly if goals aren’t clearly defined
- Limited control over fine-tuned execution unless you prompt precisely
Pricing
AgentGPT is free to use with optional OpenAI API integration for advanced use cases.
Premium plans or features may be introduced later, but core features are currently accessible at no cost.
11. Kore.ai: Best for enterprise-wide conversational automation
What does it do? Kore.ai builds enterprise-grade AI assistants to automate customer and employee conversations across voice and digital channels.
Who is it for? Perfect for large enterprises in IT, HR, finance, and customer service looking to streamline interactions at scale.
Kore.ai is an all-in-one enterprise AI platform built to automate workflows and let your teams ACTUALLY count on the AI agents for serious work.
When I tried it, what stood out immediately was how well it balances flexibility with enterprise needs.
You can deploy pre-built agents like HR Assist or build your own AI workflows using their no-code interface.
Think of it as a chatbot builder, with an orchestration layer that ties into your systems (like ServiceNow, Salesforce, etc.) and automates the back-and-forth that typically eats up time.
One of the most underrated features is its “AI for Work” suite.
It acts as a universal orchestrator that doesn’t just answer questions but executes tasks across systems.
I tested it for simple HR queries like PTO(paid time off) balance.
Kore.ai first fetched information and then triggered actions. You can then customize these experiences for specific agent roles, with audit trails and admin controls.
Yes, the learning curve exists if you're scaling from scratch.
But if your company is drowning in support tickets or repetitive internal queries, Kore.ai gives you the infrastructure to build something sustainable, without needing any dev team.
Pros
- Pre-built agents for HR, IT, and CX
- Integrates with CRMs and other AI models
- Unified orchestration across all enterprise apps
- Kore.ai academy and demo videos for beginners
- Workflows for recruiting and onboarding new members
Cons
- SDK customization can feel clunky
- Some tools still favor structured input formats
Pricing
Contact Kore.ai for a demo and custom enterprise pricing.
12. AutoGen: Best for building multi-agent AI systems
What does it do? AutoGen is a framework by Microsoft for building and orchestrating collaborative AI agents that handle complex, multi-step tasks.
Who is it for? Best-suited for developers, researchers, and technical teams building scalable multi-agent AI systems.
AutoGen is more like a toolkit for developers.
If you want AI agents that collaborate, communicate, and solve tasks without constant prompting, AutoGen nails it.
I tested a setup where one agent researched global patent filings, another summarized key innovations, and a third drafted a competitive landscape report.
Once configured, these agents ran the whole workflow end-to-end.
And despite the complex coding setup, I like how you can SIMPLY define these agents, what they do, and how they collaborate.
Whether that’s a single AI working through tasks or multiple agents bouncing ideas and actions between each other.
Setting it up took a bit of familiarity with Python and APIs, but once you're in, it’s modular and surprisingly intuitive for something so technical.
To test this, I created a basic workflow: two agents with one collecting data, and the other analyzing it to generate a report.
What usually took manual scripting was now a reusable pipeline.
AutoGen supports async communication, so tasks can run in parallel. There are built-in logging and debugging tools that make it easier to test what your agents are really doing.
Overall, AutoGen has a solid foundation that’s hard to beat.
Pros
- Modular and fully extensible
- Supports multi-agent collaboration
- Open-source and developer-friendly
- Frequent updates to improve usability
Cons
- Not ideal for non-technical users
- Requires coding and system setup
Pricing
AutoGen is open-source and free to use under an MIT license. But it requires access to external LLMs (e.g. OpenAI), which may incur usage costs depending on the provider.
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How I tested the best AI agent platforms
The best AI agent platform in 2025 is one that:
- Actually saves you time, by automating real workflows like research, scheduling, support, or sales tasks
- Works across tools, not in silos. Think integration with essential tools like Slack, CRMs, APIs, docs, and email
- Handles complexity, whether it’s a multi-agent workflow or a simple repetitive task
To find the ones that deliver, I tested over 30 platforms hands-on, using the same core criteria:
- Workflow Capability & Customization
I gave each tool a real-world task (e.g., create a competitive research report, follow up with leads, summarize support tickets) and watched how well it broke the task down. Could I define logic, control output, add a human-in-the-loop? Tools that worked like digital teammates stood out.
- Integration & Data Handling
Each agent was tested with external tools (Slack, Gmail, Notion, CRMs, calendars). I tracked how well it accessed files, scanned emails, handled APIs, or plugged into workspaces to act with context.
- Ease of Use & Scalability
From drag-and-drop builders to open-source frameworks, I checked how quickly someone (technical or not) could get started. The best tools didn’t just work, they scaled from a single task to an entire team’s workflow.
After weeks of building, tweaking, and running agents, I can confidently say these are the ones worth your time.
Can AI agents really make a difference in your work?
Yes, AI agents can make a difference by offering 24/7 customer support, automating routine tasks, and providing businesses with real-time data on customer behavior.
For example, Lindy lets you build custom AI agents that schedule meetings, triage emails, manage documents, and even coordinate across your apps, all without needing to code. It's like having a smart assistant that actually adapts to how you work.
FAQs
What is the best ai agent?
Lindy is the best AI agent that you can use today without having to code. It's described as the top choice for no-code multi-agent workflows, allowing you to build custom multifunctional AI agents (called "Lindies") that can automate various tasks including emails, meeting notes, and custom workflows. It has over 2,500 integrations via Pipedream and connections to more than 4,000 data sources via Apify, Lindy offers extensive connectivity options. It's honestly "less like a tool and more like a team," making it an easy winner in the list of top AI agents in 2025.
Which businesses benefit the most from AI agents?
Sales, marketing, support, and operations teams benefit the most from AI agents.
But they’re not limited to those areas.
Any business that deals with repetitive tasks, customer communication, or data-heavy workflows can use AI agents to save time, reduce errors, and improve how work gets done.
How are AI agents different from chatbots?
AI agents and chatbots differ in their scope and complexity.
Chatbots are primarily designed for conversational tasks, like answering FAQs or providing customer service, while AI agents can perform more diverse and complex tasks, including making decisions, taking actions, and learning from interactions.
Do AI agents replace human workers?
While AI agents can automate certain tasks and improve efficiency, they are more likely to augment human workers rather than replace them entirely.
AI agents take care of repetitive or time-consuming tasks so your team can focus on work that requires judgment, strategy, or creativity. Think of them as reliable assistants, not replacements.
Are there privacy or security concerns with AI agents?
Yes, but they’re manageable.
Make sure you choose tools with strong security policies, clear data permissions, and enterprise-grade access controls.
Always review how your data is stored and processed, especially if the agent is connecting with CRMs, emails, or sensitive documents.
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