10 Best AI Agents for Business Automation (2025)

AI agents for business automation are software programs powered by artificial intelligence that can autonomously perform tasks, make decisions, and interact with systems or people to streamline operations. In essence, they function like virtual employees or assistants: observing information, deciding on actions, and executing tasks with minimal human oversight. These agents can handle a wide range of business processes – from answering customer queries to updating databases – helping companies save time and reduce manual effort.

Demand for AI Agents in Business

Demand for such AI-driven automation is surging. In a 2024 survey of large enterprises, 82% of companies said they plan to integrate AI agents within the next 1–3 years to drive efficiency and free workers from repetitive tasks.

Recent statistics also underscore the rapid growth and impact of AI automation. Businesses that have deployed AI agents report significantly improved operations – 90% of companies using AI agents say they have smoother workflows, with employees experiencing over a 60% boost in efficiency on average.

The market for AI agents is expanding at an extraordinary pace as well. Analysts project it will grow from about $5 billion in 2024 to over $47 billion by 2030, reflecting an annual growth rate above 45%. In short, AI agents are quickly moving from experimental to essential, helping organizations automate complex workflows, augment their teams, and achieve new levels of productivity and scale.

Top 10 AI Agents for Business Automation

1. Botpress

Botpress is an all-in-one platform for building AI conversational agents that use advanced language models. It enables companies and developers to easily create, deploy, and manage intelligent chatbots for customer service, sales, HR, and more. The platform provides a rich visual interface and tooling to design conversation flows and integrate AI, so you can automate dialogues and workflows that traditionally required human agents.

Botpress offers a visual drag-and-drop chatbot builder (the AI Agent Builder) for designing conversation logic and behavior without heavy coding. Under the hood, it integrates with LLMs – you can plug in providers like OpenAI or Anthropic – so that your bot can interpret user inputs and generate fluent, context-aware responses.

It also includes a Knowledge Base module to feed your bot proprietary data or FAQs, and “Bot Tables” for managing structured information the bot might use. Developers have the flexibility to extend Botpress via its APIs and open-source SDK, while non-technical users appreciate the user-friendly studio and pre-built templates.

Key features:

• No-code visual dialog builder: Easy to design conversations and workflows.
• Multi-LLM support: (OpenAI, Anthropic, HuggingFace, etc.) to power natural language understanding.
• Seamless integrations: Integration with messaging platforms (Slack, WhatsApp, Telegram) and business apps (CRM systems).
• Knowledge base and database connectors: Give the bot context from your documents or data tables.
• Strong developer community and SDK: Originally open-source, with extensibility for custom code when needed.

Read Review →

Visit BotPress →

2. Relevance AI

Relevance AI is a no-code AI workforce platform that enables businesses to create, manage, and deploy AI-powered agents across various functions. Think of it as building a team of AI coworkers: you can spin up agents for sales, marketing, operations, customer support, or other roles, and have them work autonomously or collaboratively on your workflows.

The platform’s goal is to make advanced AI automation accessible to non-technical users – allowing companies of all sizes to leverage AI agents to handle routine tasks, communications, and data processing with an all-in-one solution.

Relevance AI provides an intuitive drag-and-drop interface to design AI agents and their workflows (no coding required). Users can either customize agents from scratch or start from Relevance’s pre-built agents tailored to common business needs – for example, an AI Sales Rep that automates lead outreach and follow-ups, or an AI Customer Support Rep that answers FAQs and routes issues.

Each agent can be trained with your data (like connecting your knowledge base or CRM) and configured with specific tasks. The platform emphasizes integrations: it natively connects with business tools such as HubSpot, Salesforce, Google Workspace, Zapier, and more. This means your AI agents can automatically update records, send emails, pull documents, or trigger workflows in your existing software stack.

Relevance AI also allows multiple agents to work together as an “AI team,” and includes monitoring dashboards so you can track performance and refine their skills over time.

Key features:

• No-code AI agent builder: Intuitive visual workflow editor to create agents without programming.
• Multiple ready-made agent templates: (by industry/function) – e.g. AI Sales, AI Marketing, AI Research assistants.
• Seamless integration ecosystem: Connects to CRM (HubSpot, Salesforce), databases, emails, documents, and more for end-to-end automation.
• Agents that learn and adapt: Agents improve from interactions, and you can “coach” them with feedback to refine their performance.
• Collaboration and scaling: Manage a whole team of AI agents, assign different roles, and scale automation as needed while monitoring results.

Read Review →

Visit Relevance AI →

3. UiPath

UiPath is a leading platform in the automation space, traditionally known for Robotic Process Automation (RPA) and now evolving to integrate AI agents into its suite. In UiPath’s vision, software robots (RPA bots) handle repetitive, rule-based tasks, while AI agents tackle the more complex, cognitive aspects of processes.

This combination – which UiPath terms “agentic automation” – allows entire business processes to be automated end-to-end, with AI making decisions and RPA executing precise actions. As an established enterprise automation tool, UiPath offers a robust environment to orchestrate workflows, from simple data entry jobs to multi-step operations that require on-the-fly judgement.

UiPath’s platform includes a range of components – a Studio for designing automation workflows, an Orchestrator for managing and deploying bots, and an AI Center for integrating machine learning models. Recently, UiPath introduced an Agent Builder and Agent Catalog that let developers create AI agents or access pre-built agent templates.

Key features:

• End-to-end process automation: Combines RPA bots and AI agents to handle both rote tasks and decision-driven tasks in one platform.
• Enterprise-grade orchestration: Schedule, monitor, and govern automations at scale with rich control dashboards (critical for compliance and reliability).
• Pre-built integrations and connectors: Vast array of enterprise applications (SAP, Oracle, Salesforce, etc.), plus the ability to record UI actions for legacy systems.
• AI capabilities built-in: Includes AI Computer Vision for UI automation, Document Understanding for OCR, and now generative AI integration for understanding text and building automations (Autopilot interface).
• Developer-friendly & community: Offers extensive documentation, a large community forum, and even a marketplace (UiPath Go!) with reusable automation components.

Visit UiPath →

4. Microsoft Copilot Studio

Microsoft Copilot Studio is the tech giant’s latest platform for building AI agents. Aimed at enterprise users, Copilot Studio allows organizations to design and deploy custom conversational AI agents that use Microsoft’s generative AI and connect deeply with the Microsoft 365 and Azure ecosystem.

Essentially, it’s Microsoft’s answer to the no-code chatbot builder, now turbocharged with the same AI that powers Bing Chat and Microsoft 365 Copilot. Businesses can use it to create assistants for things like IT helpdesk bots, customer support chatbots, or employee self-service assistants on Teams.

Copilot Studio provides an intuitive graphical interface where you can define an agent’s behavior. You can start by specifying the agent’s knowledge sources and “grounding” – for example, you might point it to your SharePoint documents or website FAQs so it has domain knowledge. Microsoft’s generative AI then allows you to add capabilities by simply describing them in natural language.

Key Features:

• Graphical Bot Builder: Drag-and-drop interface for defining conversation flows, triggers, and responses.
• Generative AI-Powered Dialogues: The agent can use Microsoft’s generative AI (GPT-4 based) to produce natural, context-aware answers.
• Knowledge & Action Integration: Easy connection to knowledge bases (SharePoint, websites, Q&A) and business applications via over 1,000 Power Platform connectors.
• Multi-Channel Deployment: Native integration with Microsoft Teams, plus options to deploy to web chat, mobile apps, or other channels.
• Enterprise Governance & Security: Centralized admin center for bot management, with role-based access, auditing (through Microsoft Purview), data loss prevention, and compliance controls.

Visit Copilot Studio →

Google’s Vertex AI platform includes a powerful Agent Builder that enables organizations to create conversational AI agents using Google’s models and infrastructure. This platform is used to develop chatbots and voice bots that can handle customer inquiries, guide users through processes, or even perform simple transactions.

As part of Google Cloud’s Vertex AI, it brings together Google’s expertise in natural language (from Search and Assistant) with a user-friendly development experience. Businesses have applied Google’s AI agents for everything from customer support and employee HR bots to voice-activated assistants in call centers and even predictive analytics conversations.

Vertex AI’s agent builder allows you to create an agent by providing natural language instructions or examples of the conversations you want it to handle. For instance, you can type, “An agent that helps users track their order status,” and the system will suggest an initial dialog setup. Underneath, it utilizes Google’s foundation models to parse intents and contexts from user utterances.

Key Features:

• Natural Language Agent Creation: Allows users to build agents by describing tasks in plain language, lowering the barrier to designing conversation flows.
• Access to Google’s AI Models: Offers integration with Google’s powerful language models and tools.
• Multi-Agent Orchestration: Supports connecting multiple specialized agents or modules, enabling complex workflows.
• Testing & Training Tools: Provides simulators and analytics to test agent responses and improve them, plus support for training custom ML models.
• Enterprise Security & Compliance: Comes with enterprise-grade security, encryption, and compliance certifications (HIPAA, SOC 1/2/3, etc.)

Visit Vortex AI →

Salesforce’s Agentforce platform is a generative AI assistant embedded within the Salesforce Customer 360 suite (Sales Cloud, Service Cloud, Marketing Cloud, etc.). It’s essentially Salesforce’s native AI agent designed to help sales reps, support agents, marketers, and analysts by automating tasks and providing AI-driven recommendations inside the CRM.

For example, Agentforce can be used to draft email responses to customers, auto-summarize sales opportunities, answer complex questions about your Salesforce data, and even take actions like updating records or creating follow-up tasks using natural language commands.

Agentforce comes with seven out-of-the-box agents tailored to key domains like sales, service, marketing, commerce, analytics, etc., each pre-trained on common tasks in that domain. For instance, a Service agent can auto-suggest knowledge base articles to a support rep or even directly answer a customer’s question in a chat.

Key Features:

• Native CRM Integration: Full access to CRM context to answer questions and perform updates using your real customer data, in real time.
• Multi-Domain AI Agents: Provides pre-built AI agents for sales, service, marketing, commerce, and analytics tasks, each designed to autonomously assist in that domain.
• Conversational Data Manipulation: Users can use natural language to query data (like an AI-powered BI tool) and also to create or modify records.
• Generative Content & Answers: Able to draft emails, knowledge articles, campaign copy, case responses, and more, using company data to ground the content.
• Trustworthy and Secure: Built with Salesforce’s enterprise security – it uses only your org’s data (no public data unless you integrate a source), honors user permissions, and maintains data privacy.

Visit Agentforce →

Ada is a leading AI customer service automation platform, known for its AI chatbots that help enterprises deliver instant support to customers at scale. If you’ve ever used an online chat where the agent is helping with your issue before a human steps in (or sometimes without any human at all), there’s a good chance Ada’s technology was behind it.

Ada’s focus is on no-code creation of customer service bots, enabling CX teams to build an AI agent that can resolve a large chunk of inquiries across chat, web, mobile, and even voice channels.

Ada provides a user-friendly dashboard where non-technical users (like customer support managers) can design the chatbot’s knowledge base and conversation flows. At its core is Ada’s “Reasoning Engine™,” which combines natural language processing, a knowledge lookup system, and integrations to perform actions.

When setting up Ada, you feed it with your relevant customer service content: FAQs, help center articles, product info, etc. Ada’s platform allows you to connect to various data sources and backend systems (for example, hooking into your e-commerce database or CRM) via API, often without coding.

Key Features:

• No-Code Chatbot Builder: User-friendly interface for creating conversation flows and managing the knowledge base – designed for CX teams to use without programming.
• Natural Language Understanding: Ada’s NLP accurately interprets customer questions (in over 50 languages).
• Generative AI + Retrieval Hybrid: Ada’s Reasoning Engine uses a combination of knowledge retrieval and LLMs to formulate answers.
• Backend Integrations: Offers integrations into CRM, e-commerce platforms, payment systems, etc.
• Omnichannel & 24/7 Operation: Deployable on web chat, mobile apps, social messengers, and phone (voice) with consistent performance.

Visit Ada →

Conversica is a pioneer in AI-driven sales and marketing assistants. Its flagship AI Sales Assistant is like having a virtual sales development representative who can reach out to leads, engage them in natural two-way conversations, nurture them over time, and flag the hot opportunities for your human sales team.

Conversica coined the term “Revenue Digital Assistants™” (RDAs) for its AI personas spanning sales, marketing, and customer success. These assistants primarily communicate via email (and increasingly SMS or website chat), handling tasks such as following up with new leads immediately, re-engaging stale leads, upselling existing customers, or collecting feedback.

Conversica’s platform comes with many pre-written conversation templates and AI models trained on business communications. When setting up an AI assistant, you choose the type (e.g., inbound lead follow-up, event attendee follow-up, customer renewal outreach) and then customize parameters like your product name, the cadence of messages, and any specific qualifiers you care about.

Key Features:

• AI Sales & Marketing Assistants: Offers virtual assistants specialized for sales (lead follow-up, demo scheduling), marketing (event lead nurture), and customer success (renewals, cross-sell outreach.
• Natural, Human-Like Conversations: The AI engages in two-way email or chat dialogues that feel personal.
• Out-of-the-Box Playbooks: Comes with a library of conversation flows and messaging templates that align with common business scenarios.
• Intent Recognition & Learning: The assistant can recognize dozens of intents (e.g., interested, not now, wrong person, unsubscribe) and handle them.
• CRM/MarTech Integration: Seamlessly integrates with systems like Salesforce, HubSpot, Marketo, etc.

Visit Conversica →

Kore.ai is an enterprise conversational AI platform that enables organizations to build, deploy, and manage AI agents across a wide range of use cases. It’s a no-code/low-code platform with a comprehensive suite of tools – from dialog builders and NLP training to integration and analytics – making it a one-stop shop for large companies’ AI assistant needs.

Kore.ai supports both customer-facing bots (for support, banking, retail, etc.) and employee-facing bots (HR assistant, IT helpdesk, knowledge management bots), and is known for its strong multilingual and omnichannel capabilities, as well as voice AI support.

At the core of Kore.ai is the Agent Platform, which provides a visual bot development studio. Users start by defining the dialog tasks – basically, what the bot should be able to do or answer. For each task, you can design the conversation flow using a drag-and-drop Dialog Builder, where you lay out the steps, prompts, and transitions (similar to flowcharting a conversation).

The platform’s NLP engine lets you train the bot to understand various phrasings for each intent; you can either use Kore’s ML models or bring your own AI model. A standout feature is Kore.ai’s Marketplace of templates: over 400+ pre-built bots and components for common domains (like a banking bot template) which you can import and adapt.

Key Features:

• Unified No-Code Development Studio: Browser-based interface to design dialog flows, define intents/entities, and manage content.
• Pre-built Templates & Skills: Extensive library of pre-built virtual assistant templates and industry-specific skills that can be imported to jumpstart development.
• Omnichannel & Voice Support: Deploy chatbots across web, mobile, messaging apps, and voice/IVR with consistent behavior.
• Enterprise Integration & Actions: Connectors and APIs to integrate with backend systems (CRM, ERP, databases).
• Security, Governance, and Analytics: Enterprise-grade security features (SSO, user roles, encryption) and a central console to monitor bot performance.

Visit Kore →

ThoughtSpot Spotter is a conversational analytics AI agent that turns data analysis into a simple dialogue. Launched in late 2024, Spotter is embedded within ThoughtSpot’s cloud analytics platform, enabling users to ask questions of their business data.

Spotter builds on ThoughtSpot’s existing search-driven analytics engine, which was already capable of answering BI questions with a Google-like search experience. With Spotter, instead of just typing keywords, users can have a conversation. You might start with a broad question: “Show me the sales trend for 2024,” and Spotter will generate a time-series chart and narrative.

Then you can ask a follow-up, “What caused the spike in July?” – Spotter remembers the context (sales trend chart) and uses AI to dig into possible drivers. This contextual awareness is a leap beyond typical query tools. Under the hood, Spotter uses LLMs to interpret the intent of the question and translate it into the necessary analytical query on ThoughtSpot’s in-memory database.

It can also use AI to decide which visualization best represents the answer (maybe a bar chart for comparison, or a line for trends). After giving an answer, Spotter might proactively suggest, “Would you like to see a breakdown by product category?” – these autonomous suggestions come from Spotter’s understanding of common analysis paths and the data schema.

Key Features:

• Conversational Data Querying: Users can ask questions in natural language and have a back-and-forth dialogue to refine questions or drill deeper.
• Automated Insight Generation: Spotter doesn’t just answer what you ask – it also surfaces related insights or follow-up questions.
• Dynamic Visualization: The agent generates charts, graphs, or tables appropriate to the query result on the fly.
• Natural Language Narratives: In addition to numbers and charts, Spotter provides narrative explanations in plain English, translating data points into understandable statements.
• Secure, Real-Time Data Access: Since it’s part of ThoughtSpot, it works on live enterprise data with enterprise security.

Visit ThoughtSpot →

How to Choose the Right AI Agent Platform

The selection of AI agents in 2025 is rich and varied, ranging from general-purpose platforms that let you build virtually any kind of agent to domain-specific solutions optimized for particular business functions. The options we explored here are all powerful, but the best choice for your organization will depend on your unique needs, technical environment, and strategic goals.

Platforms like Botpress, Kore.ai, and Relevance AI offer broad flexibility – they are ideal if you want to craft custom AI assistants tailored to multiple use cases or proprietary processes. They often require more initial design effort but reward you with a solution precisely fit to your business (and they support no-code development to empower your internal teams).

On the other hand, industry-focused agents like Ada for customer service or Conversica for sales come with a lot of expertise built-in. These can be faster to deploy in their respective domains and may yield quick wins (e.g., immediate reduction in support volume or faster lead conversions) because they’ve already solved the common challenges in that area. Similarly, if you heavily use a specific ecosystem – say you run your operations on Salesforce or Microsoft – leveraging Einstein Copilot or Microsoft’s Copilot Studio could be efficient, as those will naturally integrate with your existing workflows and data with minimal friction.

Key Factors to Consider

When choosing a platform or agent, consider several key factors. First, identify the primary problem you want the AI agent to solve: is it customer support triage, lead nurturing, internal data analysis, or something else? Look for a solution that excels in that arena.

Second, weigh your team’s technical capabilities and desire for control. A no-code platform empowers your business users to drive the project, whereas a more extensible platform might require some developer input for advanced customization but can adapt more deeply to complex requirements.

Third, consider your integration and data needs. Ensure the platform can securely connect to your data sources and applications. Pricing and scalability are also important practical considerations as well: platforms with free trials or freemium tiers allow experimentation before you commit. Enterprise-focused solutions might require a larger investment, but they could bring robust support, compliance, and reliability that high-stakes deployments demand.

Finally, keep future growth and AI trends in mind. The field of AI agents is evolving very quickly – features like multi-agent collaboration, more autonomous decision-making, and improved learning are on the horizon. You’ll want a platform and partner that is continuously innovating and can incorporate new advancements (such as improved language models or multimodal capabilities) into their offering.

Also, think about governance: as you deploy AI agents, establish policies for monitoring their performance, handling errors or escalations, and maintaining the quality of their output. Choosing a platform with strong analytics and oversight tools will help you keep the AI aligned with your business objectives and values.

The best AI agent platform is one that strikes the right balance for you between ease of use, domain fit, and customization. By clearly understanding your needs and using the comparison criteria we’ve discussed – capabilities, integrations, cost, and control – you’ll be well-equipped to select a solution that delivers real value.