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10 Best AI Agents for Business Automation (2025)

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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.

Comparison Table of Best AI Agents for Business Automation

AI ToolBest ForPriceFeatures
BotpressCustom chatbots$89/moNo-code visual builder
Relevance AIAI workforce$19/moPre-built agent templates
UiPathEnterprise automationFreeRPA bots w/ AI decision agents
Microsoft Copilot StudioMicrosoft 365 automation$30/moIntegration w/ Microsoft 365 & Teams
Google Vertex AIChat/voice botsPay-as-you-goAgent creation + Google LLMs
Salesforce AgentforceCRM usersSee belowCRM-native agents
AdaEnterprise customer supportCustomNo-code CX chatbot
ConversicaSales leadCustomTwo-way email conversations
Kore.aiAssistantsCustom400+ pre-built templates
ThoughtSpot SpotterData analyticsSee belowConversational data analysis

Top 10 AI Agents for Business Automation

1. Botpress

How to Build One AI Agent and Sell it to Multiple Customers | Build Your Business

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.

Pros and Cons

  • Easy no-code builder: Intuitive visual interface to create conversation workflows.
  • Flexible LLM integration: Plug in your choice of AI model (OpenAI, Anthropic, etc.) for language understanding.
  • Rich integrations: Connectors for chat channels and enterprise apps enable end-to-end workflow automation.
  • Community & extensibility: Active developer community, open-source roots, and SDK for custom extensions.
  • Learning curve for complex logic: Designing advanced dialogs or custom code may require some technical skill.
  • Limited templates: Fewer pre-built industry-specific bots compared to some competitors, so more design effort may be needed.
  • Usage costs at scale: While there’s a free tier, heavy usage of LLM calls and add-ons can become pricey as you scale up.

Pricing

  • Free tier: Pay-as-you-go model with a free monthly credit ($5) for AI usage.

  • Plus plan: ~$89/month (includes higher limits, remove branding, live agent handoff).

  • Team plan: $495/month (for collaboration features and larger deployments).

  • Enterprise: Custom pricing for unlimited scale and premium support.

Read Review →

Visit BotPress →

2. Relevance AI

Building AI Agents with 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.

Pros and Cons

  • No-code agent builder: Business users can design agent workflows visually, lowering the barrier to implement AI automation.
  • Multiple ready templates: Library of pre-designed agents by function (sales, marketing, support, etc.) accelerates deployment.
  • Strong integrations: Connectors to CRM, email, databases, and more – enabling agents to operate within your existing software stack.
  • Collaborative “AI team”: Supports running many agents simultaneously and allows them to pass tasks or data between each other.
  • Early-stage platform: As a newer solution, some advanced enterprise features (fine-grained controls, complex logic) are still maturing.
  • Credit-based usage: The free tier is limited (100 AI credits/day), so sustained heavy use requires paid plans.
  • Potential customization limits: Very specialized processes might require features beyond the no-code interface, needing custom work or waiting for platform updates.

Pricing

  • Free tier: ~100 AI credits per day and 1 user.

  • Pro plan: $19/month for 1 user (10,000 credits/month, larger knowledge base capacity).

  • Team plan: $199/month for up to 10 users (100,000 credits/month, priority support, premium integrations).

  • Business plan: $599/month with higher limits (300,000 credits) and unlimited users; Enterprise plans are custom.

Read Review →

Visit Relevance AI →

3. UiPath

UiPath AI Experts: What is agentic automation?

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.

Pros and Cons

  • End-to-end automation: Combines RPA and AI seamlessly – ideal for complex processes that need both deterministic and cognitive automation.
  • Enterprise-grade platform: Scalable orchestration, security, role-based access, and auditing tools suitable for large organizations.
  • Extensive integrations: Pre-built connectors for hundreds of apps (SAP, Oracle, Salesforce, etc.) and the ability to record user interface actions for legacy systems.
  • Community & support: Large user community, extensive documentation, and a marketplace (UiPath Go) with reusable components.
  • High cost for full suite: Enterprise licensing can be expensive; smaller businesses might find the pricing (hundreds to thousands per month per bot/agent) a barrier.
  • Steeper learning curve: Non-technical users may need training to design complex automations; the Studio tool, while visual, has many features to master.
  • Primarily enterprise-focused: Optimized for larger environments – may be overkill for very simple needs or SMB use cases.

Pricing

  • Community Edition: Free for individual users/developers (limited bots and features).

  • Enterprise plans: Subscription licenses start around $420/month for a basic automation developer seat; unattended robot bot licenses can run ~$1,380/month and up.

  • Cloud SaaS packages: UiPath Automation Cloud offers bundled plans (e.g. Automation Team license ~$1,930/month) – these include multiple bots and services.

Visit UiPath →

4. Microsoft Copilot Studio

Microsoft 365 Copilot | Copilot Studio agent builder

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.

Pros and Cons

  • Deep Microsoft 365 integration: Ideal for organizations already using Office 365, Teams, SharePoint, etc. – agents can seamlessly interact with your files, emails, calendars, and more.
  • Generative AI powered: Uses GPT-4 through Microsoft’s Azure OpenAI service, enabling very natural, context-aware conversations and responses.
  • Low-code design: Drag-and-drop interface plus natural language prompts to define agent behavior (no heavy coding required for most scenarios).
  • Enterprise security & compliance: Built-in enterprise-grade security (data stays within your tenant), compliance logging via Microsoft Purview, and governance controls for deploying agents company-wide.
  • Microsoft-centric: The platform is mainly beneficial if your data and workflows live in the MS ecosystem; integration with non-Microsoft systems might be less straightforward (unless those systems have connectors).
  • Additional licensing costs: Requires Microsoft 365 Copilot licensing ($30 per user/month add-on) and/or Azure pay-as-you-go for messages – this can add significant cost for large deployments.
  • New technology: As a newer offering (launched in late 2023), features like complex multi-turn reasoning or non-English support may still be in active development.

Pricing

  • Microsoft 365 Copilot license: $30 per user/month (for Office apps Copilot; includes Copilot Studio usage within M365 apps for that user).

  • Message consumption: $0.01 per message (pay-as-you-go) or purchase message packs (25,000 messages for $200/month) for agents deployed to channels like Teams or custom apps.

  • Azure OpenAI costs: If using custom GPT model instances or other Azure AI services, additional Azure usage fees apply.

  • Note: Existing M365 enterprise customers can start experimenting with Copilot Studio at no charge beyond their Copilot license, up to certain usage limits.

Visit Copilot Studio →

5. Google Vertex AI

Build and deploy generative AI agents using natural language with Vertex AI Agent Builder

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.

Pros and Cons

  • Natural language setup: Lowers the barrier to create bots by allowing you to “describe” the bot in plain English and let AI generate parts of the conversation logic.
  • Access to Google’s AI tech: Agents can utilize Google’s top-tier language models, speech recognition, and other AI services (plus the upcoming Gemini model).
  • Scalable cloud infrastructure: Built on Google Cloud, so it can scale to enterprise workloads and offers reliability, security (compliance certifications like HIPAA, SOC2, etc.), and integration with other Google Cloud services.
  • Multi-agent and multi-channel: Supports having different agents or “skills” that hand off to each other, and can deploy to chat, voice IVR, mobile apps, or web.
  • Requires GCP expertise: Setting up and optimizing Vertex AI agents may require familiarity with Google Cloud console, IAM permissions, etc., which might be a hurdle for non-technical teams.
  • Pay-per-use costs: Pricing is usage-based (roughly $12 per 1000 interactions, plus any additional services) – costs can accumulate with heavy use, so careful monitoring is needed.
  • Less domain-specific out of the box: Compared to niche platforms (like those dedicated to customer service or sales), Vertex AI provides general tools; you may need to put in more training data or design effort for domain-specific excellence.

Pricing

  • Free trial credit: New Google Cloud customers get $300 free credits (and an additional one-time $1,000 credit specifically for Vertex AI Agent Builder is often available to experiment).

  • Usage pricing: Approximately $12 per 1000 text interactions (messages) processed by the agent. Optional features like using external web search in responses cost extra (~$2 per 1000 queries).

  • Other costs: If your agent uses other Google services (for example, Dialogflow CX, or accesses a Vertex AI foundation model, etc.), those are billed per their service rates.

  • Scaling: Pay-as-you-go means you pay only for what you use; enterprises can negotiate committed-use discounts for large volume.

Visit Vortex AI →

6. Salesforce Agentforce

What Is Agentforce? | Salesforce

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.

Pros and Cons

  • Seamless Salesforce integration: Because it’s built into Salesforce, the agent has full access to your CRM data, context, and can directly perform CRM actions (update records, create tasks) with appropriate permissions.
  • Pre-built domain expertise: Agents come with industry-specific or role-specific knowledge (sales processes, customer support workflows) reducing the setup needed.
  • Conversational data analysis: Users can ask questions about their Salesforce data in natural language (like an AI-powered analytics query) and get insights or reports instantly.
  • Generative content and responses: The AI can draft emails, knowledge articles, or marketing copy that users can then refine – saving significant time on communications.
  • Salesforce-dependent: Only useful if your organization is already a Salesforce customer and keeps data there – it’s not a standalone product you can use outside Salesforce’s environment.
  • Consumption-based cost: Priced roughly at $2 per AI conversation/interaction, which can add up quickly in high-volume use cases (though basic usage may be included in some editions).
  • Data privacy scope: While Salesforce emphasizes trust (your data isn’t mingled with others), organizations must still carefully govern what data the AI agent can access or generate to avoid any incorrect outputs on critical customer info.

Pricing

  • Included starter: Limited free AI usage for existing customers.

  • Per conversation pricing: Approximately $2 USD per conversation or interaction beyond the free allotment. (A conversation is typically one session with an AI agent handling a query or task.)

  • Enterprise packages: Large customers can negotiate for Einstein/Agentforce usage as part of their contract. Additional costs may apply for advanced features or higher volumes.

Visit Agentforce →

7. Ada

Free Trial Demo | Ada's AI Agent for customer service

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.

Pros and Cons

  • User-friendly for CX teams: Truly no-code – customer support managers can update bot content and flows without developer help, which means the AI stays up-to-date.
  • Multilingual NLP: Ada’s AI can accurately interpret customer questions in 50+ languages, useful for global companies.
  • Hybrid generative approach: Uses a mix of your curated knowledge and GPT-like generation, resulting in answers that are both accurate and well-phrased.
  • Integration capabilities: Offers integrations to CRM, ticketing systems, e-commerce platforms, payment gateways, etc., allowing the chatbot to complete transactions or hand off to humans when needed.
  • Enterprise pricing: Ada’s solutions are tailored for mid-to-large enterprises and pricing isn’t transparent (typically custom quotes in the thousands per month), which may be too costly for small businesses.
  • Setup and training effort: While no-code, getting the most out of Ada requires feeding it a comprehensive knowledge base and continuously refining content – a commitment from the CX team.
  • Primarily support-focused: Ada excels at customer support use cases; it’s less of a general-purpose AI agent builder (you wouldn’t use Ada to build, say, a sales agent or an internal workflow bot).

Pricing

  • Pricing model: Custom/Enterprise – Ada does not publicize fixed plans. Costs typically scale based on usage (number of conversations or customers) and the level of features.

  • Estimated range: Reports indicate pricing can range from $4,000 to $10,000+ per month for enterprise deployments, depending on complexity and volume.

  • Free trial: Ada may offer a guided trial or demo environment on request, but there’s no free tier for full use.

  • Performance-based option: Ada has introduced performance-based pricing where cost can tie to outcomes (e.g., number of tickets resolved by AI), to align pricing with value delivered.

Visit Ada →

8. Conversica

Conversica: AI-Powered Conversation Automation for Revenue Teams

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.

Pros and Cons

  • Specialized for revenue teams: Comes with proven conversation flows for sales (lead qualification, demo scheduling) and marketing (event follow-ups, webinar leads), reducing trial-and-error in messaging.
  • Human-like two-way emails: The AI’s emails are polite, persistent, and surprisingly human – many leads don’t realize they’re talking to an AI, which can lead to higher response rates.
  • Intent recognition: Advanced ability to interpret email replies (e.g., filtering out out-of-office or referrals vs. genuine interest) and handle them appropriately without human intervention.
  • CRM integration: Seamlessly works with sales CRM systems – when the AI qualifies a lead, it can update fields or create tasks, ensuring salespeople see the context and can take over smoothly.
  • High cost: Conversica is an enterprise solution with pricing reportedly starting around $3,000 per month and scaling up with the number of AI assistants and contacts – a significant investment largely suited for companies with large lead volumes.
  • Email-focused: Historically email is the primary channel (though SMS and web chat have been added); organizations looking for a multi-channel conversational AI might find it somewhat limited.
  • Limited outside sales/marketing: This platform is not a general chatbot builder – it’s purpose-built for revenue generation workflows. It won’t help much for use cases like IT support or operations automation.

Pricing

  • Subscription model: Pricing is custom and typically annual.

  • Scaling factors: Costs will increase with additional AI personas (for example, separate assistants for sales vs. customer success), higher lead volumes, or multiple languages/regions.

  • No free tier: There’s no free plan; Conversica usually offers a pilot program or proof-of-value engagement to test the waters.

Visit Conversica →

9. Kore.ai

Revolutionizing Automation with AI Agentic Apps

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.

Pros and Cons

  • Unified development environment: One platform to design dialogs, manage NLP training, integrate data, and analyze performance – suitable for large projects with multiple bots.
  • Rich pre-built assets: The extensive library of industry-specific templates and conversational skills reduces reinventing the wheel for common use cases.
  • Omnichannel deployment: You can build the bot once and deploy it across web chat, mobile apps, SMS, popular messaging apps, voice channels (telephony/IVR), and even smart speakers with consistent behavior.
  • Enterprise-ready features: Supports on-premise or private cloud deployment, strong security (SSO, encryption), user role management, version control, and detailed analytics for governance.
  • Complexity: With great power comes complexity – Kore.ai’s platform can be overwhelming for newcomers. It may require a dedicated team (or Kore.ai’s professional services) to fully leverage all capabilities.
  • Cost is high-end: Pricing is not publicly disclosed and is typically on the higher side, targeting enterprises. Smaller organizations might find it beyond budget, especially for simple chatbot needs.
  • UI and UX customization limits: While you can design conversations, the out-of-the-box chat interface might need additional web development to perfectly match your brand’s UX beyond what the platform settings allow.

Pricing

  • Enterprise licensing: Custom quotes based on number of virtual agents, messages, and additional modules.

  • Cloud/SaaS vs On-Prem: Pricing can also depend on whether you use Kore.ai’s cloud or deploy on-premise.

  • Free trial: Kore.ai often provides a trial or developer sandbox for evaluation, but full deployment requires a paid plan.

Visit Kore →

10. ThoughtSpot Spotter

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.

Pros and Cons

  • Conversational data exploration: Users can simply ask questions in natural language, and Spotter handles the heavy lifting of interpreting intent and formulating the correct database query.
  • Automated insights: Spotter doesn’t just answer exactly what you ask – it often provides additional commentary or follow-up suggestions, surfacing insights you might not have thought to ask (e.g., highlighting an outlier or a relevant comparison).
  • Dynamic visualization selection: The agent chooses how to best display the answer – whether it’s a line chart for trends, a bar chart for comparisons, or a table for detailed data – and it can switch on the fly if you refine your question.
  • Narrative explanations: Beyond numbers and charts, Spotter generates written explanations in plain English, translating data into understandable statements for decision-makers.
  • Requires ThoughtSpot platform: Spotter is a feature of ThoughtSpot’s analytics suite, which is an enterprise product. You need to have your data in ThoughtSpot and a subscription; it’s not a standalone AI tool you can apply to arbitrary data sources without ThoughtSpot.
  • Data prep needed: The quality of answers depends on having your data modeled and loaded correctly in the underlying system. Businesses must invest time to set up their data warehouse or cloud data in ThoughtSpot for Spotter to be effective.
  • Enterprise pricing: ThoughtSpot’s pricing (starting at ~$1250/month for the base package) means Spotter is mostly aimed at medium to large organizations with serious analytics budgets.

Pricing

  • Included with ThoughtSpot Cloud: Spotter is included as part of ThoughtSpot’s cloud platform.

  • Scaling: Larger deployments (unlimited users or data) are custom priced. Pro and Enterprise plans are available with increasing data capacity and support.

  • Cloud data costs: Note that ThoughtSpot works on top of cloud data warehouses (like Snowflake, BigQuery, etc.), so you’ll also have those services’ costs.

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.

Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.