Top agentic AI tools transforming enterprise businesses

If you’re serious about bringing autonomy into your business, you'll want to start with the right agentic AI tools — the ones designed to power complex workflows and drive meaningful results.

Agentic AI tools represent a major leap forward from traditional automation. Instead of relying on rigid scripts or rules, these systems combine large language models (LLMs) for natural-language understanding with a reasoning and orchestration layer that can plan and execute multi-step workflows.

But with the space evolving fast, comparing the best options requires a sharp lens: Which platforms are reliable? Which integrate deeply? And which will actually scale with your enterprise?

The rapid rise of these tools isn’t just about novelty. It’s about necessity. Leaders are tired of automation that breaks easily, too much manual intervention, and fragmented workflows across systems .

Now according to the The New Face of AI Leadership, 78% of enterprises say implementing agentic AI has already led to a significant or total transformation of their operations. Leaders are realizing that real productivity gains depend on agentic AI that can connect the dots instantly, intelligently, and securely. They don’t just automate work — they orchestrate it too. 

That’s why we put together this overview of agentic AI tools, to help you to evaluate solutions, compare capabilities, and try to understand where agentic AI fits in your operations.

Real-world use cases for agentic AI tools

Agentic AI is already reshaping how enterprises get work done. From IT and HR to finance and operations, these systems are driving meaningful efficiency and improving the employee experience by automating the complex, multi-step processes that slow organizations down.

Here are some of the most common use cases across the enterprise:

• HR approvals: Agentic AI can process time-off requests, update employee records, and manage benefits workflows — cutting case volume and response times.
• IT access requests: The AI can verify user roles, manage permissions, and execute multi-step access provisioning with minimal human intervention (where appropriate), helping teams resolve requests faster.
• Compliance nudges: Intelligent agents proactively remind employees to complete required training or submit documentation, helping to improve compliance rates and reduce manual follow-ups.
• Procurement tracking: By connecting procurement systems, chat platforms, and approval workflows, agentic AI provides real-time purchase order updates and enables approvals directly in chat.
• Onboarding workflows: From account creation to device setup, agentic AI coordinates multiple systems to complete onboarding tasks seamlessly — giving new hires immediate access and HR teams back valuable time.

Each of these examples illustrates how agentic AI tools help enterprises streamline everyday work so employees can focus on higher-value priorities. 

As enterprises scale their use of agentic AI, these tools can connect hundreds of workflows, breaking down silos across departments and delivering measurable ROI — from reduced case volumes to improved employee satisfaction. The result is a smarter, faster organization where work flows naturally.

Top features when selecting an agentic AI tool

Choosing the right agentic AI platform is about more than checking boxes. Enterprises need to evaluate how each solution balances autonomy with governance, adaptability with control, and intelligence with security. Below are the key capabilities to prioritize when comparing tools.

Reasoning and decision-making capabilities

The real power of agentic AI lies in its sophisticated decision-making and independent execution skills. These capabilities are crucial because they allow the AI to move beyond simple automation and handle the complexity and unpredictability of real-world business environments.

When comparing agentic AI tools, you need to assess the quality of their reasoning and planning, to enable the tool to reliably perform complex tasks without constant human intervention.

• Smart reasoning systems: They use real-time adaptive logic flows to understand complex requests and determine optimal actions. The reasoning engine is key for independent prioritization and execution.
• Context-aware planning: The AI must formulate plans by integrating business rules and goals, ensuring actions are compliant and relevant. This involves breaking down complicated workflows into actionable steps.

Reasoning and planning allow the AI to handle nuanced situations requiring human-like judgment (e.g., prioritizing urgent IT tickets or adapting an approval path). To evaluate these capabilities consider:

• Test prioritization: Give the agent competing tasks to see if it correctly prioritizes based on urgency.
• Test adaptation: Introduce a sudden constraint mid-workflow to see if the system can adjust its decision-making and plan accordingly.

Contextual awareness and adaptive learning

Contextual awareness allows AI to understand not just what is being asked, but why. Agentic AI should identify user intent based on context — such as department, role, or past interactions — to deliver relevant responses and actions. Leading platforms use feedback loops (like user corrections and admin adjustments) to refine performance over time, reducing maintenance and improving accuracy.

Multi-agent orchestration and integration

Enterprises rarely run on a single AI system — they’ve already invested in dozens of tools across IT, HR, finance, and customer support, many of which now have their own embedded AI features. That creates a fragmented landscape where different systems “know” different things, but don’t naturally talk to each other, and basic or narrow AI in each tool can’t easily handle cross-system work end to end.

• To unlock the real value of those investments, organizations need a way to connect and coordinate these capabilities so employees can just state a goal in natural language and have the right actions happen across ServiceNow, Workday, Salesforce, Slack/Teams, and more. 
• That’s where multi-agent systems become important. Instead of relying on a single monolithic bot, agentic AI architectures use multiple specialized agents that collaborate, each focused on a slice of the problem, such as perception, reasoning, or taking action in a specific system — while an orchestrator coordinates them to achieve a shared objective.
• This multi-agent approach maps naturally onto how enterprises already work: different systems and teams own different parts of a workflow, and AI agents mirror that division of labor, then reason and route work across it. 

The result is that enterprises can turn a patchwork of siloed AI capabilities into a cohesive, cross-system automation layer, which is why multi-agent collaboration is widely viewed as the next frontier for enterprise agentic AI. 

Ease of use and deployability

Enterprise adoption depends on accessibility. Look for platforms that empower both technical and non-technical teams to deploy and customize automations quickly. Low-code or no-code environments allow business users to design and launch agents without developer bottlenecks, ensuring faster time-to-value and more distributed innovation across the organization.

Transparency, governance, and explainability

As AI grows more autonomous, trust becomes non-negotiable. Leading platforms offer built-in governance, permission controls, and detailed audit trails that allow admins to monitor  AI decisions. Transparent reporting and explainable actions help enable accountability, compliance, and confidence among business leaders.

Security and compliance

Enterprise AI needs to meet strict security standards, especially when handling sensitive data across systems. Prioritize solutions that come with: 

• End-to-end encryption for data in transit and at rest 
• Role-based access controls with detailed permissions 
• Compliance with key regulations like GDPR, HIPAA, and others 
• Regular security audits and vulnerability testing 
• Data residency options to meet regional requirements 

The most secure agentic AI tools bake security into their design from the start, instead of tacking it on later as an afterthought, with solid data handling practices to keep your information safe.

Scalability and future-readiness

Agentic AI is evolving quickly, and so should your platform. Choose a solution that can scale with your enterprise, adding new agents, integrations, and capabilities as needs expand. Future-ready platforms are designed for modular growth, helping your investment today keep pace with tomorrow’s innovations.

Together, these capabilities define what separates a capable automation tool from a truly agentic AI platform. The right choice will help your enterprise reason, plan, and act across systems — safely, intelligently, and at scale.

Top 10 agentic AI tools for enterprises

Selecting your ideal platform depends on your environment, needs, and objectives. Below are the essential capabilities to consider when comparing tools.

1. Moveworks — An agentic AI platform that unifies search, action, and expensibility across the enterprise  

Moveworks is an agentic AI platform that acts as the front door to work, combining intelligent search, autonomous action, and deep extensibility so employees can get what they need — and get work done — through a single, trusted experience.

Key features

• Agentic AI Assistant: A conversational interface where employees can ask questions, resolve issues, and complete tasks across IT, HR, finance, and more.
• Unified search and action: Understands natural language, retrieves precise answers, and takes real action across your enterprise systems—rather than just surfacing links or instructions.
• Agent Studio for extensibility: A builder environment where teams can design, deploy, and manage new agents and plugins, extending Moveworks to custom workflows and business systems.
• Reasoning engine across systems: Interprets intent, reasons through context, and orchestrates multi-step workflows end-to-end, instead of relying on brittle, rule-based automations.

Best use case

Moveworks is ideal for enterprises that need a single surface where employees can resolve issues, complete approvals, update records, and find knowledge without navigating multiple portals or waiting on service desks. 

It is especially powerful in environments with complex, cross-functional workflows spanning IT, HR, finance, facilities, and line-of-business applications.

The platform is designed for enterprise-scale deployment, with the security and compliance features regulated industries require, including financial services, healthcare, and government.

See how Moveworks can transform your employee experience with agentic AI.

2. Microsoft AutoGen — Framework for building collaborative, multi-agent AI systems

Microsoft AutoGen is an open-source programming framework designed to simplify the creation of complex applications by facilitating collaboration among multiple AI agents. It functions as a foundational layer where customizable agents—integrating Large Language Models (LLMs), tools, and human input—can autonomously communicate and solve tasks collectively.

Key features

• Multi-agent conversation framework: Supports patterns like one-to-one, group chat, and hierarchical agent communication
• LLM and tool integration: Allows agents to use multiple models and external tools to complete tasks.
• Customizable agent roles: Lets developers define specialized agent behaviors and responsibilities.
• Modular architecture: Designed for extensibility, enabling integration with APIs, datasets, or custom tools.

Best use case

Ideal for complex problem-solving or workflows that require multiple agents with specialized skills. It works well for analytics, coding tasks, simulation, research, and multi-step reasoning processes.

3. ChatGPT Agent (Formerly OpenAI Operator) — API-driven action and UI interaction

 ChatGPT Agent is an evolution of OpenAI’s earlier Operator framework, enabling autonomous, multi-step task execution through tool use, API integrations, and UI-level action. It is designed to take high-level goals, break them into actionable steps, and complete tasks across applications with minimal human involvement.

Key features

• Autonomous workflow execution: Plans and performs multi-step tasks end to end based on high-level instructions.
• Tool and API integration: Connects to external apps, APIs, and data sources to perform actions and retrieve information.
• UI navigation capabilities: Interacts with web interfaces—clicking, typing, and form-filling—to complete tasks that lack APIs.
• Custom agent builder: Allows users to define reusable agents with specific roles, tools, and knowledge without coding.

Best use case

ChatGPT Agent is suited for automating digital tasks that require reasoning, multi-step planning, and action across multiple systems. It is especially useful for research workflows, administrative task automation, data gathering, document preparation, and any process that spans tools or interfaces without existing automation

4. CrewAI — Open source multi-agent collaboration for complex workflows

CrewAI is an open-source framework that brings multiple AI agents together to collaborate on solving complex problems and building multi-agent systems, similar to how human teams coordinate projects.

Key features

• Multi-agent collaboration: Enables multiple specialized agents to work together on complex tasks.
• Expanded integration layer: Allows agents to trigger workflows via Gmail, Slack, Salesforce, and cloud services.
• Role-based architecture: Assigns defined roles and responsibilities to each agent.
• Flexible LLM support: Works with various large language models, allowing users to choose the best fit.

Best use case

CrewAI works well for organizations with complex workflows that benefit from breaking tasks into discrete steps handled by specialized agents. It's a valuable tool for research, data analysis, content creation, and other creative or analytical processes that require multiple perspectives and capabilities.

5. Adept — LLM-powered automation that navigates software interfaces

Adept focuses on building AI that can operate enterprise software systems through its action-oriented approach to agentic AI.

Key features

• Interface navigation: Automates tasks by interacting with application UIs directly through clicks, text input, and form completion.
• Multimodal understanding: Combines text and visual inputs to interpret software environments accurately.
• Cross-application actions: Performs workflows across multiple apps, including those lacking official integrations.
• Enterprise workflow automation: Designed to handle repetitive digital tasks such as data entry, report creation, or record updates.

Best use case

Adept is suited for organizations needing automation where APIs are limited or unavailable. It performs well for operational tasks that require interacting with multiple SaaS interfaces or legacy tools.

6. Beam — AI-native process automation built for global enterprises

Beam provides an AI-driven automation platform built to support large-scale process orchestration, combining multi-agent intelligence with enterprise-grade reliability.

Key features

• Multi-agent automation: Uses multiple agents to solve end-to-end business workflows.
• AI-native operating system: Provides governance, accuracy, and orchestration in a unified platform.
• Enterprise system integration: Connects with internal tools, APIs, and business systems for coordinated task execution.
• High-volume scalability: Designed for operational environments that process large workloads.

Best use case

Beam is best for enterprises with large, complex operational workflows requiring strong governance and scalability. It is particularly effective for back-office operations, multi-step approvals, and high-volume process automation.

7. UiPath — Bridging RPA and agentic AI for intelligent automation

UiPath combines traditional robotic process automation (RPA) with emerging agentic AI capabilities to enable intelligent, end-to-end workflow automation across enterprise systems. Recent updates strengthen its orchestration, reasoning, and governance layers to support more autonomous and adaptable automation.

Key features

• Hybrid RPA + agentic AI automation: Integrates rule-based automation with reasoning and decision-making capabilities.
• UiPath Maestro orchestration: Manages AI agents, robots, and human-in-the-loop processes within a unified control layer.
• Expanded model integrations: Offers native support for OpenAI and other LLM providers for advanced reasoning tasks.

Best use case

UiPath is well suited for enterprises seeking to combine existing RPA investments with more adaptive, AI-driven automation. It excels in document-heavy workflows, back-office operations, compliance-driven processes, and multi-step tasks that require both structured automation and contextual decision-making.

8. Orby — Generative process automation powered by neural reasoning

Orby offers an enterprise AI automation platform focused on improving efficiency through generative process automation, which increases automation scope while simplifying workflow definitions.

Key features

• Multimodal Large Action Model: Understands text, images, and UI elements to perform tasks.
• Neuro-symbolic reasoning: Combines neural models with structured logic for more reliable decision-making.
• Rapid deployment capabilities: Reduces effort required to define and implement automation workflows.
• Cross-application task execution: Works across various enterprise systems and interfaces.

Best use case

Orby is well suited for organizations that need flexible and adaptable automation across multiple systems. It is especially useful for operations teams managing processes that require both pattern recognition and structured reasoning.

9. Relevance AI — Build and deploy custom multi-agent AI workflows

Relevance AI is a low/no-code platform designed to help businesses build and orchestrate an "AI Workforce"—teams of intelligent agents that can autonomously complete complex, adaptive workflows. Unlike traditional automation, Relevance AI focuses on agentic automation, empowering non-technical business users to create specialized AI agents.

Key features

• No-code agent builder: Allows users to create agents using natural language instructions.
• Multi-agent orchestration: Enables visual workflow design, assigning multiple agents to different roles.
• Tool and API integration: Connects to thousands of external services and internal systems.
• Knowledge base integration: Uses retrieval-augmented generation to ground agent reasoning in company data.
• LLM-agnostic platform: Supports models from OpenAI, Anthropic, and others.

Best use case

Relevance AI is ideal for teams that want fast deployment of AI-driven workflows without heavy engineering investment. It performs well for sales operations, customer support, data analysis, and reporting processes.

10. Cognosys — Autonomous enterprise agents for end-to-end decision automation

Cognosys provides fully autonomous AI agents designed to execute high-level business objectives by conducting research, planning workflows, and completing tasks across digital tools.

Key features

• Autonomous objective execution: Breaks complex goals into workflows and executes tasks independently.
• Research and data analysis tools: Performs competitive research, summarization, and structured reporting.
• Workflow automation: Supports scheduled and trigger-based tasks across email, documents, and business applications.
• Integration hub: Connects with Gmail, Notion, Outlook, Google services, and other workplace tools.
• Self-correction and adaptation: Adjusts plans as needed based on results or new information.

Best use case

Cognosys is best for research-heavy or decision-support tasks that require synthesizing large amounts of information. It is effective for market analysis, reporting, content generation, and automated administrative processes.

How to choose the best agentic AI tool for you

Selecting the right agentic AI platform isn’t about finding the most sophisticated technology — it’s about finding the best fit for your enterprise. Each organization has unique goals, systems, and governance needs, so the evaluation process should focus on outcomes and alignment, not just features.

Use the following criteria to separate capable automation tools from truly agentic AI platforms:

1. Define your business requirements.
   Start by clearly identifying the specific processes you want to automate and the problems you need to solve. Consider both immediate pain points as well as long-term goals.

2. Assess your technical environment. 
   Evaluate your existing systems, data sources, and infrastructure. The ideal solution should integrate easily with your current technology stack while providing flexibility and adaptability for future growth.

3. Prioritize real enterprise integrations and time-to-value. 
   Choose solutions that offer seamless integration with your core enterprise systems. Focus on platforms that can demonstrate rapid deployment and a clear, measurable return on investment (ROI) in a short timeframe.

4. Consider implementation resources. 
   Be realistic about your internal capabilities for implementing and managing an AI system. Some solutions require significant technical expertise, while others offer more plug-and-play deployment options.

5. Evaluate reasoning and autonomy.
   Assess how each platform reasons through complex tasks. Does it plan multi-step actions within enterprise guardrails? Does it require heavy customization, or can it adapt dynamically to real-world conditions?
   

6. Assess governance and oversight features.
   Look for tools with built-in auditability, permissions management, and explainability. The best agentic AI systems operate with transparency, giving administrators full visibility into how and why actions are taken.
   

7. Evaluate security and compliance needs.
   If you operate in a regulated industry or handle sensitive data, prioritize solutions with security features and compliance certifications that align with your requirements.

8. Calculate total cost of ownership.
   Look beyond initial licensing costs to consider implementation expenses, ongoing maintenance, and potential need for specialized talent. The most affordable option upfront isn't necessarily the most cost-effective in the long run.
   

9. Consider scalability and long-term adaptability.
   Agentic AI is evolving quickly. Choose a platform designed for modular growth — one that can support future integrations, new capabilities, and emerging technologies without needing a full rebuild.
   

The future of agentic AI tools and enterprise transformation

Agentic AI isn’t just another phase of automation — it’s part of the new era of enterprise intelligence. In fact, 80% of executives are saying that agentic AI has already produced a significant or total transformation of their operations.

As organizations adopt agentic tools, they’re discovering that the real advantage lies in agility: the ability to adapt, reason, and act as business conditions change.

In the coming years, we’ll see:

• Greater collaboration between multiple specialized agents working together across departments. 
• More non-technical employees driving agentic AI projects than in previous technology waves.
• AI that's more context-aware and integrated with existing systems, combining reasoning, perception, and planning into cohesive enterprise workflows.

Forward-looking companies are already preparing for this shift by adopting platforms that balance innovation with governance. By investing in tools that evolve alongside the enterprise, leaders can ensure their AI strategies remain both adaptable and responsible.

Transform your enterprise with agentic AI

Agentic AI is redefining how enterprises work. Moveworks delivers enterprise-grade agentic AI that unifies reasoning, search, and action — empowering employees to find information, complete tasks, and drive outcomes in a single conversation.

From IT and HR to finance and operations, the Moveworks platform helps organizations orchestrate complex workflows securely and efficiently. With features like low-code development, deep integrations, and built-in governance, Moveworks helps enterprises turn potential into performance.

It’s time to move from automation to orchestration. See how Moveworks can help your enterprise scale with confidence and lead in the era of agentic AI.

Learn more about Moveworks Agentic AI →