Multi-step AI agents are transforming how businesses automate complex workflows, requiring precise orchestration across multiple tasks. Selecting the right tool involves evaluating integration capabilities, data handling, and scalability. This brief highlights top tools that excel in orchestrating multi-step AI processes, focusing on specific strengths and limitations.
AI-Native & No-Code Agent Builders
These platforms are built from the ground up for agentic reasoning and multi-step execution.
Zapier Central
Zapier Central is an AI-powered workspace that transforms traditional linear automations into intelligent “AI teammates.” By 2026, it has become the command center for multi-step agentic orchestration within the Zapier ecosystem. Unlike standard Zaps, which follow rigid triggers, Central agents use reasoning to analyze data and decide which of the 6,000+ connected apps to execute. These agents act as persistent cognitive partners that can be taught through natural language and conversation, rather than complex coding, to manage end-to-end business functions.
Features include Persistent Memory, allowing agents to learn from past interactions and refine their decision-making over time. The platform’s Instruction-Based Training enables users to provide “knowledge documents” and “FAQ links” that ground the agent’s logic in specific company context. In 2026, the Agent Monitoring suite provides real-time visibility into an agent’s reasoning chain, while the Zapier MCP (Model Context Protocol) allows these agents to discover and use actions from any external service as if they were native tools.
Best for small to medium businesses and operations teams that need to delegate high-volume cognitive tasks like lead research, content creation, or customer support triage. It is the ideal choice for “no-code” builders who want to transition from simple automations to autonomous agents that can plan and execute multi-step goals. Teams at organizations like Website Genii and NisonCo use Zapier Central to reclaim hundreds of hours by having AI agents manage their sales pipelines and research tasks directly.
Lindy AI
Lindy AI is a specialized platform designed to build and manage “AI Employees” capable of executing end-to-end business processes. By 2026, it has solidified its position as a leader in multi-step agentic orchestration by providing agents that don’t just follow instructions but reason through complex objectives. These digital workers operate autonomously across email, calendars, and third-party software, acting as a scalable extension of a company’s human workforce to handle repetitive, high-level cognitive labor.
Features include an intuitive Natural Language Builder that allows users to create custom agents—called Lindies—simply by describing their job descriptions and goals. The platform supports Native Voice and Video, enabling agents to participate in meetings, record notes, and even handle inbound or outbound phone calls with human-like latency. In 2026, Lindy introduced Agent Collaboration, which allows multiple specialized agents to work together on a single project, and a comprehensive Planning Layer that enables agents to self-correct and adjust their strategy when they encounter unexpected obstacles.
Best for fast-growing startups and departments like Sales, Recruiting, and Customer Support that need to automate entire functional roles rather than just isolated tasks. It is the ideal choice for business leaders who want “out-of-the-box” productivity without the need for technical engineering, as Lindy handles the underlying model orchestration and tool integration. Companies in the tech, finance, and real estate sectors use Lindy to manage lead generation, schedule high-volume interviews, and resolve complex customer support tickets autonomously.
Gumloop
Gumloop is a high-performance visual automation platform that empowers users to build sophisticated, multi-step AI agents using a drag-and-drop interface. By 2026, it has become the preferred framework for “knowledge work automation,” allowing teams to design workflows that move far beyond simple task triggers. Unlike traditional linear tools, Gumloop treats AI models as modular nodes that can reason, extract data from complex sources, and make branching decisions, effectively turning human-level business processes into scalable digital assets.
Features include an AI Router that enables agents to dynamically choose the best next step in a workflow based on real-time data or user sentiment. The platform offers over 125+ native nodes for deep integration with apps like Salesforce, Apollo, and Zendesk, alongside advanced capabilities for web research and unstructured data parsing. In 2026, Gumloop introduced Model Flexibility, allowing users to swap between GPT-4, Claude 4, and Gemini 2.5 within a single flow, and a robust Audit Logging system to ensure complete transparency and governance over autonomous agent actions.
Best for Operations managers, Growth engineers, and Marketing teams at fast-scaling startups and mid-market enterprises who need to automate complex, data-heavy research and outreach. It is the ideal choice for organizations that have outgrown basic “if-this-then-that” tools and require a platform that can handle the nuance of reasoning and decision-making. Global innovators like Instacart, Shopify, and Webflow use Gumloop to reclaim thousands of collective hours by automating account analysis, sentiment reporting, and lead qualification at scale.
Relevance AI
Relevance AI is a comprehensive platform designed to build, manage, and scale a digital “AI Workforce.” By 2026, it has become a central hub for companies seeking to transition from single-task bots to multi-agent systems that perform human-quality work. The platform allows organizations to recruit specialized AI agents that function with a high degree of independence, coordinating multi-step processes across sales, marketing, and operations. It uniquely combines an intuitive visual builder with enterprise-grade depth, enabling teams to deploy autonomous agents that are deeply integrated with their existing tech stack and proprietary knowledge.
Features include AI Workforce Management, which provides a single interface to oversee a team of specialized agents, and Metadata Capture to automatically track high-value data from every completed task. The platform offers a library of over 100+ expert-designed templates, ranging from AI BDRs to Lifecycle Marketers, alongside a powerful Tool Builder for creating custom LLM prompt chains and API integrations. In 2026, Relevance AI introduced Agent Scheduling and Approvals, allowing for precise control over when agents run and when they require human intervention, ensuring that autonomous workflows remain governed and transparent.
Best for mid-market to enterprise operations and GTM teams that need to scale their capacity without increasing headcount. It is the ideal choice for businesses looking for a “plug-and-play” AI workforce that can handle complex, multi-step objectives like CRM enrichment, account research, and inbound lead qualification. Organizations such as Canva, Autodesk, and KPMG use Relevance AI to redesign their core work motions, transforming manual, repetitive sequences into high-velocity autonomous operations that deliver consistent results at scale.
FlowHunt
FlowHunt is a versatile no-code AI automation platform that enables the creation of complex, multi-step AI agents and workflows. By 2026, it has become a go-to tool for businesses looking to build autonomous AI systems that can perform real-world tasks beyond simple text generation. It allows users to combine multiple AI models with specific tools and knowledge sources on a visual canvas, facilitating the orchestration of specialized “teams” of agents that collaborate to solve sophisticated business challenges.
Features include a Visual Drag-and-Drop Builder that simplifies the connection of intuitive blocks, turning ideas into fully functional AI automations. The platform’s Knowledge Sources capability allows agents to access real-time information from documents, websites, and FAQs, ensuring answers remain fresh and grounded. In 2026, FlowHunt stands out with its support for MCP (Model Context Protocol) Servers, enabling seamless tool integration, and specialized agents for Advanced Content Teams that group researchers, copywriters, and humanizers into a single, cohesive workflow.
Best for small to medium businesses and marketing agencies that need to scale content production, automate lead generation, or perform deep market research without a large technical team. It is the ideal choice for “citizen developers” who want to build custom AI solutions, such as autonomous trading bots or company analysis tools, through a user-friendly interface. Organizations in real estate, SEO, and customer support use FlowHunt to gain a competitive edge by automating market trend analysis and high-volume content updates effortlessly.
Enterprise Agentic Platforms (The “Big Tech” Suites)
These are the enterprise powerhouses (including those from your list) that integrate multi-step agents into corporate infrastructure.
Salesforce Agentforce (formerly Einstein)
Salesforce Agentforce is a cutting-edge platform designed to deploy autonomous AI agents that act as a digital workforce across the Salesforce ecosystem. By 2026, it has moved beyond simple “copilots” to provide agents that can independently reason, plan, and execute complex business processes by interacting directly with CRM data. This evolution allows organizations to augment their human teams with always-on digital labor that is deeply grounded in the company’s proprietary data, ensuring high-fidelity outcomes for sales, service, and marketing functions.
Features include the Atlas Reasoning Engine, which enables agents to evaluate context, prioritize tasks, and perform multi-step planning without human intervention. The platform’s Agentforce Builder provides a low-code environment to customize agent logic, while Agentforce Voice adds natural-language conversational abilities to digital interactions. In 2026, the system features a robust AgentExchange, where teams can discover and deploy pre-built agent templates, and native integration with Data Cloud ensures that every agentic action is informed by a real-time, 360-degree view of the customer.
Best for large enterprises and Salesforce-heavy organizations that need to scale their customer operations without a proportional increase in headcount. It is the ideal choice for businesses looking to automate mission-critical workflows, such as complex case resolution, proactive lead nurturing, and personalized marketing orchestration. Companies in finance, healthcare, and retail use Agentforce to deliver 24/7 autonomous support and sales interactions that are consistent, auditable, and securely governed within their existing Salesforce infrastructure.
IBM Watson Orchestrate
IBM Watson Orchestrate is an enterprise-grade AI solution designed to coordinate a digital workforce of specialized agents. By 2026, it has shifted from a simple “assistant” to a “multi-agent orchestrator,” serving as the brain that sequences complex tasks across diverse business departments. It allows companies to unify their AI agents in an open, vendor-neutral ecosystem, ensuring that disparate tools—from customer service bots to supply chain planners—collaborate seamlessly to complete high-level objectives without requiring a human to manually hand off data between systems.
Features include a Catalog of Tools with over 400 prebuilt connectors and a specialized AI Agent Builder for both no-code and pro-code development. The platform’s Multi-Agent Orchestration engine uses reasoning to identify the necessary skills required for a goal, dynamically assigning them to the most suitable agents in real-time. In 2026, it emphasizes Governance and Observability, providing centralized oversight with built-in safety guardrails and automated policy enforcement, ensuring that all autonomous actions remain compliant with strict corporate standards and data privacy regulations.
Best for large enterprises in regulated industries like finance, HR, and procurement that need to automate “messy” cross-functional processes. It is the ideal choice for organizations that want to avoid vendor lock-in by orchestrating agents from various providers (OpenAI, Google, Microsoft) under a single, secure management layer. Global brands like Dun & Bradstreet and UFC use Watson Orchestrate to automate complex supplier risk evaluations and content generation, significantly reducing the time spent on administrative “glue work” and freeing employees for strategic initiatives.
Google Vertex AI Agent Builder
Google Vertex AI Agent Builder is an open and comprehensive platform designed for building, scaling, and governing enterprise-grade AI agents. By 2026, it has established itself as a premier environment for multi-step agentic systems, offering a “full-stack” foundation that bridges the gap between raw models and production-ready applications. The platform allows developers to create sophisticated multi-agent workflows that are deeply grounded in real-time enterprise data, leveraging Google’s massive search and mapping infrastructure to ensure every autonomous action is accurate and contextually aware.
Features include the Agent Development Kit (ADK), which enables the creation of complex reasoning systems in under 100 lines of Python or Java code, and the Agent Engine for serverless, auto-scaling deployment. The platform supports the Agent2Agent (A2A) protocol, allowing agents across different ecosystems to negotiate and collaborate securely. In 2026, it also features Grounding with Google Search and Maps, providing agents with access to real-time global information, and the Model Context Protocol (MCP) for seamless connection to diverse enterprise data sources like BigQuery, Slack, and Jira.
Best for enterprise developers and data science teams who need to transform fragmented business processes into reliable, high-scale agentic systems within the Google Cloud ecosystem. It is the ideal choice for organizations prioritizing “secure-by-design” foundations, requiring granular IAM controls and full observability into an agent’s reasoning process. Large-scale operations in retail, logistics, and customer service use Vertex AI Agent Builder to deploy collaborative teams of agents that can handle everything from complex document processing to real-time geospatial reasoning with global reliability.
Microsoft Copilot Studio (via Azure)
Microsoft Copilot Studio is a sophisticated agent-building platform that enables organizations to create, manage, and deploy autonomous AI agents across the Microsoft 365 ecosystem. By 2026, it has transitioned from a simple chatbot tool into a comprehensive “control plane” for multi-step agentic orchestration. It empowers both business users and developers to build agents that don’t just answer questions but independently plan and execute complex business processes, seamlessly interacting with enterprise data and third-party systems through a unified, secure interface.
Features include Multi-Agent Orchestration, which allows specialized agents to collaborate by routing tasks based on their specific expertise, and Autonomous Capabilities for independent planning and self-correction. The platform leverages Work IQ to ground agents in an organization’s unique knowledge base, supported by over 1,400 external connectors and Model Context Protocol (MCP) servers. In 2026, it also introduces Agent 365, providing a centralized management layer for governing agent identities and monitoring their reasoning chains directly within the Power Platform admin center.
Best for enterprises heavily invested in Microsoft 365 that need to automate high-stakes internal workflows like recruitment screening, balance sheet reconciliation, or IT support triage. It is the ideal choice for organizations requiring “secure-by-design” AI that complies with strict corporate governance while providing employees with always-on digital teammates. Companies like Estée Lauder, Dow, and Amgen use Copilot Studio to build custom agents that accelerate R&D and reduce operational costs by millions of dollars annually through automated decision-making.
Amazon Bedrock Agents (via SageMaker ecosystem)
Amazon Bedrock Agents is a fully managed capability within AWS that enables generative AI applications to execute multi-step tasks by connecting to company systems and data. By 2026, it has become a central pillar for “agentic” development, allowing builders to create autonomous systems that don’t just chat, but act. These agents use the reasoning of foundation models to decompose complex user requests, orchestrate the necessary API calls, and maintain context across interactions, all while staying within the secure perimeters of the AWS cloud environment.
Features include Multi-Agent Collaboration, which allows a “supervisor” agent to coordinate multiple specialized agents to solve intricate business challenges. The platform provides Memory Retention, enabling agents to remember historical user interactions for more personalized experiences, and Code Interpretation for dynamic execution of complex analytical queries. In 2026, it also introduces Amazon Bedrock AgentCore, an open framework that helps developers deploy and operate AI agents securely at scale, regardless of the underlying open-source framework or model used, while maintaining integrated Guardrails for built-in security.
Best for cloud-native enterprises and developers who need to build “production-grade” autonomous workflows that integrate deeply with existing AWS infrastructure. It is the ideal choice for organizations requiring high-security environments for handling sensitive company data while automating processes like insurance claims processing, inventory management, or complex data visualization. Teams at high-scale organizations use Bedrock Agents to move from experimental AI pilots to robust, multi-step systems that can independently resolve customer issues and navigate enterprise-wide data silos with global reliability.
RPA-to-Agentic Platforms
These tools (from your list) represent the bridge between traditional “Bot” automation and modern “Agent” reasoning.
UiPath (Autopilot & Agentic RPA)
UiPath (Autopilot & Agentic RPA) represents the evolution of robotic process automation into a sophisticated agentic system where AI “thinks” and robots “do.” By 2026, it has solidified its position as a leader in enterprise automation by blending traditional task execution with high-level reasoning. Through its Autopilot suite, UiPath empowers everyone from developers to business analysts to create autonomous agents that can navigate complex legacy software, parse unstructured documents, and manage end-to-end workflows that were previously too variable for standard RPA.
Features include Agentic Test Design, which evaluates requirements and generates tests autonomously, and Agentic Test Management for automated report generation and load test scenario planning. The platform’s Autopilot for Developers uses “text-to-workflow” and “text-to-expressions” to transform natural language descriptions into robust automation code instantly. In 2026, the Maestro orchestration engine and the Agent Builder allow for the creation of “coded agents” that securely collaborate across a business, while Clipboard AI automates digital paperwork by intelligently copying and pasting data between disparate applications and documents.
Best for large-scale enterprises that need to bridge the gap between modern AI reasoning and legacy business systems. It is the ideal choice for organizations looking to scale their automation programs across IT, Finance, HR, and Supply Chain by empowering non-technical employees to build and manage their own digital teammates. Global leaders in banking, healthcare, and manufacturing use UiPath to resolve complex customer inquiries, automate high-volume invoice processing, and maintain rigorous software quality through autonomous, agent-led testing cycles that operate with enterprise-grade security and trust.
Automation Anywhere (AI + RPA)
Automation Anywhere (AI + RPA) is a leading “Agentic Process Automation” (APA) system that transforms traditional robotic workflows into autonomous, goal-driven operations. By 2026, it has redefined enterprise automation by integrating a Process Reasoning Engine (PRE), which is trained on over 400 million enterprise workflow data points. This allows the platform to move beyond static bots to intelligent agents that can plan, execute, and self-heal across complex business processes, seamlessly bridging the gap between modern cloud applications and legacy infrastructure.
Features include Mozart Orchestrator, a unified composer that coordinates AI agents, RPA bots, and APIs within dynamic, governed workflows without context switching. The platform’s AI Agent Studio provides a low-code environment for building specialized agents, while Automation Co-Pilot allows employees to trigger multi-step agentic workflows directly from their existing everyday apps using natural language. In 2026, the system also emphasizes Continuous Learning, enabling agents to refine their performance over time, and a High-Scale Cloud Extraction Service that processes complex documents five times faster than previous generations.
Best for large-scale enterprise operations in sectors like finance, healthcare, and manufacturing that require robust, high-trust automation for judgment-based workflows. It is the ideal choice for organizations looking to scale their automation ROI by moving from simple task execution to “autonomous enterprise” status. Companies like KPMG and Alight use Automation Anywhere to automate mission-critical processes—such as multi-step claims processing and recruiting—achieving up to 95% accuracy and millions of dollars in cost savings by combining AI reasoning with the “muscle” of RPA.
Pega GenAI Blueprint & Platform
Pega GenAI Blueprint & Platform is a collaborative, AI-infused workflow design workspace that accelerates the transformation of business ideas into production-ready applications. By 2026, it has become the foundational engine for “Agentic Orchestration,” moving beyond simple app building to allow enterprises to rapidly modernize legacy systems. It uniquely fuses generative AI with decades of industry-specific best practices to create optimized blueprints for case management, data models, and personas, which can be instantly imported into the Pega Infinity suite for rapid deployment.
Features include Legacy Discovery, which analyzes BPMN files and legacy code to automatically reimagine outdated workflows as cloud-native journeys. The platform’s Agentic Process Fabric serves as a unified orchestration layer, allowing specialized AI agents to collaborate on multi-step tasks such as insurance underwriting or complex dispute resolutions. In 2026, the Pega Agent Experience enables these workflows to power real-time conversational agents across voice and text, while Predictable AI Agents provide built-in governance and sensitive data filters to ensure all autonomous actions remain secure, transparent, and compliant.
Best for large-scale enterprises in regulated industries like banking, healthcare, and telecommunications that need to modernize high-complexity legacy infrastructure with speed and precision. It is the ideal choice for business and IT leaders who want to bridge the “alignment gap” by co-designing mission-critical applications in a shared, low-code environment. Organizations like Microsoft, IBM, and Dun & Bradstreet use Pega to transform fragmented manual processes into intelligent, autonomous operations that increase productivity by up to 60% while maintaining absolute governance and scalability.
Developer Frameworks (Code-First Orchestration)
The foundational libraries used to build the agents that run on the platforms above.
LangGraph (by LangChain)
LangGraph is a high-performance open-source library from LangChain designed for building stateful, multi-actor applications with LLMs. By 2026, it has become the definitive framework for “agent engineering,” providing low-level primitives that give developers granular control over an agent’s reasoning and collaboration loops. Unlike simpler frameworks that treat agents as black boxes, LangGraph allows for the creation of complex cognitive architectures—including cycles, branching logic, and hierarchical “manager” structures—essential for solving non-linear business problems that require persistent state and memory.
Features include Human-in-the-Loop checkpoints, enabling users to inspect an agent’s state, roll back “time” to correct actions, and approve drafts before execution. The platform’s Built-in Persistence manages conversation history and context automatically across sessions, while native Token-by-Token Streaming provides real-time visibility into an agent’s intermediate reasoning steps. In 2026, the LangGraph Studio offers a visual IDE for prototyping and debugging, supported by Fault-Tolerant Scalability and task queues to handle long-running background research or multi-step transactional work at a global scale.
Best for software engineers and AI researchers who need to build “production-ready” agents tailored to unique, complex company requirements. It is the ideal choice for developers who have outgrown basic chains and need the flexibility to design custom, multi-agent workflows for tasks like automated software engineering, high-fidelity research, or complex customer service orchestration. Organizations like Ally Bank and guest-facing solution providers use LangGraph to make data-driven, deliberate decisions in their AI workflows, ensuring reliability and control over every autonomous step.
CrewAI
CrewAI is a leading multi-agent orchestration framework designed to enable “Role-Based Collaboration” among autonomous AI agents. By 2026, it has become the premier tool for orchestrating “crews” of agents that work together to solve complex, multi-step problems by assuming specific roles like Researcher, Writer, or Manager. The platform shifts the focus from writing individual prompts to managing a digital workforce, allowing users to define a high-level goal and let a structured team of agents plan, collaborate, and execute the necessary tasks autonomously.
Features include Advanced Agent Orchestration, which supports sequential, hierarchical, and consensual process flows to ensure agents stay aligned with the objective. The platform’s CrewAI Studio provides a visual editor for no-code builders to equip agents with tools like Salesforce, Notion, and HubSpot, while developers can utilize a powerful API for deeper customization. In 2026, the Agent Management Platform (AMP) introduces real-time tracing of agent reasoning, automated and human-in-the-loop training, and serverless scaling to handle millions of agentic workflows per month with enterprise-grade security.
Best for GTM teams, content departments, and software engineers who need to automate intricate, collaborative processes rather than single-turn tasks. It is the ideal choice for organizations looking to move from manual coordination to “autonomous teams” for use cases like programmatic curriculum design, multi-system lead enrichment, and complex code generation. Companies like PwC, DocuSign, and IBM use CrewAI to slash development times by up to 90% and achieve significantly higher accuracy in customer support and functional specification generation.
Microsoft AutoGen
Microsoft AutoGen is an advanced event-driven programming framework designed for building scalable, multi-agent AI systems through “Conversational Orchestration.” By 2026, it has become the leading open-source library for creating agents that solve complex problems by talking to each other, humans, and external tools. The platform uniquely enables developers to define specialized agents with distinct personas—such as a Coder, a Reviewer, and a Manager—who collaborate in a shared chat environment to decompose intricate tasks and verify their own results through recursive feedback loops.
Features include AutoGen Studio, a web-based UI for prototyping agentic workflows without writing code, and AutoGen Core, a low-level framework for building distributed agents across multi-language applications. The system supports Deterministic and Dynamic Workflows, allowing agents to follow strict business rules or adapt their strategy based on real-time conversation context. In 2026, it also features the McpWorkbench extension for connecting to Model Context Protocol (MCP) servers and a DockerCommandLineCodeExecutor for safely running model-generated code in isolated environments, ensuring high reliability for technical task automation.
Best for software engineers, data scientists, and AI researchers who need to build high-complexity, collaborative agent systems for coding, scientific research, or advanced data analysis. It is the ideal choice for developers seeking an “event-driven” architecture that can handle long-running, non-linear processes where agents must negotiate solutions and handle edge cases autonomously. Teams at leading tech organizations use AutoGen to automate the entire software development lifecycle—from requirements gathering to testing—by coordinating specialized crews of agents that maintain consistent state across distributed systems.
OpenAI Agents SDK
The OpenAI Agents SDK is a lightweight, Python-centric framework designed to build and orchestrate autonomous AI systems with minimal plumbing. Released as a production-ready evolution of experimental frameworks like Swarm, it provides developers with a clear, safety-conscious environment for creating multi-agent workflows. By 2026, it has become the gold standard for OpenAI-native stacks, allowing developers to move beyond passive chatbots to “active thinking” systems that can independently plan, act, and collaborate to solve real-world problems.
Features include Intelligent Handoffs, which allow agents to seamlessly delegate tasks to specialists—for instance, a general triage agent can “hand off” a complex billing query to a specialized accounting agent. The SDK also provides Built-in Safety Guardrails to validate inputs and outputs in real-time, preventing hallucinations and ensuring compliance. In 2026, it integrates deeply with the OpenAI Responses API, offering native support for Token-by-Token Streaming and Trace Logging, which gives developers a “debug dashboard” to visualize every decision, tool call, and reasoning step the agent takes.
Best for Python developers and teams already integrated into the OpenAI ecosystem who need to build high-scale, reliable agentic apps without the complexity of heavy orchestration frameworks. It is the ideal choice for creating smart assistants, task automation bots, and multi-step research pipelines that require official OpenAI support and minimal abstractions. Organizations use the OpenAI Agents SDK to automate mission-critical workflows, such as tax law research or multi-system data reconciliation, by leveraging its “enough features, few primitives” design philosophy to ship reliable agents fast.
Autonomous Web & Action Agents
Specialized agents that act as a “User” on the web.
MultiOn
MultiOn is a powerful autonomous AI agent designed specifically for web-based action and navigation, often referred to as the “motor cortex” for AI. By 2026, it has transitioned into a robust platform that enables users to delegate transactional tasks—such as booking travel, ordering groceries, or managing e-commerce checkouts—directly to an AI. Unlike standard LLMs that only generate text, MultiOn interacts with the live web, navigating complex interfaces, bypassing CAPTCHAs, and executing multi-step sequences to achieve real-world outcomes on behalf of the user.
Features include Agentic Commerce, powered by a strategic partnership with Visa to enable secure, autonomous payments and transaction handling. The platform offers a Web Navigation API that allows developers to integrate “Action Agents” into their own apps, capable of logging into accounts and filling out forms with human-like precision. In 2026, it also features Self-Improving Agents that learn from navigation failures to refine their paths, and the AGI-0 Mobile Agent, a personalized proactive co-worker designed to handle digital errands across smartphone applications and the mobile web.
Best for developers and individual power users who need to bridge the gap between AI reasoning and real-world execution on the web. It is the ideal choice for building “concierge-style” applications that require an agent to act as an intermediary for booking, purchasing, or data retrieval across platforms without native APIs. Innovative startups and enterprises use MultiOn to automate research workflows, manage complex logistics, and create seamless “agentic commerce” experiences where the AI takes care of everything from price comparison to final checkout.
Skyvern
Skyvern is an open-source AI browser automation platform that uses computer vision and LLMs to navigate and interact with complex websites. By 2026, it has become a cornerstone for “Agentic Web Orchestration,” offering a robust alternative to brittle, selector-based automation tools. Instead of relying on rigid code that breaks when a website’s UI changes, Skyvern “sees” the browser like a human, allowing it to complete multi-step workflows—such as filling out dynamic forms or managing procurement pipelines—across any web interface with a high degree of resilience and autonomy.
Features include Explainable AI, which provides detailed summaries and visual traces of every action the agent takes, ensuring full transparency in the reasoning process. The platform supports CAPTCHA Resolution and 2FA/TOTP, enabling agents to bypass security hurdles and log into user accounts securely to perform transactional tasks. In 2026, it also features Precise Proxy Targeting, allowing users to run workflows from specific zip codes or countries, and a powerful Data Extraction engine that can transform unstructured web information into structured CSV or JSON schemas on the fly.
Best for operations teams, insurance adjusters, and logistics managers who need to automate repetitive, browser-based tasks that lack official APIs. It is the ideal choice for organizations looking to scale work across hundreds of concurrent sessions, such as fetching invoices from vendor portals or submitting government forms. Companies across various industries use Skyvern to eliminate manual “boring” work, leveraging its API-driven, infinitely scalable infrastructure to run thousands of complex web tasks simultaneously without the need for constant script maintenance.
Adept
Adept is a pioneering AI research and product lab focused on building a “universal adapter” for software. By 2026, it has redefined the workforce by creating agents that translate natural language intents directly into actions across any digital interface. Unlike traditional models that only generate text, Adept’s foundational models are trained on trillions of tokens specifically related to software usage and web UIs, enabling them to act as an intelligent “overlay” that can drive browsers, spreadsheets, and complex enterprise tools just as a human would.
Features include Adept Planning, a multimodal reasoning engine that achieves high accuracy in executing end-to-end enterprise workflows, and Adept Locate, which can precisely identify buttons, text fields, and links on any webpage. The platform’s Custom Actuation Software utilizes a proprietary Domain Specific Language (DSL) to ensure actions are performed reliably, even when software interfaces change. In 2026, the suite also features Web VQA (Visual Question Answering) for reasoning about charts, graphs, and tables within documents, along with intuitive Feedback Tools for continuous model improvement through human-in-the-loop interactions.
Best for enterprise operations, financial services, and supply-chain teams that need to automate high-complexity processes across multiple disconnected software applications. It is the ideal choice for organizations looking to scale productivity by allowing employees to use simple commands to trigger multi-step sequences, such as checking shipping availability across hundreds of sites or processing license applications. Global leaders use Adept to create a “future-proof” digital workforce where agents handle the manual execution of software tasks, freeing humans to focus on high-level decision-making and strategy.
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