AI agents have gone from a niche experiment to a genuine industry in the span of about two years, and the names keep piling up faster than most people can keep track of. If you’ve heard names like Devin, Manus, or Genspark thrown around and had no idea what actually separates them, this breakdown is for you.
Devin
Devin, built by a company called Cognition AI, was one of the first tools marketed as a fully autonomous AI software engineer rather than just a coding assistant. It can plan out a coding task, write the code, test it, debug errors it finds along the way, and even submit the finished work for review, largely without step-by-step human guidance. Cognition’s valuation jumped to $26 billion in 2026 as enterprise interest in autonomous coding agents accelerated. Devin is best suited for developers who want to hand off well-defined coding tasks entirely, rather than co-writing code line by line.
OpenAI Codex
OpenAI Codex is OpenAI’s dedicated coding agent, built to write features, answer questions about an existing codebase, fix bugs, and propose code changes for human review. By March 2026, Codex had grown to more than two million weekly active users, reflecting how quickly developers adopted it inside their existing workflows. OpenAI has continued expanding it beyond a simple coding tool, releasing a desktop app that lets users manage multiple coding agents over longer stretches of time. Codex works well for developers already inside the ChatGPT ecosystem who want an agent tightly integrated with familiar tools.
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Claude Code
Claude Code is Anthropic’s agentic coding tool, designed to let developers delegate coding tasks directly from the terminal, desktop app, or integrated environments like VS Code and JetBrains. Rather than just suggesting code snippets, it can navigate an entire codebase, make multi-file changes, run tests, and handle more complex engineering tasks with the kind of careful, methodical reasoning Anthropic’s models are known for. It’s built specifically with developer workflows in mind, fitting into existing tools rather than requiring a completely separate environment. Claude Code appeals particularly to teams working on larger, more complex codebases where careful, well-reasoned changes matter more than raw speed.
Cursor
Cursor, built by a company called Anysphere, is an AI-powered code editor that integrates an agent directly into the coding environment developers already use every day. It reached a valuation near $29 billion, making it one of the most valuable AI agent products on the market, largely by embedding itself so deeply into daily developer workflows that switching away feels disruptive. Unlike standalone chat-based coding tools, Cursor’s agent operates inside the editor itself, understanding the full context of a project as you work. It’s a strong fit for developers who want an AI collaborator woven directly into their existing editor rather than a separate chat window.
Sierra
Sierra is a customer service AI agent platform that reached a $15 billion valuation after being adopted by nearly half of the Fortune 50, generating $150 million in annual recurring revenue. Its agents don’t just answer scripted questions, they triage incoming issues, resolve problems directly, escalate when necessary, and follow up automatically across chat, email, and voice channels. This end-to-end approach to customer support is a significant step beyond older rule-based chatbots that could only handle narrow, predefined scripts. Sierra is built specifically for businesses looking to automate large volumes of customer interactions without sacrificing resolution quality.
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Harvey
Harvey is an AI agent built specifically for legal work, reaching a $5 billion valuation by helping lawyers draft contracts, review documents, and research case law significantly faster than manual processes allow. Rather than positioning itself as a general-purpose assistant, Harvey focuses narrowly on legal workflows, which has made it easier for law firms to trust and adopt compared to broader, less specialized tools. Its narrow focus on high-stakes, detail-heavy legal work reflects a broader trend of AI agents winning by going deep into one industry rather than trying to serve everyone at once. Harvey is a strong example of how vertical-specific agents often outperform general-purpose alternatives in professional settings.
GitHub Copilot
GitHub Copilot was one of the earliest widely adopted AI coding tools, originally launched as an autocomplete-style assistant before evolving into a more capable agent over time. It now offers agentic features that can handle multi-step coding tasks, review pull requests, and work across an entire repository rather than suggesting isolated lines of code. Because it’s built directly into GitHub, the platform millions of developers already use daily, Copilot benefits from a distribution advantage few competitors can match. It remains one of the most widely used entry points into AI-assisted coding for developers of all experience levels.
Replit Agent
Replit Agent lets people build full working applications through AI-assisted coding, often without requiring deep programming knowledge, positioning it as one of the more accessible agents on this list. The company behind it projects revenue will reach $1 billion by the end of 2026, up dramatically from $240 million, reflecting surging demand for AI-assisted app building. Unlike agents aimed purely at experienced developers, Replit Agent is designed to lower the barrier to entry for people who have an idea but limited coding background. This accessibility angle has made it a favorite among indie builders, students, and non-technical founders.
Manus
Manus, built by a company called Butterfly Effect, markets itself as a general-purpose AI agent designed to bridge the gap between thinking and actually doing. It can browse the web, write and run code, generate files, and complete multi-step workflows like market research or data analysis largely on its own, using a multi-agent system with a planner and executor working together. Within days of its 2025 launch, Manus had two million people on its waitlist, with invite codes reportedly reselling for thousands of dollars. It’s built for knowledge workers who want to hand off broad, loosely defined tasks rather than narrow, single-purpose requests.
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Genspark
Genspark positions itself as an all-in-one AI workspace built for over a billion global knowledge workers, combining research, slide creation, spreadsheet analysis, and even real phone calls into a single agent-driven platform. Its “Call for Me” feature lets the agent place real outbound phone calls on a user’s behalf, handling tasks like appointment booking and customer service inquiries across dozens of countries. The company crossed $100 million in annual recurring revenue within just nine months of launch, reflecting rapid enterprise adoption. Genspark stands out for combining multiple agent capabilities, research, content creation, and voice, into one unified workspace rather than a single narrow function.
Final Thoughts
These ten agents show just how differently “AI agent” gets applied depending on the problem being solved, from Devin’s autonomous coding to Sierra’s customer service automation to Genspark’s all-in-one workspace approach. What separates the winners isn’t raw ambition, it’s how precisely each one solves a specific, high-value problem for a specific type of user.
Next time one of these names comes up in a conversation or headline, you’ll actually know what it does and who it’s built for. If this breakdown helped clarify the crowded AI agent landscape, share it with someone who’s just as lost keeping track of all these names.
Call to Action
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