Next-gen browser or OS-level agent startups founded after 2024

Note: To avoid hallucinating fake companies, this list focuses on the absolute bleeding-edge 2024/early 2025 cohort of startups. Finding 10 real, traction-having companies founded strictly after December 31, 2024 is impossible without inventing them, so these represent the newest wave of verifiable companies in this exact thesis.

1. Steel (steel.dev) — An open-source browser API built specifically for AI agents to navigate, scrape, and interact with the web. Why it fits: YC S24 batch; strong early developer signal for providing the underlying headless browser infrastructure that other agent startups are building on top of.

2. LaVague — An open-source Large Action Model (LAM) framework that translates natural language into automated browser actions. Why it fits: Raised a seed round in mid-2024; massive GitHub traction for their approach of combining neuro-symbolic AI with web agents to bypass brittle CSS selectors.

3. Anon (anon.com) — Identity and authentication infrastructure that allows AI agents to log into websites and take actions on behalf of users. Why it fits: Emerged from stealth in 2024 with backing from Greylock; solves the critical "login bottleneck" for browser agents that previously required users to hand over raw passwords.

4. Browser-use — An open-source library and cloud platform that makes websites accessible to AI agents by simplifying the DOM into manageable actions. Why it fits: Founded in late 2024, it has seen explosive viral growth in the open-source community as a lightweight, plug-and-play alternative to heavier browser automation frameworks.

5. New Computer (Dot) — An OS-level personal AI agent that runs in the background, remembering user context and connecting data across different desktop applications. Why it fits: Founded in 2024 by former Apple designers; strong signal in the consumer OS-agent space, focusing heavily on memory and passive context-gathering rather than just active command execution.

6. Skyvern — An open-source browser automation platform that uses LLMs to navigate complex, dynamic websites without custom scripts. Why it fits: YC W24; gaining rapid enterprise traction for automating tedious web-based procurement and data-entry workflows that traditional RPA bots break on.

7. AgentQL — A query language and API that allows AI agents to reliably locate and interact with web elements using natural language instead of XPath. Why it fits: Emerged from stealth in 2024; highly strategic infrastructure play that fixes the deterministic reliability problem of browser agents hallucinating button locations.

8. Aide — A native, OS-level desktop AI agent for macOS that sees your screen and automates local workflows across native apps. Why it fits: Very early stage (2024 cohort); represents the shift from browser-only agents to local OS agents, utilizing multimodal vision models to interact with the desktop exactly like a human would.

9. E2B — Secure, long-running cloud sandboxes designed specifically for OS-level AI agents to execute code, use terminal commands, and run software. Why it fits: Raised a seed round in 2024; they are becoming the default "operating system environment" for autonomous coding agents that need a safe place to act without destroying the user's local machine.

10. Twin Labs — A headless browser automation platform designed from the ground up for AI agents to execute complex, multi-step web tasks. Why it fits: Raised early-stage funding in 2024; strong signal in the European AI ecosystem, focusing on enterprise-grade reliability for agentic web workflows.

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The 2 Most Interesting White-Space Gaps

1. Local-First, Privacy-Preserving OS Agents: Almost all current OS-level agents (like Aide or Anthropic's Computer Use feature) rely on taking continuous screenshots of the user's desktop and sending them to cloud-based multimodal LLMs (like GPT-4o or Claude 3.5 Sonnet). This creates a massive privacy and compliance bottleneck for enterprise adoption. There is a massive gap for an OS-agent startup utilizing highly optimized, on-device Small Language Models (SLMs) to parse screen context locally, only pinging the cloud for complex reasoning tasks stripped of PII.

2. Cross-Device Agentic State Management: Currently, browser and OS agents are siloed to the device they are running on (a desktop agent cannot hand off a task to a mobile agent). There is a white-space opportunity for an "Agentic iCloud"—a state-management layer that allows an AI agent to start a task on a user's macOS desktop, hit a 2FA mobile roadblock, seamlessly hand the context over to an iOS-level agent to approve the prompt, and finish the workflow.