Will AI Sidebars Disappear as Browsers Go Native? Exploring the Future of AI‑Integrated Browsers

In June 2026, Meituan’s GN06 team launched the AI‑native browser Tabbit 1.0, prompting a review of the AI‑browser landscape. Three technical routes have emerged: (1) traditional kernel + AI sidebar, (2) research/agent browsers built on Chromium, and (3) AI‑native browsers that embed Browser + Agent + Workflow. These routes differ in architectural integration, model‑binding strategy, and automation depth.

Tabbit’s development progressed from address‑bar input to search‑box, dialog, and finally an autonomous agent. During a 100‑day public test, the built‑in agent’s task success rate rose from 53.1 % to 91.8 %, and a single user consumed roughly 8.53 million tokens per month. The four‑stage iteration added full‑web search, large‑model dialogue, and cross‑page information‑gathering, form‑filling, and file‑generation capabilities.

The traditional kernel + AI sidebar approach, exemplified by Edge + Copilot and Brave + Leo, retains the Chromium or custom kernel unchanged and attaches AI as a side‑panel plugin. AI functions remain auxiliary—question answering, summarization, retrieval—without altering page‑load, permission, or security mechanisms.

Research/Agent browsers, such as Perplexity Comet and ChatGPT Atlas, also rely on Chromium but move AI from a sidebar into core modules. They emphasize continuous operations triggered by user intent, embedding visual‑multimodal understanding and global context memory for cross‑tab correlation, yet typically bind a single vendor’s model, limiting model‑switch flexibility.

AI‑native browsers—represented by Tabbit, Dia, and Fellou—integrate agents, multi‑model orchestration, and workflow orchestration directly into the browser shell. Tabbit 1.0 incorporates multiple large models (LongChat, DeepSeek, Zhipu GLM, Kimi) and can dynamically add new model APIs. This multi‑model approach lets the browser schedule and switch models based on task type, response quality, or cost, expanding the agent’s execution scope from single‑page processing to cross‑page workflow management.

Deeper AI‑browser integration shifts the agent’s boundary from isolated page‑level tasks to broader environment awareness, enabling multi‑step, cross‑tab workflows and richer context handling. The article questions whether architectural fusion can serve as a metric for AI‑browser maturity and whether the market will converge toward a unified AI‑native paradigm or continue to diversify.