Pichai Acknowledges Google Trails in Agentic Coding

Sundar Pichai told Search Engine Journal that Google is "a bit behind" the frontier on agentic coding, tool use, instruction following, and long-horizon tasks. He said coding is "very foundational" to Googles AI work. Pichai attributed part of the lag to a lack of external developer-facing product surface that generates coding data flows, and he cited Anthropics relationship with Cursor as an example, according to the interview. The article reports that Google announced Antigravity 2.0 as a standalone desktop application at its recent I/O developer conference. Pichai is quoted saying internal usage of Antigravity is "doubling every week," which he said is helping Google "hill climb quite a bit." The piece also notes the recent launch of the model Gemini 3.5.

Companies building agentic coding systems rely on two linked capabilities: models that support multi-step planning and reliable tool integration, and product surfaces that expose those capabilities to developers at scale. Public reporting frames Google as technically competitive on core model capabilities such as text, multimodality, voice, audio, and reasoning, while placing the reported gap specifically in longer-running, tool-enabled developer workflows. Observed patterns in the sector show that external developer products produce valuable operational signals and labeled usage data that accelerate iterative model and product improvements.

For practitioners, the distinction between single-shot web front ends and multi-step agentic workflows matters for data collection, evaluation, and deployment complexity. Industry observers note that startups and specialist vendors that ship developer-facing integrations often capture higher-signal telemetry for coding tasks, which can translate into faster feature and dataset cycles. Public reporting frames Googles push with Antigravity 2.0 and internal usage growth as an attempt to increase those developer data flows.

• •Adoption and feature set of Antigravity 2.0 among external developers, as reported usage beyond Google will indicate real data-flow generation.
• •Any technical disclosures or benchmarks for Gemini 3.5 on long-horizon, tool-enabled coding tasks, which would clarify model-level progress.
• •Partner integrations similar to the AnthropicCursor example, which public coverage treats as a precedent for developer product-led data advantages.

Editorial analysis: The reported statements and product moves are important signals about where vendor efforts and data incentives are concentrating, but public reporting does not include internal roadmaps or commitments from Google beyond the interview quotes and the I/O announcement.