Google Jules Rebrands as End-to-End Agentic Product Development Platform, Opens Waitlist for New Version

Alibaba Cloud Launches JVS Crew, Enterprise-Grade AI Agent Platform

Gate News message, April 23 — Alibaba Cloud officially released JVS Crew, an enterprise-grade AI Agent construction platform designed with an "integration-first" approach. The platform enables enterprises to quickly embed AI Agent capabilities into existing apps, SaaS services, or smart hardware

Taiwan banks team up to build local AI! Finance’s large language model goes live by the end of the year at the fastest

CITIC Financial Holding, led by CITIC Financial Holding’s 16 financial institutions, announced the launch of the “Financial Large Language Model FinLLM” project. The first release of the banking model is expected to be published in August, and in 2026 Q1, AI agents based on FinLLM will be introduced. Training will begin in May, with a budget of approximately 40–70 million yuan. Due to regulatory and localization needs, local data training will be the core, strengthening sovereign AI, building shared infrastructure, and extending to inclusive finance. The plan has been incorporated into the national AI development plan and has received cross-ministry support.

Google CEO: Capital expenditures in 2026 will reach $185 billion; ramping up investment in the era of AI agents

Google CEO Sundar Pichai announced at Google Cloud Next in Las Vegas on April 22 that Google plans to invest $175 billion to $185 billion in 2026 in capital expenditures to build the infrastructure needed for autonomous AI agents, up from $31 billion in 2022.

Google Jules releases a new version candidate list, repositioning it as an end-to-end product development platform

According to the official April 23 announcement by the Google Jules team, Jules’s product positioning has been upgraded from an asynchronous coding agent to an “end-to-end agentic product development platform.” The new version can read the full product context, independently determine the next steps for building, and submit a PR. The official also announced that the new version candidate list is now open.

Perplexity Discloses Web Search Agent Post-Training Method; Qwen3.5-Based Model Outperforms GPT-5.4 on Accuracy and Cost

Perplexity uses SFT followed by RL with Qwen3.5 models, leveraging a multi-hop QA dataset and rubric checks to boost search accuracy and efficiency, achieving best-in-class FRAMES performance. Abstract: Perplexity's post-training workflow for web-search agents combines supervised fine-tuning (SFT) to enforce instruction-following and language consistency with online reinforcement learning (RL) via the GRPO algorithm. The RL stage uses a proprietary multi-hop verifiable QA dataset and rubric-based conversational data to prevent SFT drift, with reward gating and within-group efficiency penalties. Evaluation shows Qwen3.5-397B-SFT-RL achieving top FRAMES performance, 57.3% accuracy with a single tool call and 73.9% with four calls at $0.02 per query, outperforming GPT-5.4 and Claude Sonnet 4.6 on these metrics. Pricing is API-based and excludes caching.

OpenAI Codex Team Fixes OpenClaw Authentication Bug, Significantly Improves Agent Behavior

OpenClaw switches from Pi to Codex harness to fix a silent authentication fallback, with two PRs addressing the bridge and fallback; post-fix, the agent shifts from shallow heartbeat polling to a full work loop, enabling progress. Abstract: OpenClaw’s Codex harness optimization addressed a critical authentication flaw that caused silent fallback to the Pi harness when using Codex with OpenAI models. Two pull requests fix the authentication bridge and prevent silent fallback, changing the runtime adapter. As a result, agent behavior evolves from shallow heartbeat polling to a full work loop that reads context, analyzes tasks, edits repositories, and verifies progress, improving continuity and visibility across heartbeats.