AI just shipped three power moves reshaping agent governance, long-term AI strategy, and scientific intelligence. From tightly controlled enterprise agents to bold 2026 forecasts and benchmarks that test real scientist workflows, here’s what’s new:

🧠 Vertex AI Agents — Governance at Production Scale
Google upgrades Vertex AI Agent Builder with org-wide tool governance via Cloud API Registry, MCP-based integrations, Gemini 3 support, and advanced agent state control making enterprise agents safer, scalable, and truly production-ready.

🔮 10 Bold AI Predictions for 2026 — Agents Become Digital Teams
From inference overtaking training spend to AI agents, robots, and open models reshaping industries, Rob Toews maps a fast-approaching future where enterprises, energy, regulation, and trust are rebuilt around agent-first systems.

🔬 SGI-Bench — Measuring AI Like a Scientist
SGI-Bench introduces a rigorous benchmark aligned with real scientific workflows, exposing where LLMs still fail at long-horizon reasoning, experimentation, and discovery setting the bar for true Scientific General Intelligence.

AI isn’t just scaling anymore it’s being governed, forecasted, and scientifically stress-tested.

Vertex AI Agents: Govern Tools Like Never Before

Google Cloud has introduced enhanced tool governance in Vertex AI Agent Builder through integration with Cloud API Registry, enabling administrators to curate and manage approved tools directly in the console for organization-wide use. This includes pre-built tools for Google services like BigQuery and Google Maps via MCP support, custom MCP servers from Apigee for existing APIs, and simplified developer access via a new ApiRegistry object in the Agent Development Kit (ADK). Key updates accelerate agent building with full ADK support for Gemini 3 Pro and Flash, TypeScript compatibility, advanced state management (failure recovery, human-in-the-loop pauses, context rewind), Interactions API for multimodal I/O, and A2UI for secure LLM-generated UIs. Scaling improvements feature general availability of Agent Engine's sessions and memory (powered by ACL 2025 research), expanded regional support, and pricing updates starting January 28, 2026. Customer examples from Burns & McDonnell, Payhawk, Gurunavi, and SeaArt highlight efficiency gains in knowledge application, financial automation, and personalized creativity. Developers can explore via GitHub adk-samples, Agent Garden, and the Startup Technical Guide.

10 Bold AI Predictions for 2026: From Agents to Robots

Rob Toews' Forbes article outlines 10 bold AI predictions for 2026, emphasizing rapid evolution amid fierce competition. Key forecasts include US-China AI decoupling accelerating with export controls and domestic chip production; inference eclipsing training in compute spend (60-70% allocation); small, efficient models dominating via techniques like test-time compute and distillation; AI agents maturing into "digital teams" for complex tasks in enterprises; robotics surging with humanoid bots from Tesla, Figure, and Boston Dynamics hitting warehouses; synthetic data exploding to train models without real-world limits; energy demands skyrocketing, pushing nuclear revival and efficiency gains; regulatory divergence—EU tightening while US stays light-touch; multimodal AI advancing voice/video interfaces; and open-weight models challenging closed giants via community innovation. Toews warns of a "fusion moment" integrating AI, blockchain for trust, and robots via A2A protocols, urging businesses to adapt marketing to agents and prioritize verifiable human content as trust signals. Holiday robot gifts and AI-optimized brands signal cultural shifts, with specialization (e.g., cooking bots) trumping generalists for ROI. Leaders must rebuild trust amid breakneck pace, as blockchain logs agent actions for accountability.

SGI-Bench: Scientist Workflows for AI

SGI-Bench, from InternScience on GitHub, introduces a rigorous benchmark for Scientific General Intelligence (SGI) in LLMs, aligning evaluations with real scientist workflows across 10 disciplines like physics, biology, and materials science—inspired by Science’s 125 Big Questions. Over 1,000 expert-curated tasks by 100+ PhD/Master's holders undergo multi-stage validation for executability, uniqueness, and challenge (filtering >50% LLM-solved items). Structured via the Practical Inquiry Model, it tests four stages: Scientific Deep Research (iterative deliberation, 10-20% exact match); Idea Generation (feasible hypothesis design); Dry/Wet Experiments (code/protocol execution, high executability but low accuracy); and Experimental Reasoning (multimodal analysis). Results expose LLM gaps in long-horizon reasoning, planning, and perception despite agentic tools. Complemented by multi-metric protocols (e.g., PassAll@5, sequence fidelity), it provides scalable assessment for advancing SGI toward autonomous discovery. Check the repo for leaderboard, agent framework, and arXiv paper.

Hand Picked Video

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Top AI Products from this week

• My Vision Board - Stop pinning stock photos of other people's lives. Upload a selfie, set your 2026 milestones, and let our Gemini-powered engine generate hyper-realistic photos of YOU achieving them. See yourself in that car, on that trip, or in that home. 100% Free.

• Chrone AI - Intelligent scheduling powered by local AI. Your calendar works anywhere, anytime. free Beta is out, one click install

• Story Generator Pro - Story Generator Pro is the #1 AI story generator. Create personalized stories featuring yourself, your child, your loved ones or any real characters. Story Generator Pro lets you create, edit, and manage all your personalized stories in one place.

• Sooko.ai - Discover cutting-edge AI courses and powerful tools designed to accelerate your learning journey. Join thousands of learners transforming their careers with artificial intelligence.

• ​Image Aware AI Support - Image Handling lets AskYura’s AI understand images inside chat, screenshots, product photos, and proof of payment, so it can respond with accurate, helpful answers immediately. By understanding screenshots and images, the AI reduces back-and-forth, responds more accurately.

• 1 Heart Point - 1HP is a proof first workflow for founders. Add research notes from interviews, calls, and exploration. 1HP summarizes them into structured candidate insights with evidence. You approve what’s real, convert insights into decisions, and maintain a weekly.

This week in AI

• Gemini's AI Video Detector - Google Gemini now detects AI-generated videos via SynthID watermarks in its app. Upload clips ≤100MB/90s, ask "Was this made with Google AI?"—it scans audio/visuals for Google tools only. Builds on image detection for transparency.

• Instacart Ends AI Pricing Tests - Instacart halts Eversight AI tool after backlash from Consumer Reports probe showing up to 23% price hikes for same items. Retailers tested dynamic pricing on platform; FTC scrutiny followed. Same-store prices now uniform, but discounts/promos continue.

• Human Path for AI - MIT Sloan Dean Richard Locke urges making work more human amid AI rise. AI excels at routine tasks but falters in judgment, empathy, creativity. Leaders must redesign jobs for human strengths like collaboration, ethics—boosting productivity 2-3x vs. automation alone.

• Async Coding Agents DIY - Ben Anderson builds custom async coding agents by sandboxing tools like Claude Code/Codex for remote execution. Bypasses cloud limits (env vars, packages); lets any model run background tasks autonomously. Shifts bottleneck from coding to task delegation—launch 94 jobs, review outputs later.

• Context Engineering Skills - Muratcan Koylan's repo offers 100+ agent skills for context engineering in production AI systems. Covers fundamentals like context degradation, compression; tackles lost-in-middle, attention scarcity. Load skills on-demand for optimal token use across platforms.

Paper of The Day

ActionFlow accelerates Vision-Language-Action (VLA) models on edge devices via cross-request pipelining. Batches memory-bound Decode phases with compute-bound Prefill across timesteps using fused Cross-Request State Packed Forward operator and Unified KV Ring Buffer. Achieves 2.55× FPS speedup on OpenVLA-7B (3.2 FPS on Jetson AGX Orin) without retraining or accuracy loss, enabling 20-30Hz robotic control.

To read the whole paper 👉️ here