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Today’s AI news signals a clear shift from experimentation to orchestration, governance, and scale—with agents, evaluation, and integration taking center stage. Agentic software development goes mainstream. OpenAI introduced the Codex app for macOS, positioning it as a command center for AI-native software development: multi-agent workflows, parallel execution, and long-running tasks. This marks a step change from “AI-assisted coding” to AI-managed engineering, where coordination, memory, and execution matter as much as model quality. In parallel, platforms like Together AI are standardizing cross-provider evaluations, reflecting a maturing market that now compares models on cost, latency, and reliability—not just benchmarks. Governance, testing, and regulation are catching up. Debates are sharpening around how to regulate AI: a growing view (echoed by IEEE Spectrum) is to regulate AI use, not models, especially as the EU AI Act grapples with fragmentation. Stanford HAI and new research also highlight a deeper issue—today’s tests still fail to measure real reasoning, robustness, and uncertainty handling. The takeaway: evaluation and policy are now bottlenecks, not model capability. Infrastructure and convergence accelerate. The biggest structural signal came from xAI joining SpaceX, creating a vertically integrated stack spanning data, compute, deployment, and real-world systems. Combined with advances in neuromorphic and energy-efficient computing, the direction is clear: AI is being rebuilt as long-lived, embedded infrastructure, not just cloud APIs. Bottom line: 2026 is shaping up as the year of agentic coordination, serious evaluation, and institutional AI—where trust, governance, and integration determine winners more than raw model scale. #ArtificialIntelligence #AIAgents #AgenticAI #AIInfrastructure #AIGovernance #ModelEvaluation #FutureOfWork #SoftwareEngineering #FrontierAI / issue-100-sam-ghosh-pvoic https://asthalavista.com/