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Insights on agentic AI, from agent architectures and LLM infrastructure to enterprise deployment and developer tooling. Our team shares practical guides on building AI agents, optimizing model pipelines, and scaling AI systems in production.
Written for CTOs, developers, AI engineers, and technical leaders who are building or deploying agentic AI. Each article includes actionable takeaways grounded in real-world implementation.
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Building, deploying, and managing autonomous AI agents for workflow automation, customer support, internal operations, and more.
LLM InfrastructureModel selection, hosting, fine-tuning, cost optimization, and scaling LLM-powered systems in production.
Enterprise AIStrategies for deploying AI at scale across organizations, including governance, compliance, and change management.
Developer ToolsMCP servers, CLIs, SDKs, APIs, and open source tooling for building on agentic AI platforms.
IndustryAI applications across education, healthcare, finance, government, and other verticals.
ConferencesTranscripts and key takeaways from major education and AI conferences including ASU+GSV Summit.
Showing 913-928 of 928 posts
Cambridge: How Educators Can Help Future Learners Outwit the Robots
Professor Rose Luckin's keynote at the Cambridge Summit emphasizes that while AI can transform education, nurturing uniquely human skills such as social intelligence and meta-cognition is crucial, and ethical, collaborative development between educators and AI developers is essential for future learning.
Deloitte: Powering Artificial Intelligence – A Study of AI's Environmental Footprint, Today and Tomorrow
Deloitte's report assesses AI's growing environmental impact, noting that data center energy use may nearly triple by 2030 due to AI demands. It advocates for strategies like renewable energy adoption, improved efficiency, ecosystem collaboration, and greater transparency to achieve "Green AI" and calls for joint action from industry and policymakers to ensure a sustainable future.
Google: LearnLM – Improving Gemini for Learning
LearnLM is a Google AI model designed for educational settings that follows detailed pedagogical instructions to improve teaching effectiveness. Human evaluations show it outperforms existing models in various learning scenarios, and future work will explore additional educational applications.
University of Michigan: Artificial Intelligence Research Committee Recommendations Report
The report recommends significant investments in computing, personnel, and ethical oversight to boost U-M's AI capabilities, advocating for better internal coordination, a centralized AI resource hub, and enhanced national and industry collaborations.
Capgemini: Harnessing the Value of Generative AI - 2nd Edition: Top Use Cases Across Sectors
Capgemini’s report examines the widespread adoption of generative AI across industries, highlighting increased investments, improved productivity, and enhanced customer satisfaction. It emphasizes the growing role of AI agents, the need for strong governance, and addresses ethical and environmental concerns based on insights from a global survey of 1,100 executives.
Microsoft/Accenture: Unlocking the Economic Potential of the US Generative AI Ecosystem
The white paper examines how the US generative AI ecosystem can boost the economy by 2038, focusing on increased productivity, innovation, and investment, while highlighting the need for strong partnerships, skilled workers, robust infrastructure, clear policies, and public trust.
Hangzhou Normal University: Does ChatGPT Enhance Student Learning? A Systematic Review and Meta-Analysis of Experimental Studies
This review of 69 experimental studies found that ChatGPT interventions improved students' academic performance, affective motivation, and higher-order thinking while reducing mental effort, though it had no significant effect on self-efficacy and many studies had methodological limitations.
George Washington University Law School: Artificial Intelligence and Privacy
Daniel J. Solove’s piece argues that current privacy laws—focused mainly on individual control—are inadequate for addressing the systemic harms posed by AI, and calls for a regulatory framework based on harm analysis and structural reforms.
U.S. House of Representatives: Bipartisan House Task Force Report on Artificial Intelligence
A bipartisan House task force report assesses the impact of AI on privacy, national security, society, and the economy, while offering recommendations for responsible development and regulation.
Anthropic: Clio – Privacy-Preserving Insights into Real-World AI Use
Clio is a privacy-preserving AI system that analyzes aggregated conversation data to uncover usage patterns and cultural differences while enhancing AI safety and misuse detection, all without compromising individual privacy.
World Economic Forum: Leveraging Generative AI for Job Augmentation and Workforce Productivity
The report explores how generative AI can enhance job roles and workforce productivity by outlining future scenarios based on varying levels of trust and technological enhancement. It includes insights from early adopters and provides a framework for organizations to effectively implement and scale GenAI across their operations.
Anthropic: The Dawn of GUI Agent – A Preliminary Case Study with Claude 3.5 Computer Use
This study evaluates Claude 3.5 Computer Use—a novel AI model that interacts with GUIs via API—to understand its capabilities and limitations in executing tasks across various software, guiding future improvements in GUI automation.
Deloitte: Tech Trends 2025
Deloitte's Tech Trends 2025 report forecasts a future where AI seamlessly underpins all aspects of business and technology, influencing everything from hardware and cybersecurity to core system modernization.
Google DeepMind: A New Golden Age of Discovery
AI is transforming scientific research by accelerating key areas like knowledge synthesis and experimental simulation, while also requiring careful strategies, investments, and policies to manage risks and ensure sustainable, equitable innovation.
Google DeepMind: New Golden Age of Discovery
AI is transforming scientific research by accelerating key areas like knowledge synthesis, data management, simulation, and complex modeling, while urging strategic investments and interdisciplinary collaboration to harness its benefits and address potential risks.
National Academies: Artificial Intelligence and the Future of Work
The report examines how AI, particularly large language models, could boost productivity and reshape job markets by creating new roles and displacing existing ones, while emphasizing the need for investments in skills, infrastructure, ethical oversight, improved data collection, and lifelong learning.