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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.
Our editorial team publishes new content weekly, drawing on deployment data from 400+ organizations and 1.6M+ users. Every piece is reviewed by practitioners with hands-on experience building AI platforms.
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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.
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Showing 841-864 of 928 posts
Harvard Business School: The Cybernetic Teammate – A Field Experiment on Generative AI Reshaping Teamwork and Expertise
The paper shows that generative AI can act as a "cybernetic teammate" by considerably enhancing knowledge work. In field experiments at Procter & Gamble, individuals using AI achieved performance comparable to human teams, produced balanced solutions across functional lines, and experienced more positive emotions. Overall, the study suggests that AI not only boosts efficiency but also transforms team dynamics and innovation strategies.
Baruch College: Not all AI is Created Equal – A Meta-Analysis Revealing Drivers of AI Resistance Across Markets, Methods, and Time
The meta-analysis reveals that while consumers generally show a slight aversion to AI (Cohen’s d = -0.21), resistance is context-dependent—stronger for embodied forms like robots and high-risk domains—and evolves over time, with negative evaluations decreasing, especially in settings with greater ecological validity.
CSET: Putting Explainable AI to the Test – A Critical Look at Evaluation Approaches
The brief discusses how explainable AI is evaluated in recommendation systems, highlighting a lack of clear definitions for key concepts and an overemphasis on system correctness rather than real-world effectiveness. Researchers mainly use case studies and comparative evaluations, with less focus on methods that assess operational impact. The study concludes that clearer standards and expert evaluation methods are needed to ensure that explainable AI is genuinely effective.
Harvard Business School: The Value of Open Source Software
This study reveals that open source software (OSS) provides massive economic benefits, with a small supply-side cost of about $4.15 billion versus an enormous demand-side value around $8.8 trillion, emphasizing its crucial role in saving costs and boosting productivity across industries.
Hoover Institution: The Artificially Intelligent Boardroom
Artificial intelligence is set to reshape corporate boardrooms by enhancing information processing, decision-making, and various governance functions. At the same time, its adoption raises challenges such as maintaining board independence, managing data security, and avoiding potential biases in AI models.
Harvard Business School: Why Most Resist AI Companions
Research indicates that despite AI companions offering benefits like constant availability and non-judgment, people resist forming genuine relationships with them because they believe AI lacks the core emotional depth and mutual caring required for true interpersonal connections.
Center for AI Policy: US Open-Source AI Governance – Balancing Ideological and Geopolitical Considerations with China Competition
The document examines U.S. open-source AI policies amid tensions between promoting innovation and safeguarding against security risks in the context of US-China competition. It argues that targeted, nuanced interventions—rather than broad restrictions—are needed to balance open access with mitigating misuse, while emphasizing continuous monitoring of technological and geopolitical shifts.
National Security: Superintelligence Strategy
The document proposes a national security strategy for advanced AI that leverages deterrence through Mutual Assured AI Malfunction (MAIM), nonproliferation via tight controls on AI technology and information, and competitiveness by boosting domestic capabilities and legal frameworks—all aimed at mitigating the risks of superintelligence while maintaining global strategic balance.
Monash University: Gen AI in Higher Ed – A Global Perspective of Institutional Adoption Policies and Guidelines
This study analyzes generative AI policies at 40 universities worldwide, revealing a focus on academic integrity, enhancing teaching, and AI literacy, while exposing gaps in comprehensive frameworks for data privacy and equitable access. It also highlights varied regional priorities and communication strategies, with clear roles assigned to faculty, students, and administrators.
UNESCO: AI Competency Framework for Students
UNESCO's AI Competency Framework for Students outlines 12 key competencies—spanning a human-centered mindset, ethical awareness, practical AI skills, and system design—designed to progressively prepare students to critically engage with and responsibly shape the future of AI.
PWC: Agentic AI – An Executive Playbook
Agentic AI leverages autonomous, human-like reasoning to optimize workflows and drive business growth by reducing costs, improving customer experience, and enhancing decision-making. It requires strategic planning, robust infrastructure, and ethical guidelines, and has evolved through advances in machine learning, NLP, and multimodal data integration.
Harvard Business School: Global Evidence on Gender Gaps and Generative AI
Global research shows that women are less likely than men to adopt and effectively use generative AI tools, largely due to lower familiarity, confidence, and concerns about ethical use, which may worsen existing inequalities and bias in AI systems.
UC Berkeley: Responsible Use of Generative AI – A Playbook for Product Managers and Business Leaders
This playbook offers product managers and business leaders strategies for using generative AI responsibly by addressing risks like data privacy, inaccuracy, and bias while enhancing transparency, compliance, and brand trust.
Coursera: 2025 Job Skills Report
The report reveals a rapid rise in demand for skills in generative AI, computer vision, machine learning, and cybersecurity, while also emphasizing the growing importance of data ethics and sustainability. It calls for coordinated upskilling and reskilling efforts among individuals, businesses, educational institutions, and governments to remain competitive in a technology-driven job market.
McKinsey: The Critical Role of Strategic Workforce Planning in the Age of AI
McKinsey highlights the crucial need for strategic workforce planning in the age of AI, advocating for proactive talent investments, skill gap analysis, multiscenario planning, innovative hiring, and integrating these practices into daily business operations to secure long-term competitiveness and agility.
Open Praxis: The Manifesto for Teaching and Learning in a Time of Generative AI – A Critical Collective Stance to Better Navigate the Future
The manifesto critically examines generative AI in higher education, arguing that while it offers personalized learning and efficiency, it also risks reinforcing biases, eroding human creativity and judgment, and devaluing educators. It calls for ethical, evidence-based approaches that prioritize AI literacy and rethinking education to maintain human agency.
Microsoft: The AI Decision Brief – Insights from Microsoft and AI Leaders on Navigating the Generative AI Platform Shift
Microsoft’s AI Decision Brief highlights how generative AI is rapidly transforming industries, emphasizing the importance of aligning strategies with different stages of AI readiness, ensuring trustworthy AI via security, privacy, and safety, and demonstrating significant ROI potential for organizations that embrace advanced AI practices.
Georgia Institute of Technology: It’s Just Distributed Computing – Rethinking AI Governance
The paper argues that “AI” isn’t a single technology but a collection of machine learning applications embedded within a broader digital ecosystem. It suggests that rather than regulating AI as a whole, policymakers should focus on the specific impacts of individual applications, as broad strategies often entail unrealistic and potentially authoritarian control of the entire digital ecosystem.
George Mason University: Generative AI in Higher Education – Evidence from an Analysis of Institutional Policies and Guidelines
Higher education institutions are increasingly embracing generative AI, particularly for writing tasks, with many providing detailed classroom guidance. However, they also face ethical, privacy, and pedagogical challenges, as well as concerns about the long-term impact on intellectual growth.
Digital Education Council: Global AI Faculty Survey 2025
The survey reveals that most faculty have experimented with AI in teaching, though its use tends to be limited. Many are worried about students’ over-reliance on AI and their ability to critically assess its output, while also noting that institutions lack clear AI guidance. Additionally, a significant number advocate for reforming student assessments, although a strong majority remain optimistic about the future integration of AI in teaching.
Google: Towards an AI Co-Scientist
The AI co-scientist is a multi-agent system that accelerates biomedical research by generating, debating, and refining hypotheses through iterative improvements and expert feedback, with its capabilities validated in drug repurposing, target discovery, and antimicrobial resistance.
OpenAI: Building an AI-Ready Workforce – A Look at College Student ChatGPT Adoption in the US
OpenAI's report finds that many US college students are self-learning AI skills, leading to uneven adoption across states, and emphasizes the urgent need for clear institutional and nationwide AI education policies to build an AI-ready workforce.
MIT: The AI Agent Index
The MIT AI Agent Index is a public database that catalogs agentic AI systems—tools capable of planning and executing tasks with minimal human oversight—by detailing their technical components, applications, and risk management practices. It reveals that most systems are developed in the USA, mainly by companies in software engineering, and while many projects offer open code and documentation, information on safety policies and external evaluations remains limited.
Artificial Analysis: State of AI in China – Q1 2025
Chinese AI labs have achieved language model and reasoning capabilities comparable to leading US technologies, aided by strong government and Big Tech support. The report also highlights the impact of US export controls on NVIDIA accelerators and outlines detailed hardware benchmarks for AI development.