Developer Tools
MCP servers, CLIs, SDKs, APIs, and open source tooling for building on agentic AI platforms.
Building on agentic AI platforms requires the right developer tools—from MCP servers and CLIs to SDKs, APIs, and integration frameworks. Explore open source tooling, integration guides, and developer resources for building, extending, and connecting AI-powered applications.
615 articles in this category
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.
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.
OWASP: LLM Applications Cybersecurity and Governance Checklist
The document outlines a cybersecurity checklist for organizations using large language models (LLMs). It emphasizes balancing the benefits and risks of LLMs, incorporating security measures into existing practices, providing specialized AI security training, and implementing continuous testing and validation to ensure ethical deployment and robust defenses against threats.
ETS: 2025 Human Progress Report
The report reveals a global shift toward skills-based credentials—particularly AI literacy and continuous learning—as critical for advancing education and career growth, while highlighting both rising progress and ongoing concerns about tech obsolescence, especially among Gen Z.
University of California Irvine: What Large Language Models Know and What People Think They Know
The study reveals that users tend to overestimate large language models' accuracy due to discrepancies between the models' internal confidence and the users' interpretation, with longer explanations and specific uncertainty language boosting user confidence regardless of actual accuracy. Tailoring LLM responses to better reflect internal uncertainty can help bridge this calibration gap, improving trustworthiness in AI-assisted decisions.
Stanford University: The Labor Market Effects of Generative Artificial Intelligence
Stanford's research finds that around 30% of workers have used Generative AI at work, with particularly high adoption among younger, educated, and higher-income individuals in customer service, marketing, and IT; users experience significant productivity gains, often reducing task times by two-thirds, indicating that Generative AI can both replace and enhance various forms of labor.
Hugging Face: Fully Autonomous AI Agents Should Not Be Developed
The paper argues that fully autonomous AI agents, which operate without human oversight, pose serious risks to safety, security, and privacy. It recommends favoring semi-autonomous systems with maintained human control to balance potential benefits like efficiency and assistance against vulnerabilities in accuracy, consistency, and overall risk.
University of Cologne: AI Meets the Classroom – When Does ChatGPT Harm Learning?
LLMs can aid coding education when used as personal tutors by explaining concepts, but over-reliance on them for solving exercises—especially via copy-and-paste—can impair actual learning and lead students to overestimate their progress.
MIT Sloan: AI Detectors Don't Work – Here's What to Do Instead
AI detection tools are unreliable; instead, educators should set clear AI use guidelines, foster open discussions, and design engaging, inclusive assignments to promote genuine learning.
Anthropic: Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations
The study analyzes four million Claude.ai conversations mapped to US occupational tasks, revealing that AI is mainly used to augment specific tasks—especially in software development, writing, and other cognitive roles—rather than to replace entire jobs. It finds that mid-to-high wage occupations are using AI significantly, with different models specializing in distinct tasks, highlighting a nuanced, task-specific impact of AI on the economy.