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.
619 articles in this category
Deloitte: The Cognitive Leap – How to Reimagine Work with AI Agents
The white paper advocates for using multiagent AI systems to transform business processes through scalable, human-in-the-loop designs, supported by industry examples and a detailed implementation framework.
IBM: The CEO's Guide to Generative AI – 2nd Edition
IBM's report offers CEOs a concise guide to leveraging generative AI for transforming their businesses. It highlights strategies for digital innovation, IT automation, ethical AI implementation, and talent management, emphasizing a human-centered approach and strategic investment to maximize benefits while managing risks.
MIT Technology Review: A Playbook for Crafting AI Strategy
The report highlights strong AI ambitions among executives but notes progress is often limited to pilots due to high costs, data quality, and regulatory challenges. It offers strategic guidance for building a robust data foundation, choosing vendors, and measuring ROI to successfully scale AI initiatives.
George Mason University: Artificial Intelligence Policy Framework for Institutions
The paper proposes an ethical AI policy framework for institutions that focuses on data privacy, bias mitigation, energy efficiency, and the importance of interpretability to build trust, illustrated through case studies in various sectors including education and healthcare.
IBM: Enterprise AI Development – Obstacles and Opportunities
A survey of 1,063 US enterprise AI developers revealed significant skills gaps—especially in generative AI—and challenges from a lack of standardized processes and trusted, easy-to-integrate tools, with ongoing concerns about AI agents’ trustworthiness and compliance.
University of Chicago: Agentic Systems – A Guide to Transforming Industries with Vertical AI Agents
The content explains agentic systems—industry-specific AI agents powered by large language models—that offer real-time adaptability, domain expertise, and complete workflow automation through components like memory, reasoning engines, and cognitive modules.
World Economic Forum: Navigating the AI Frontier – A Primer on the Evolution and Impact of AI Agents
This white paper examines the evolution of AI agents—from simple rule-based systems to advanced models capable of complex decision-making—and discusses their benefits, risks, and the critical need for robust ethical and governance frameworks to manage their growing role in society.
UNESCO: Guidance for Generative AI in Education and Research
UNESCO's guidance outlines ethical and responsible use of generative AI in education and research, addressing potential biases, copyright issues, and digital inequalities, while recommending human-centered strategies and regulatory measures for its integration and competency development.
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.
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.
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.
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.