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.
770 articles in this category
ibl.ai on AWS: Seamless Integration with Bedrock, SageMaker, and the AWS Gen AI Stack
Institutions that run on AWS can deploy ibl.ai directly inside their existing VPC, leveraging Amazon Bedrock for managed model access, SageMaker for custom fine-tuning, and the full AWS security and observability stack—without introducing new vendors or moving data outside their account boundary.
ibl.ai on Google Cloud: Deep Integration with Vertex AI, Gemini, and the GCP Gen AI Stack
Institutions running on Google Cloud can deploy ibl.ai directly on GKE with Vertex AI as the model backbone—accessing Gemini 2.0, Gemma, Llama 3, and more through a single API. VPC Service Controls keep student data inside the institution's perimeter, while Cloud Monitoring provides full cost and performance visibility.
ibl.ai on Microsoft Surface Copilot+ PCs: Local AI Tutoring Powered by the NPU
ibl.ai runs directly on Microsoft Surface Copilot+ PCs, using the built-in Neural Processing Unit (NPU) to deliver real-time AI tutoring and content tools without requiring a cloud connection. Students get instant, on-device mentoring; faculty get powerful authoring tools; and institutions keep every byte of data local.
Microsoft Fabric + ibl.ai: Unified Data Analytics Meets AI Tutoring via MCP
Institutions already running Microsoft Fabric for data analytics can now extend their investment into AI-powered tutoring and mentoring with ibl.ai—connected through the Model Context Protocol (MCP). This post shows how OneLake, Power BI, and Fabric's unified data lakehouse feed directly into ibl.ai's AI agents, giving universities a single pane of glass for learning analytics and intelligent student support.
Why AI Architecture Matters More Than AI Capability
Microsoft's AI chief says white-collar automation is 12 months away. But the real challenge isn't whether AI can do the work — it's whether institutions can deploy AI within the constraints that actually matter: privacy, pedagogy, and control.
MiniMax M2.5 and the New Economics of Agentic AI
MiniMax M2.5 delivers frontier-level agent performance at ~$1/hour. We break down the technical benchmarks, cost economics, and what this means for institutions deploying agentic AI at scale.
The Real-Time AI Race: What GPT-5.3 Codex-Spark and Gemini 3 Deep Think Mean for Education
OpenAI and Google both shipped major model updates today — one optimized for real-time coding, the other for deep scientific reasoning. Here's what educators and platform builders need to understand about this divergence, and why LLM-agnostic architecture matters more than ever.
Why Researchers Need AI Agents with Sandboxes, Not Just Chatbots
Simple chatbot wrappers like GPTs and Gems are useful — but researchers need AI agents that can actually execute code, process data, and produce reproducible results. We explore why sandboxed AI agents are the next frontier for academic research.
Admissions Automation: Complete Guide for Higher Education
A comprehensive guide to automating higher education admissions processes, from application processing to enrollment confirmation.
Admissions Communication Plan: Building Effective Student Outreach
How to build an effective admissions communication plan that guides prospective students from inquiry through enrollment.
Admitted Student Personalization: Strategies That Improve Yield
How to personalize the admitted student experience to improve yield, from communication strategies to event personalization.
Agentic AI for Cybersecurity: Protecting Digital Assets Autonomously
How AI agents enhance cybersecurity operations through autonomous threat detection, response, and remediation.
Agentic AI for Enterprise: A Comprehensive Implementation Guide
A comprehensive guide to implementing agentic AI in enterprise environments, from strategy through deployment and optimization.
Agentic AI in Retail: How Agents Are Transforming Commerce
How AI agents are transforming retail operations from inventory management to customer experience, and what retailers need to know.
Agentic AI Orchestration: Managing Multi-Agent Systems
How to orchestrate multiple AI agents that work together, including coordination patterns, conflict resolution, and production best practices.
Agentic AI Platforms: Complete Comparison Guide for 2026
A comprehensive comparison of agentic AI platforms for 2026, examining capabilities, architecture approaches, and enterprise readiness.
AI Agent Companies: The Complete Industry Landscape for 2026
A comprehensive map of the AI agent market for 2026, covering key players, categories, and emerging trends.
AI Agent Evaluation: Frameworks for Measuring Agent Performance
How to evaluate AI agent performance using structured frameworks, meaningful metrics, and practical benchmarking approaches.
AI Agent Governance: Managing Autonomous AI Systems Responsibly
How to govern AI agents that operate autonomously, including policy frameworks, monitoring strategies, and risk management approaches.
AI Agent Management: How to Run AI Agents at Scale
Practical guidance for managing, monitoring, and scaling AI agents in production environments.
AI Agent Security: How to Protect Autonomous AI Systems
Security considerations unique to autonomous AI agents, including attack surfaces, defense strategies, and monitoring approaches.
AI Agents in Healthcare: Transforming Patient Care Operations
How AI agents improve healthcare operations including patient triage, administrative automation, and clinical decision support.
AI Automation Services: Transforming Business Operations
How AI automation services transform business operations, what to look for in a provider, and how to measure ROI.
AI Compliance Monitoring: Tools and Best Practices for 2026
How to implement effective AI compliance monitoring using the right tools and best practices to stay ahead of evolving regulations.