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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.
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 745-768 of 922 posts
ibl.ai On Thinkific: Investling’s AI Agent
How Investling embedded ibl.ai directly into Thinkific to deliver a goal-aware, risk-profiled investing agent—with in-video chat, mobile access, and persistent learner memory that turns passive lessons into personalized coaching.
AI That Moves the Needle on Learning Outcomes — and Proves It
How on-prem (or university-cloud) ibl.ai turns AI-powered tutoring into measurable learning gains with first-party, privacy-safe analytics that reveal engagement, understanding, equity, and cost—aligned to your curriculum.
Weekly Platform Updates — September 26, 2025
Weekly platform update for September 26, 2025, highlighting Comprehensive Analytics for Instructors, Screen Share for Students, Canvas & Brightspace Deep Linking, and the ibl.ai iOS App—plus a partnership spotlight with UC San Diego.
ibl.ai: An AI Operating System for Educators
A practical blueprint for an on-prem, LLM-agnostic AI operating system that lets universities personalize learning with campus data, empower faculty with control and analytics, and give developers a unified API to build agentic apps.
ibl.ai: The Platform for Campus Builders
A practical look at how ibl.ai gives universities Python/Web SDKs and a unified API to build, embed, and measure agentic apps with campus data—on-prem or in their cloud.
ibl.ai Evidence of Impact
An academic analysis of the ibl.ai platform — the learning theories behind its design, the features that drive student engagement, and documented learning outcomes from deployments at GWU, Morehouse, and Syracuse.
American University of Sharjah × ibl.ai: Course-Tuned AI Agents for Calculus & Physics
AUS and ibl.ai are launching a fall pilot of course-tuned AI agents for Calculus and Physics that use a code interpreter to compute, visualize, and cite instructor-approved resources—helping students learn reliably and transparently.
Seamless LTI Deep Linking in Canvas, Brightspace and Blackboard with ibl.ai
A step-by-step walkthrough of how ibl.ai supports LTI Deep Linking in Canvas, Brightspace, Blackboard, and other compliant LMS platforms—allowing instructors to embed AI agents directly into courses with minimal setup and a seamless launch experience.
Human-In-The-Loop Course Authoring With ibl.ai
This article shows how ibl.ai enables human-in-the-loop course authoring—AI drafts from instructor materials, faculty refine in their existing workflow, and publish to their LMS via LTI for speed without losing academic control.
Cost Math University CFOs Love With ibl.ai
Why universities save—and gain control—by owning their AI application layer. We compare $20/user/month retail pricing to a low six-figure campus license that routes to developer-rate APIs, show breakevens (e.g., ≈$300k vs multi-million retail), and outline the governance, safety, and adoption benefits CFOs and provosts care about.
Let AI Handle The Busywork With ibl.ai
How ibl.ai designs course-aware assistants to offload busywork—so students can be present, collaborate with peers, and build real relationships with faculty. Practical patterns, adoption lessons, and pilots you can run this term.
Guided, Proactive AI Agents on ibl.ai
Guided, proactive AI agents from ibl.ai are course-aware assistants that know your units and outcomes, nudge learners with timely suggestions, and cite your slides/readings by default—bringing structure, transparency, and better study habits to every class.
How ibl.ai Helps Build AI Literacy
A pragmatic, hands-on AI literacy program from ibl.ai that helps higher-ed faculty use AI with rigor. We deliver cohort workshops, weekly office hours, and 1:1 coaching; configure course-aware assistants that cite sources; and help redesign assessments, policies, and feedback workflows for responsible, transparent AI use.
Per-Course and Per-Student AI Agents on ibl.ai
How ibl.ai enables per-course and per-student assistants that answer with cited sources, follow instructor-defined pedagogy, and respect domain-specific safety—so campuses get precision, transparency, and control without the complexity.
Cited Answers By Design with ibl.ai
An overview of ibl.ai’s Document Retrieval—answers that cite the exact lecture/slide/page, a ranked Source Panel that updates as you chat, one-click opening of the originals, and admin-level visibility controls—so campuses get transparent AI that teaches students to verify claims and helps faculty keep content governance simple.
ibl.ai's Custom Safety & Moderation Layers in ibl.ai
An explainer of ibl.ai’s custom safety & moderation layer for higher ed: how domain-scoped assistants sit on top of base-model alignment to enforce campus policies, cite approved sources, and politely refuse out-of-scope requests—consistent behavior across Canvas (LTI 1.3), web, and mobile without over-permitting access.
No Vendor Lock-In, Full Code & Data Ownership with ibl.ai
Own your AI application layer. Ship the whole stack, keep code and data in your perimeter, run multi-tenant deployments, choose your LLMs, and integrate via LTI—no vendor lock-in.
ibl.ai's Multi-LLM Advantage
How ibl.ai’s multi-LLM architecture gives universities one application layer over OpenAI, Google, and Anthropic—so teams can select the best model per workflow, keep governance centralized, avoid vendor lock-in, and deploy across LMS, web, and mobile. Includes an explicit note on feature availability differences across SDKs.
UCSD's ibl.ai Collaboration
UC San Diego is partnering with ibl.ai to pilot an instructor-centered assistant that analyzes student drafts and suggests top, rubric-aligned comments from UCSD’s approved comment banks—keeping faculty in full control while scaling high-quality feedback in writing-intensive courses.
Owning Your AI Application Layer in Higher Ed With ibl.ai
A practical case for why universities should run their own, LLM-agnostic AI application layer—accessible via web, LMS, and mobile—rather than paying per-seat for closed chatbots, with emphasis on cost control, governance, pedagogy, and extensibility.
Security-First LMS Integration
A practical, standards-aligned overview of how ibl.ai integrates with Canvas, Blackboard, and Brightspace using admin-registered LTI 1.3, optional, IT-approved RAG ingest, and course-scoped links—delivering security, transparency, and instructor control without fragile workarounds.
How ibl.ai Makes AI Simple and Gives University Faculty Full Control
A practical look at how ibl.ai pairs “factory-default” simplicity with instructor-level control—working out of the box for busy faculty while offering deep prompt, corpus, and safety settings for those who want to tune pedagogy and governance.
Roman vs. Greek Experimentation: Pilot-First Framework
A practical, pilot-first framework—“Roman vs. Greek” experimentation—for universities to gather evidence through action, de-risk AI decisions, and scale what works using model-agnostic, faculty-governed deployments.
How ibl.ai Keeps Faculty at the Heart of the ibl.ai Experience
This article explains how ibl.ai keeps instructors at the center of teaching with an LLM-agnostic, faculty-controlled platform that delivers grounded answers from course materials, streamlines grading and content prep, and integrates directly with campus systems—cutting costs while preserving academic rigor and the human connection in learning.