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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 817-840 of 982 posts
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.
How ibl.ai Keeps Your Campus’s Carbon Footprint Flat
This article outlines how ibl.ai enables campuses to scale generative AI without scaling emissions. By right-sizing models, running a single multi-tenant back end, enforcing token-based (pay-as-you-go) budgets, leveraging RAG to cut token waste, and choosing green hosting (renewable clouds, on-prem, or burst-to-green regions), universities keep energy use—and Scope 2 impact—flat even as usage rises. Built-in telemetry pairs with carbon-intensity data to surface real-time CO₂ per student metrics, aligning AI strategy with institutional climate commitments.
How ibl.ai Makes Top-Tier LLMs Affordable for Every Student
This article makes the case for democratizing AI in higher education by shifting from expensive per-seat licenses to ibl.ai—a model-agnostic, pay-as-you-go platform that universities can host in their own cloud with full code and data ownership. It details how campuses cut costs (up to 85% vs. ChatGPT in a pilot), maintain academic rigor via RAG-grounded, instructor-approved content, and scale equity through a multi-tenant deployment that serves every department. The takeaway: top-tier LLM experiences can be affordable, trustworthy, and accessible to every student.
How ibl.ai Cuts Cost Without Cutting Capability
This article explains how ibl.ai helps campuses deliver powerful AI—tutoring, content creation, and workflow support—without runaway costs. Instead of paying per-seat licenses, institutions control their TCO by choosing models per use case, hosting in their own cloud, and running a multi-tenant architecture that serves many departments on shared infrastructure. An application layer and APIs provide access to hundreds of models, hedging against price swings and lock-in. Crucially, ibl.ai keeps quality high with grounded, cited answers, faculty-first controls, and LMS-native integration. The piece outlines practical cost curves, shows how to right-size models to tasks, and makes the case that affordability comes from architectural control—not compromises on capability.
ibl.ai for Your University's Website
The article introduces ibl.ai, an AI chatbot tailor‑trained on a university’s own public and internal content to provide prospective students with immediate, accurate answers while freeing admissions staff from repetitive emails.
Microsoft Education AI Toolkit
Microsoft’s new AI Toolkit guides institutions through a full-cycle journey—exploration, data readiness, pilot design, scaled adoption, and continuous impact review—showing how to deploy AI responsibly for student success and operational efficiency.
Nature: LLMs Proficient Solving & Creating Emotional Intelligence Tests
A new Nature paper reveals that advanced language models not only surpass human performance on emotional intelligence assessments but can also author psychometrically sound tests of their own.
Multi-Agent Portfolio Collab with OpenAI Agents SDK
OpenAI’s tutorial shows how a hub-and-spoke agent architecture can transform investment research by orchestrating specialist AI “colleagues” with modular tools and full auditability.
BCG: AI-First Companies Win the Future
BCG’s new report argues that firms built around AI—not merely using it—will widen competitive moats, reshape P&Ls, and scale faster with lean, specialized teams.
McKinsey: Seizing the Agentic AI Advantage
McKinsey’s new report argues that proactive, goal-driven AI agents—supported by an “agentic AI mesh” architecture—can turn scattered pilot projects into transformative, bottom-line results.
LEGO/The Alan Turing Institute: Understanding GenAI Impact on Children
A new study reveals how children aged 8–12 are already using tools like ChatGPT, highlighting benefits, risks, and the urgent need for child-centred AI design and literacy.
OpenAI: Disrupting Malicious Uses of AI - June 2025
OpenAI’s latest threat-intelligence report reveals how ten malicious operations—from deep-fake influence campaigns to AI-generated cyber-espionage tools—were detected and dismantled, turning AI against the actors who tried to exploit it.
Oakland University: The Memory Paradox
Oakland University’s latest paper warns that offloading too much thinking to digital tools can erode human memory systems, arguing for education that strengthens internal knowledge even while embracing AI.