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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 97-120 of 922 posts
The Semantic Layer AI Agents Need — and Who Should Own It
A warehouse semantic layer gives dashboards consistent metrics; AI agents need that plus an operational layer — actions, permissions, audit — with governance. ibl.ai ships both as one open-source, MIT-licensed ontology you self-host and own.
Ontology vs Taxonomy vs Knowledge Graph: What AI Needs
A taxonomy classifies things into a hierarchy; an ontology adds typed relationships, attributes, and actions; a knowledge graph is the ontology populated with your real data. AI agents need all three levels — and you should own the whole stack.
How to Build an Organizational Ontology: A Practical Guide
A practical, step-by-step guide to building an organizational ontology: model the nouns, add the verbs, connect your systems once over MCP, govern access by role, and ship the whole layer from a CLI — open source, self-hosted, and owned by you.
What Is a Data Ontology? Definition, Layers, and Examples
A data ontology is a structured, machine-readable map of your organization's entities, relationships, and actions that AI agents reason over. Definition, the two layers, ontology vs database schema, a concrete cross-system example — and an open-source, self-hosted implementation you own.
AI Agents Already Work in K-12 — Just Not Where Districts Are Looking
K-12 districts are chasing AI tutoring demos while the proven ROI sits in administrative workflows. IEP compliance, attendance tracking, and multilingual parent communication are where AI agents already deliver measurable results.
Microsoft Is Replacing OpenAI Models With Its Own — What This Means for Enterprise AI Strategy
Microsoft is quietly swapping OpenAI and Anthropic models for its in-house MAI family across M365. The company that invested $13B in OpenAI just demonstrated why every enterprise needs model-agnostic infrastructure.
GPT-5.6 and Model Routing: Why Enterprise AI Must Be Model-Agnostic
OpenAI's GPT-5.6 Sol/Terra/Luna launch proves enterprises need model-agnostic infrastructure — not vendor commitment.
Implementation Requirements for AI Agents on Your IT Stack
What are the implementation requirements for deploying custom AI agents within an organization's existing IT infrastructure? The six requirement areas — identity, data integration, compute, guardrails, audit, and operations — with the concrete checklist for each.
Enterprise AI OS Pricing vs Standard Cloud AI Services
How does enterprise AI operating system pricing compare to standard cloud AI services? The three pricing shapes, the same workload priced each way, and why the OS layer should cost like the API — not like a per-seat suite.
AI Platforms for Universities That Keep Data On-Premise
What are the best AI platforms for universities that need to keep student data on-premise? The direct answer, the FERPA case for on-premise, the honest vendor landscape, and the cost math at a 30,000-student university.
AI OS Platforms That Deploy Agents on Your Infrastructure
Which AI operating system platforms let you deploy AI agents on your own infrastructure? A direct answer, the honest vendor landscape, what 'your own infrastructure' actually means, and the requirements checklist buyers should use.
MiniMax's 2.7-Trillion-Parameter Model Proves Enterprise AI Must Be Model-Agnostic
MiniMax is preparing a 2.7-trillion-parameter open-source model — the largest ever. Here is why enterprises that locked into a single model vendor are about to pay for it.
K-12 AI Vendor Subscriptions vs Infrastructure You Own
Both the US and China are now restricting access to frontier AI models. K-12 districts relying on vendor-hosted AI subscriptions face the same risk — and there is a better path.
Paying for Tokens Isn't Buying AI Value — Own the Stack
Token spend is a cost, not an outcome. The organizations getting real AI value run an LLM-agnostic architecture and an owned application layer, so every dollar of usage compounds into an asset they keep.
AI Ownership: The Four Questions Every Buyer Must Ask
The value of enterprise AI concentrates in the application layer — the ontology — not the model. Four ownership questions (data, weights, application layer, compute) decide whether that value is yours or your vendor's.
Why Government Agencies Cannot Afford to Rent Their AI Infrastructure
AWS and Microsoft just committed $3.5B to forward-deployed AI engineering. Government agencies that rent this infrastructure instead of owning it are building dependency into their most sensitive systems.
Open Models in Closed Environments: The Sovereign AI Playbook
The Palantir-NVIDIA partnership reveals the emerging blueprint for sovereign AI: open-source models deployed inside closed government infrastructure.
The Sovereign AI Movement: Why Governments Are Building Their Own AI — And Why It Matters
Five European nations are building sovereign AI foundation models. This isn't about nationalism — it's about control. Here's what the movement means for government AI strategy worldwide.
Rampart and the Rise of Sovereign AI: Why Governments Are Building Their Own Models
The US government just open-sourced its first AI model. Rampart is 14.7 MB, runs locally, and signals a fundamental shift in how governments approach AI infrastructure.
The Open-Source Model Explosion Is Rewriting Enterprise AI Strategy
A food delivery company built a frontier AI model. Export controls pulled another offline. The enterprise takeaway: own your infrastructure or lose access to it.
The Fable 5 Blackout Proved Universities Need LLM-Agnostic AI Infrastructure
When the US government restricted Fable 5 and limited Mythos 5 to 100 organizations, universities locked into single-vendor AI learned the cost of dependency. Here is why LLM-agnostic infrastructure is now a strategic imperative for higher education.
Why MCP Is the Data Layer for AI Agents
The Model Context Protocol lets AI agents reach your systems through one governed interface — connect each source once, with scoped, audited access and no data extraction. It's the integration layer a private AI program is built on, and you run it yourself.
Vector Database vs Knowledge Graph for AI Agents
A vector database finds similar text; a knowledge graph models entities, relationships, and permitted actions. AI agents need both — and you should own the layer rather than rent it inside a vendor's index.
Legal AI: Unify Firm Data With an Ontology
Legal AI agents fail when matter data is scattered across the DMS, practice-management, docketing, and billing systems. The prerequisite is an ontology — a governed knowledge graph the firm owns and self-hosts — that unifies those silos before any agent is deployed.