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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.
Explore Topics
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 928 posts
AI Agent Security Is an Infrastructure Problem, Not a Feature
Uber's security lead says securing AI agents is what keeps him up at night, and Google just shipped agent evaluation tooling to production. The tooling layer is maturing; the infrastructure question underneath it is not. This post explains why you cannot fully secure an agent whose reasoning runs on someone else's servers, and gives the five-question perimeter test to run on any agent platform before you sign.
Q2 2026 Earnings: AI Infrastructure Pays — For Whoever Owns It
The quarter ending June 30, 2026 settled the question of whether AI infrastructure pays off: AWS grew 37% to $42.2B, Google Cloud 82% to $24.8B, Azure crossed $100B annualized, and Copilot passed 30 million paid seats. This post does the arithmetic on what those seats cost a 10,000-person enterprise versus token-priced and self-hosted alternatives, and shows where the return actually lands.
SaaS Fragmentation Is the Hidden Cost of Enterprise AI
Enterprises run six or seven per-seat tools that each hold a partial copy of the same customer. That fragmentation, not model capability, is what stalls AI deployment — and it carries a per-seat bill that grows with headcount. This post itemizes the fragmentation tax and shows the MCP-based orchestration layer that reads across every system instead of adding another one.
AI Budgets Are Growing 40% a Year. Deployment Isn't.
Enterprise AI investment is compounding near 40% a year — roughly double cloud and mobile at the same stage — yet most of it never reaches production. This post introduces deployment yield, the ratio of AI budget attached to systems real users touch, and shows why the missing control plane, not model capability, is what security and compliance actually block on.
The AI Harness Thesis: Orchestration Beats Model Selection
Enterprises spend their AI strategy debating which model to buy. The model is the commodity — it is replaced every few months and its price falls. The harness around it (retrieval, validation, routing, memory) is the durable asset, and it only compounds if you own it.
Self-Hosted Voice AI Agents for Hospital Health Systems
What it actually costs to run outbound voice AI agents on hospital-owned infrastructure, which BAAs you still need, and where PHI travels during an AI phone call.
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.