AI Agents
Building, deploying, and managing autonomous AI agents for workflow automation, customer support, internal operations, and more.
AI agents represent the next evolution in enterprise automation—intelligent systems that can reason, plan, and take action autonomously. Unlike simple chatbots, AI agents handle complex multi-step tasks across customer support, internal operations, data analysis, and specialized workflows. Discover how agentic AI is transforming how organizations operate.
510 articles in this category
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.
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.
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.
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.
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.
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.
K-12 AI: Unify District Data With an Ontology
K-12 AI agents fail when student data is scattered across the SIS, LMS, assessment, and special-education systems. The prerequisite is an ontology — a governed knowledge graph the district owns and self-hosts — that unifies those silos before any agent is deployed.
Ontology vs RAG for AI Agents: Why You Need Both
RAG retrieves text by similarity; an ontology gives agents structured entities, relationships, and governed actions. Agents that act need both — and you should own the layer, not rent it inside a vendor's index.
Higher Education AI: Unify Campus Data With an Ontology
Higher-ed AI agents fail when student data is scattered across the SIS, LMS, CRM, and financial aid systems. The prerequisite is an ontology — a governed knowledge graph the institution owns and self-hosts — that unifies those silos before any agent is deployed.
Enterprise AI Data Integration: The Ontology-First Approach
Enterprise AI agents fail when employee, customer, and operational data is scattered across CRM, HRIS, ERP, ITSM, and the data warehouse. The fix is an ontology — a governed knowledge graph the company owns and self-hosts — that unifies those silos before any agent ships.
The Karpathy Lesson for K-12: Teach Comprehension, Not Just Usage
Andrej Karpathy coined vibe coding, then stopped using AI for his most important work. His reasoning holds a critical lesson for how K-12 schools should teach AI.
Healthcare AI Agents Need a Unified Patient Ontology
Self-hosted AI agents for healthcare break when patient data is scattered across EHR, scheduling, claims, and lab systems. The prerequisite is an ontology — a governed patient data layer the health system owns and runs itself — that unifies those silos before any agent is deployed.
Financial Services AI: Unify Data Silos With an Ontology
Self-hosted AI for financial services breaks when customer data is scattered across core banking, CRM, risk, and KYC/AML systems. The prerequisite is an ontology — a governed knowledge graph the institution owns and runs itself — that unifies those silos before any agent is deployed.
Sovereign AI for Government Starts With a Data Ontology
Sovereign AI for government agencies fails when constituent data is scattered across case management, benefits, permitting, and records systems. The prerequisite is an ontology — a governed knowledge graph the agency owns and runs itself — that unifies those silos before any agent is deployed.
Why AI Agents Fail Without an Ontology: Unify Data First
Most enterprise AI agents fail for one reason: organizational data is trapped in silos — SIS, LMS, CRM, ERP, HRIS. The fix isn't a better model. It's an ontology — a governed knowledge graph you own — built first, with agents deployed on top. Why data unification comes before automation.
Why 94% of Government AI Pilots Stall — And What Sovereign Infrastructure Changes
New research shows only 6% of organizations have deployed AI to production. Government agencies face even steeper odds — but sovereign AI infrastructure built on ownership, not licensing, is closing the gap.
Why the Transformer Co-Author's Move to OpenAI Should Reshape How Universities Think About AI Infrastructure
Noam Shazeer's move from Google to OpenAI signals that the next AI architectural shift is imminent. Universities locked into single-vendor AI platforms risk building on foundations that could become obsolete overnight.
What Is an Enterprise LLM Platform? The One You Own
An enterprise LLM platform lets a company build, deploy, and govern LLM applications and agents on its own infrastructure. The version that wins is the one you own outright — all the code and data, any model, no per-seat tax.
Why AI Agent Security in K-12 Requires a Different Playbook
NVIDIA's SkillSpector found 26.1% of AI agent skills contain vulnerabilities. In K-12, where students are minors and regulations are strictest, the stakes are even higher.