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.
519 articles in this category
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.
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.