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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.
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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.
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Showing 361-384 of 985 posts
The NextGen Financial Firm Runs Its Own AI
Financial firms outsourced analytics to Bloomberg and CRM to Salesforce. Outsourcing AI — which processes client data and makes compliance decisions — is a different risk entirely.
The NextGen Agency Runs Its Own AI
Agencies outsourced email to the cloud. Outsourcing AI — which processes mission data, makes decisions, and touches classified systems — is a fundamentally different risk.
The NextGen Health System Runs Its Own AI
Healthcare systems outsourced EHR to Epic and billing to Waystar. Outsourcing AI — which processes PHI and supports clinical decisions — is a fundamentally different risk.
The NextGen University Runs Its Own AI
The last decade's trend was outsourcing everything to SaaS. The next decade's trend in higher ed is bringing AI back under institutional control.
The NextGen Law Firm Runs Its Own AI
Law firms outsourced research to Westlaw and document management to the cloud. Outsourcing AI — which processes privileged data — is a fundamentally different decision.
How School Districts Can Pilot AI Without Losing Control of Student Data
The superintendent approved an AI pilot. Three months later, eight teachers are using unapproved tools with student data. Here's how to enable experimentation without chaos.
How to Organize for AI Experimentation Without Losing Institutional Control
Most organizations respond to AI by creating a center of excellence and a governance committee. Six months later, departments have quietly deployed three different chatbot vendors.
How Enterprises Can Organize for AI Experimentation Without Shadow IT
The CIO created an AI center of excellence. Six months later, twelve business units have deployed their own chatbots with company data flowing to unapproved servers.
How Financial Firms Can Experiment with AI Without Creating Regulatory Exposure
The CIO approved an AI pilot for risk modeling. Three trading desks are already using unapproved tools with client data. Here's how to enable experimentation without SEC exposure.
How Government Agencies Can Experiment with AI Without Compromising Security
The agency CIO approved an AI pilot. Three divisions are already using unapproved tools. Here's how to enable experimentation within ATO boundaries.
How Healthcare Systems Can Experiment with AI Without Creating HIPAA Exposure
The CMO approved an AI pilot for clinical decision support. Three departments are already using unapproved tools with patient data. Here's how to enable experimentation safely.
How Universities Can Organize for AI Experimentation Without Shadow IT
The provost created an AI task force. Six months later, twelve departments have deployed their own chatbots with student data flowing to servers nobody can name.
How Law Firms Can Experiment with AI Without Compromising Privilege
The managing partner approved an AI pilot for discovery. Three practice groups are already using unapproved tools with client data. Here's how to enable experimentation safely.
Why Teachers Don't Adopt AI Tools — And What Districts Can Do About It
Teacher adoption of district-approved AI tools rarely exceeds 15%. More PD sessions won't fix it. Giving teachers control over what the AI teaches will.
Enterprise AI Adoption Fails Because of Vendors, Not Employees
Enterprise AI adoption stalls at 25%. The standard fix is more training. The actual fix is giving business units control over what the AI does.
Why Financial Services Professionals Don't Adopt AI Tools — And What Fixes It
Compliance officers won't use AI tools they can't audit. That's not resistance — it's regulatory diligence. Here's what actually drives adoption in finance.
Why Government Workers Don't Adopt AI Tools — And What Actually Fixes It
Government AI adoption stalls because staff can't explain the tool's reasoning in an audit. That's not resistance — it's accountability. Here's what fixes it.
Why Clinicians Don't Adopt AI Tools — And What Healthcare Systems Can Do About It
Clinician adoption of AI tools remains below 20% at most health systems. More training won't fix it. Proving where PHI stays will.
Why Faculty Don't Adopt AI Tools — And What Actually Fixes It
Faculty adoption of AI tools hovers below 20% at most universities. The standard fix is more training. The actual fix is giving faculty control over the platform.
Why Attorneys Don't Adopt AI Tools — And What Firms Can Do About It
Attorney adoption of AI tools hovers below 20% at most firms. More CLE sessions won't fix it. Giving attorneys control over privilege protection will.
Platform Adoption Fails Because of Vendors, Not Users
The conventional wisdom on AI platform adoption: buy the tool, train the users, manage the change. When adoption stalls, blame culture. This is backwards.
The Real ROI of AI in K-12: Why Per-Seat Pricing Breaks at District Scale
Your three-school AI pilot cost $24,000. Scaling to 47 schools will cost $1.4 million a year — for a platform the district doesn't own. Here's a better framework.
The Real ROI of Enterprise AI: Stop Measuring Pilots, Start Measuring Ownership
Your AI pilot showed 40% faster onboarding. Now the vendor wants $30/employee/month to scale it to 10,000 employees. Here's the ROI framework that changes the math.
The Real ROI of AI in Financial Services: Beyond the Pilot, Before the Regulatory Risk
Your compliance AI pilot caught 3x more violations. Now the vendor wants a multi-year contract — and the Chief Risk Officer wants to know who controls the audit logs.