Research & Insights
Agent Readiness Journal
Empirical research, protocol standards, and engineering papers on software architecture for the autonomous AI era.
The $433k Token Tax: Why Bad Docs Drain AI Infrastructure Budgets
How defective docs cause AI coding agents to waste $433k per year in token overhead across a 10-developer team. Learn to calculate and eliminate token tax.
The Agent Resilience Paradox: Why 'Working' Software Fails AI Agents
Discover why software that works for humans regularly fails AI coding agents, and how the Agent Readiness Score captures what traditional QA misses.
Beyond /llms.txt: The Full Machine-Readable Stack for AI Agents
Explore the full machine-readable stack: llms.txt, mcp.json, and OpenAPI schemas that transform software into a first-class AI-operable surface.
State of Agent Readiness 2026: What We Found Across 75 Software Platforms
Empirical findings from AI agent simulations across 75 production software platforms scored on ARS 1.0, revealing the state of machine readiness in 2026.
How to Gate CI/CD on Agent Readiness: A Practical Glintbase CLI Tutorial
Use the Glintbase CLI to enforce Agent Readiness checks in CI/CD, blocking releases when machine-facing documentation falls below target thresholds.
Agent Readiness: The Missing Metric in Modern Software
Why software teams need to measure how well their products work for AI agents, not just humans.
Why AI Doesn’t "Use" Software the Way Humans Do
Why agents retrieve, traverse, execute, and infer differently — and why software must be designed for that reality.
The Internet Was Built for Humans. AI Agents Need a Different Interface.
Why software needs an agent-facing layer alongside human UIs, and how structured context and machine entrypoints unlock autonomous AI operability.
What is AI Readiness? The New Benchmark for Codebases
Understanding how machine-operable software is changing the meaning of good documentation, good architecture, and good developer experience.
Docs Drift Is Killing Your AI Coding Productivity
Why outdated documentation quietly breaks AI coding agents, and why static analysis plus runtime validation is becoming essential.
The Future of Agent-Operable Software
Why the next generation of software will need machine-readable documentation, context layers, and operational structure for AI agents.
Why Most RAG Systems Fail Developer Documentation
Why retrieval alone is not enough for technical docs, and why structural context beats simple vector search.