Documentation
Prose references, guides, and conceptual content.
Agent Readiness Infrastructure
Software Was Built for Humans.The Next Users Are AI Agents.
Glintbase helps companies measure, understand, and improve how AI agents discover, navigate, and operate software across documentation, APIs, onboarding, SDKs, and support surfaces.
The way software is consumed has fundamentally changed.
For decades, every assumption baked into software design was built around a human on the other end. That assumption no longer holds.
Human user
AI agent
01
Browse
Retrieve
02
Search
Reason
03
Click
Execute
04
Read
Infer
05
Scroll
Consume context
Most software was designed for none of this. The documentation, APIs, onboarding, and support surfaces that exist today were built entirely for human consumption — and they break in predictable ways when AI agents attempt to use them.
Current software breaks when AI agents try to use it.
These are not edge cases. They are the predictable, structural consequences of building software that was never designed for machine consumption.
01
AI wastes context.
When documentation is unstructured, narrative, or formatted for human reading, agents consume enormous token budgets attempting to extract actionable information. Most of what they consume is irrelevant to their task.
02
AI encounters dead ends.
Incomplete API references, broken cross-links, undocumented parameters, and missing authentication flows cause agents to stall mid-journey. Without recovery paths, they give up or hallucinate continuations.
03
AI hallucinates.
When structural information is absent or ambiguous, agents fill gaps with plausible-seeming fabrications. This is not a model failure — it is a documentation failure. Missing context forces inference.
04
AI repeats work.
Without persistent, structured knowledge representations, every agent session begins from scratch. Agents re-parse the same surfaces, re-resolve the same ambiguities, and re-discover the same information.
05
AI consumes excessive tokens.
Prose-heavy documentation, inconsistent structures, and redundant content dramatically increase the token cost of operating software. At scale, this translates directly to operational expense and latency.
An intelligence layer for software.
Glintbase sits between your existing software and the AI agents that need to operate it. It continuously extracts, structures, and optimises the knowledge those agents require.
Runtime analysis
Evaluates software surfaces as they actually behave, not as they are documented to behave.
Graph intelligence
Maps relationships between entities, surfaces, endpoints, and concepts across an entire codebase.
Semantic understanding
Derives meaning from structure — not just text — to build representations agents can navigate.
Journey simulation
Traces the paths an AI agent would follow to complete real tasks, identifying failures before they occur.
Intelligence pipeline
Every surface your software exposes to agents.
Scanner analyses ten distinct software surfaces. Hover any card to see what we look for.
APIs
REST, GraphQL, and RPC endpoint surfaces.
SDKs
Client libraries, type definitions, and wrappers.
GitHub
READMEs, wikis, and repository structure.
Support Centers
Help articles, FAQs, and troubleshooting guides.
Onboarding
Setup guides, quickstarts, and first-run experiences.
Authentication
OAuth, API keys, and identity flows.
MCP
Model Context Protocol servers and tool manifests.
OpenAPI
OpenAPI 3.x and Swagger specification files.
CLI
Command-line interfaces and developer tooling.
Soon
More coming
LLMs.txt, changelogs, runbooks, internal wikis.
Measure agent readiness. For free.
Run Scanner against any public URL. In minutes, you receive a detailed readiness report across six dimensions — no account required.
- AI Readiness Score (0–100)
- Context Health analysis
- Structural Health evaluation
- AI Journey Simulation
- Machine Entry Point detection
- Confidence assessment
For teams who need certainty.
Deep Audit is an expert-led engagement that delivers a complete understanding of your software's agent readiness — and a precise roadmap to improve it.
AI Journey Simulation
Full end-to-end tracing of agent paths through your software surfaces.
Runtime Validation
Live execution testing of documentation code examples and API calls.
Knowledge Graph
A complete entity-relationship map of your software's agent-facing structure.
Executive Recommendations
Prioritised action plans with estimated token savings and risk reduction.
Hallucination Risk Assessment
Identifies specific gaps in coverage that are causing or will cause agent hallucination.
Agent Readiness Roadmap
Phased implementation plan to reach full agent operability.
Latest research. Built for AI agents.
Built in the open.
The core of what Glintbase is building is open source. We believe agent readiness infrastructure should be inspectable, forkable, and community-owned.
glintbase/glintscanner
stable
Open-source agent readiness scanner. Analyses documentation, APIs, SDKs, and support surfaces for AI operability.
TypeScript
@glintbase/cli
stable
Command-line interface for running Scanner locally. Integrates into CI/CD pipelines and pre-deploy checks.
TypeScript
@glintbase/mcp
stable
Model Context Protocol server that exposes agent readiness data and documentation queries as structured tools.
TypeScript
glintbase/sdk
planned
Official SDK for integrating agent readiness checks and documentation intelligence directly into your application.
TypeScript
Vision
Software increasingly serves machines.
Software is increasingly operating in a world where its primary consumers are not humans.
AI agents — systems that retrieve, reason, execute, and infer — are becoming active participants in every software workflow. They read documentation. They call APIs. They follow onboarding flows. They use CLIs, SDKs, and support surfaces.
This is happening now. And virtually no software was designed for it.
The gap between what software exposes and what AI agents need to operate it effectively is one of the most consequential infrastructure problems of the next decade.
Glintbase exists to close that gap.
We are building the intelligence layer that enables software to become understandable, navigable, and operable by the machines that increasingly depend on it.
This is not a documentation product. It is not an AI writing assistant. It is infrastructure — the kind that sits underneath, does its work quietly, and becomes foundational.
The companies that build this layer into their software now will have a compounding structural advantage as the agentic era develops.
We are building that layer.
Get Started
Help shape the agentic internet.
Run a free scan on any URL. Or join the waitlist to be part of what we build next.