Architect your platform for the autonomous AI era.
Glintbase conducts empirical multi-agent sandbox audits to eliminate context token tax, resolve broken authentication loops, and certify agent operability.
Human-first interfaces break autonomous agents.
When software surfaces are built exclusively for human browsing, autonomous coding agents (Claude Code, Cursor, Devin) stall and fail.
Excess tokens wasted by agents traversing circular docs, unanchored SDKs, and bloated schemas.
Autonomous workflows that stall or hallucinate credentials when authentication flows are undocumented for machines.
Code quickstarts and SDK snippets that fail headless runtime execution due to undeclared environment dependencies.
A 4-week empirical engineering engagement.
From automated surface discovery to multi-agent sandbox validation and continuous CI/CD PR drift gating.
Surface Discovery & AST Inspection
Automated 119-Check Taxonomy Scan
Comprehensive structural crawl across OpenAPI 3.1 schemas, documentation trees, SDK repositories, and machine surfaces (llms.txt, auth.md) using the 119-check ARS 3.0 protocol.
Executive & engineering artifacts.
We deliver concrete code diffs, telemetry replay logs, and automated CI gates — not high-level abstract slide decks.
Executive Board Dossier
Strategic briefing document tailored for CTOs, VPs of Engineering, and Board review. Quantifies AI discoverability risk, competitive agent benchmark standing, and platform readiness posture.
Engineered for enterprise procurement.
Every audit is executed under strict zero-trust parameters with formal legal protections.
Standard mutual NDA executed prior to discovery or architecture scoping.
We inspect the same public or staging surfaces AI agents encounter. Zero system writes.
Agent simulations run in isolated micro-VMs cryptographically wiped post-run.
Optional on-premise or private cloud runner deployment for sovereign data residency.
Schedule your platform architecture review.
Complete the technical intake console to schedule an initial scoping session with Glintbase researchers.
Direct inquiry: enterprise@glintbase.dev
From the Glintbase Research Lab.
The peer-reviewed conceptual models powering our audit heuristics and scoring formulas.
The Internet Was Built for Humans. AI Agents Need a Different Interface.
Why AI Doesn’t "Use" Software the Way Humans Do
Agent Readiness: The Missing Metric in Modern Software
Enterprise Audit FAQ.
Procurement, security, code access, and execution details.
A standard Enterprise Assessment is conducted across 3 to 4 weeks. Discovery and automated AST crawling occur in Week 1, multi-agent sandbox simulations in Week 2, counterfactual fix verification in Week 3, and executive delivery with CI drift shield handoff in Week 4.
No. Glintbase evaluates the surfaces that autonomous AI systems actually interact with: documentation, public or staging APIs, SDKs, onboarding workflows, and machine discovery files (llms.txt, OpenAPI). Source code access is only requested if you specifically commission internal repository linting.
Yes. For pre-production or internal platforms, we support secure execution via VPC peering, dedicated wireguard tunnels, temporary IP whitelisting, or isolated runner instances deployed inside your private cloud environment.
We execute mutual non-disclosure agreements (NDAs) prior to technical discovery or platform scoping. All findings, telemetry traces, and architectural documentation remain strictly confidential to your organization and are never used to train models or published publicly.
Autonomous flight simulations run inside isolated, ephemeral micro-VMs (E2B / Firecracker). All test credentials are scoped with zero-trust permissions, and container environments are cryptographically destroyed immediately upon simulation completion.
The open-source @glintbase/cli runs local static rule analysis. The Enterprise Audit provides multi-agent runtime execution with real LLM reasoning loops, counterfactual sandbox testing, competitor benchmarking, tailored PR diffs, and an executive board dossier presented directly to your engineering leadership.
Yes. Our audit inspects MCP tool definitions, JSON-RPC 2.0 schema validity, token payload budgets, error recovery protocols, and stdio/SSE streaming latencies against the 119-check ARS 3.0 standard.