Treblle Blog
What matters in the enterprise API world.
OpenAPI Describes Your API. But Who Tells AI Agents How to Use It Safely?
OpenAPI tells AI agents what an API can do, not what is safe to do. Overlays, Arazzo, and MCP annotations add context. Runtime checks still decide.
The Manchester Airports breach didn't need a hacker. It needed an API key nobody was watching
Manchester Airports Group lost 8.8 million records to an API key sitting in public JavaScript for four years. The real failure was runtime visibility and API governance.
SAFR Assumes You Have Treblle
SAFR (Safeguards for Agentic Finance at Runtime) is a white paper published in July 2026 by the Monetary Authority of Singapore's BuildFin.ai program, written with eight industry members including HSBC, J.P. Morgan Chase, Mastercard, and Visa. It specifies a runtime governance checkpoint for AI agents in financial services, built around four components: Agent Identity, Controls Repository, Disposition Engine, and Audit Log.
API Testing: A Practical Guide for Backend Teams
API testing is the practice of verifying that an API behaves as documented under real conditions. It covers whether endpoints return correct responses for valid inputs, handle errors gracefully, enforce authentication and authorization, meet latency and throughput requirements, and match their documented schemas. It's distinct from unit testing (which tests internal logic) and end-to-end testing (which tests full user journeys through a UI).
API Monetization: Models, Strategies, and What Actually Works
API monetization is the practice of generating revenue from an API by charging consumers for access or usage. The primary models are freemium (free up to a usage threshold, paid above it), pay-per-call (a flat rate per individual API request), tiered pricing (flat monthly fees with usage caps per tier), and enterprise licensing (negotiated flat fees for high-volume or high-value consumers). Choosing the right model requires usage distribution data, consumer behavior analysis, and an understanding of which billing unit best captures the value the API delivers.
API Analytics: What to Measure and What the Numbers Mean
API analytics is the measurement and analysis of API traffic data to understand usage patterns, consumer behavior, performance characteristics, and business value. It goes beyond aggregate call counts to cover per-consumer usage decomposition, endpoint-level traffic distribution, latency percentile tracking, and error rate analysis. API analytics answers the product and operational questions that raw call volume alone can't: which consumers are growing, which endpoints are being abandoned, where latency is degrading, and how usage data maps to pricing and monetization decisions.
