Beyond AI Vibes: Deterministic Foundations for Agentic Coding
AI coding adoption is high and trust is dropping. A testing pyramid for agents, plus reproducible production context that grounds AI in real behavior.
Browse 23 posts in this category
AI coding adoption is high and trust is dropping. A testing pyramid for agents, plus reproducible production context that grounds AI in real behavior.
Trace-based testing uses OpenTelemetry traces as replayable test input so CI catches production regressions before deploy, not after incident review.
Observability tells you what failed—but not how to recreate it. Why reproducibility is the missing fourth pillar, and what that means for incident response.
SaaS AI fails when agents need continuous access to your codebase and internal APIs. Here's why BYOC is the only deployment model that works at scale.
Learn a practical workflow to convert Datadog metrics, traces, and incidents into CI tests that catch regressions before deploy.
LLMs have collapsed the cost of custom internal tools. Here's the startup distribution problem I've watched kill companies — and how I vibe-coded my way out.
Teams spend six figures on observability but test with synthetic data. Close the gap between what you know about production and what you validate pre-release.
RBAC and DLP let developers access production data safely—without configuration drift or PII exposure. Here's how to design it right.
AI-generated code is moving fast—but without behavioral validation, you're gambling with production stability. See how Proxymock changes the equation.