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.
Co-founder and CEO of Speedscale, passionate about performance engineering. • 9 posts published
AI coding adoption is high and trust is dropping. A testing pyramid for agents, plus reproducible production context that grounds AI in real behavior.
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.
Record real LLM traffic from a FastAPI app, mock it locally, and replay it in CI with proxymock.
Production AI spend gets attention. Non-prod LLM calls in development, CI, and load tests often do not. Simulation fixes that.
Most flaky test fixes focus on retries and quarantine. The real fix is replacing hand-written test data with recorded traffic that stays fresh.
Compare WireMock, MockServer, proxymock for enterprise Java API mocking. See how each handles recorded traffic, Kubernetes, CI/CD, AI-generated code.
Enterprise Spring Boot APIs need more than unit tests. Learn how to test external service integrations, JWT auth, production edge cases with real traffic.
Static analysis catches code smells. Runtime validation catches behavioral failures. Enterprise teams adopting AI coding tools need both to ship safely.
Choose the desktop ProxyMock or the hosted cloud trial to get started.