Observability on dplabs — Software Engineering & Technology Consultancy

Building AI Features Without Losing Control of Your Architecture

Mon, 06 Apr 2026 00:00:00 +0000

AI features are getting added to production systems quickly — often quickly enough that the usual architectural discipline gets skipped. The integration is direct: OpenAI SDK in the service, API calls scattered through business logic, prompts hardcoded in the calling code.

This works for a proof of concept. At production scale, with real operational requirements, it creates a class of problems that are expensive to fix after the fact.

The Vendor Lock-in Problem

Directly using the OpenAI SDK, the Anthropic SDK, or any provider’s client library throughout your codebase creates tight coupling to that provider’s interface. When you want to:

AI Engineering Is Software Engineering

Mon, 09 Mar 2026 00:00:00 +0000

There’s a tendency to treat AI features as different from other software. Different enough that normal engineering practices don’t apply, or apply differently, or can be deferred. This tendency produces AI systems that are unreliable, unobservable, expensive to operate, and difficult to improve.

AI systems built with the same engineering discipline as other distributed systems are more reliable, cheaper to operate, and easier to improve. The disciplines aren’t different. The application is.

Spring Boot Observability: Metrics, Tracing, and Logs That Actually Help

Mon, 31 Mar 2025 00:00:00 +0000

Spring Boot 3.x has strong observability support built in. Spring Boot Actuator, Micrometer for metrics, Micrometer Tracing (built on OpenTelemetry), and structured logging all work out of the box with minimal configuration.

The gap between “instrumented” and “observable” is wider than it looks. Adding dependencies and enabling endpoints is instrumentation. Being able to answer “why are 3% of requests failing between the payment service and the inventory service, and since when?” is observability.