AI Engineering

The hardest part of Structured Outputs is not getting JSON once. It is surviving schema changes without turning production into a small fire with excellent logs and terrible business results. Once a Go service starts evolving prompts and response contracts, the usual failure modes show up fast: a new required field breaks older consumers, an enum expands and strict validation kills valid requests, or one bad sample drags the whole chain into retries and rollback panic. ...

If you plan to put OpenAI Realtime into production, do not let a passing demo fool you. What usually breaks the system is not the model itself. It is non-rotating short-lived auth, missing interruption state, and zero end-to-end latency budgeting. Miss those three and your voice UX starts sounding like an angry walkie-talkie. ...

When Go services call the OpenAI Responses API in production, the real failures are rarely about model quality. Most incidents come from transport instability: weak connection pooling, conflicting timeout layers, and retry storms. This guide gives you a practical baseline: HTTP/2 reuse, layered timeout budgets, bounded retries, and error-budget driven operations. ...

The most expensive outage is not a single failure — it is a failure amplified by retries. In an OpenAI Responses + Go tool-calling stack, missing idempotency, jittered backoff, and breaker thresholds can turn 10 failing requests into 1000 downstream calls in minutes. ...

Long-running agent sessions usually fail the same way: context keeps growing, latency spikes, costs blow up, and answer quality gets worse. That is rarely a model-quality issue. It is almost always missing context governance. ...

When OpenAI API calls start timing out in production, the real problem is usually not “OpenAI is down.” The real problem is you don’t know which hop is failing: DNS, TLS handshake, proxy path, or your own connection pool. ...

When CI keeps failing, the real risk is not “slow fixes” — it is “fast bad fixes.” This guide gives you a practical GitHub Actions + AI Agent auto-fix pipeline with failure tiering, strict edit boundaries, and merge-time gates. ...

Most teams can connect an LLM in a demo. The real pain starts in production: multi-step tasks, flaky tool calls, unclear retries, and rising cost. This guide gives you a pragmatic Go-first blueprint for shipping an Agent workflow that can survive real incidents. ...

Production streaming fails in two predictable ways: users wait while the stream silently drops, and your logs say “timeout” without telling you where it actually broke. This guide gives you a practical Go pattern for OpenAI Responses API streaming with strict timeout boundaries, safe retries, and useful telemetry. ...

If you still use one model for everything, you usually pay in one of three ways: higher cost, slower delivery, or more rework. A better setup is role-based collaboration: Claude Code for planning and quality gates, Codex for fast implementation and batch edits. ...