AI Engineering

Claude Code + GitHub Actions CI Self-Healing Pipeline: Error Attribution, Minimal Patches, and Human Approval Gates

If your CI keeps failing and engineers keep babysitting logs, you’re paying an invisible velocity tax. A production-grade AI self-healing pipeline is not “let the agent edit anything”. It’s a controlled loop: attribution, patching, approval, rollback. This post gives you a deployable baseline: Claude Code proposes a minimal fix patch, GitHub Actions enforces risk gates and regression checks, and humans only approve at high-impact checkpoints. ...

Claude API Rate-Limit Storm Playbook: Adaptive Concurrency, Jittered Backoff, and Quota Isolation

When Claude API starts returning 429 under high load, most systems don’t just slow down—they collapse: queue buildup, retry storms, upstream timeout chains, and pager noise. ...

OpenAI Responses Streaming in Production: Backpressure, Chunk Reassembly, and Timeout Budget

Most streaming failures are not about “can it stream”, but “does it stay stable under load”: broken chunks, stuck clients, timeout cascades, and retry storms. ...

Claude + OpenAI Model Routing Gateway: Latency Tiers, Cost Caps, and Quality Guardrails

Connecting both Claude and OpenAI in production is the easy part. The hard part is keeping the system stable across the quality-latency-cost triangle. Without a routing gateway, you usually get latency spikes, runaway bills, and ugly cascading failures. ...

OpenAI Responses + Go Stream Recovery: Delta Persistence, Resume Tokens, and Duplicate Chunk Dedup

In production, the painful part is not “streaming is slow.” It’s “streaming breaks and then duplicates output after reconnect.” This guide gives you a practical recovery loop: delta persistence + resume token + idempotent dedup, so reconnection does not replay garbage. ...

OpenAI Responses in Go Multi-Tenant Quota Governance: Token Buckets, Budget Circuit Breakers, and Cost Attribution

Most multi-tenant AI platforms fail for two boring reasons: one tenant saturates shared capacity, and finance discovers the burn too late. This guide gives you a practical Go blueprint: token-bucket throttling, budget circuit breakers, and request-level cost attribution. ...

Go + OpenAI Responses Agent Memory Layering: Short-Term Context, Long-Term Index, and Cost Caps

In production Go agents, the first thing that breaks is usually not model quality. It is memory management: context grows, bills spike, and answers drift. Use a 3-layer memory design: L1: short-term conversational window (seconds) L2: rolling summary (minutes) L3: long-term retrieval memory (days) ...

Handling OpenAI 429/5xx Storms in Go: Token Bucket, Exponential Backoff, and Circuit Breakers

Most Go teams are not killed by a single API error. They are killed by a retry storm they created themselves. ...

OpenAI Responses + GitHub Actions PR Risk Gate: Automated Evals, Tiered Blocking, and One-Click Rollback

You don’t need an AI reviewer that “sounds smart.” You need a gate that stops risky PRs before they hit main. This post shows a production-ready minimum setup: OpenAI Responses generates structured risk output, GitHub Actions enforces tiered policies, and critical failures can trigger a one-click rollback. ...

OpenAI Batch API with Go: Offline Batching, Failure Replay, and Cost Boundaries

Short answer: if your workload is delay-tolerant, batchable, and replay-safe, move it from online calls to Batch API. The savings are real, but only if you design splitting, failure routing, and replay first. Many teams treat Batch API as a cheaper sync endpoint. That usually creates a replay mess instead of stable savings. A conservative rollout starts with cost boundaries and SLOs, then implements offline batching and controlled replay. ...