Topic
Taking AI systems from prototype to production - observability, verification, and real-world failure modes.
11 resources - 11 posts

Self-host LiteLLM or Kong, or use a managed gateway like Portkey, OpenRouter, or Cloudflare AI Gateway? A factual breakdown of cost, control, and ops tradeoffs.

LiteLLM vs Portkey vs OpenRouter compared for production AI apps: cost tracking, fallbacks, caching, and load balancing, plus when to use each router.

Agent architecture for multi-step Claude workflows: loop patterns, state management, error recovery, and the production gotchas that break agents at scale.

Sandbox agents, file-based memory and Codex-style filesystem tools in the OpenAI Agents SDK: what the docs actually ship, with code adapted from OpenAI's own examples.

Claude API error handling for production: retry shapes, backoff with jitter, circuit breakers, fallback chains, and the observability to debug failures at 3am.

GPT-5.4 developer guide with OpenAI SDK code, computer use, the 272K-token pricing cliff, and when 5.4 still beats GPT-5.3 and GPT-5.5 in production.

GPT-5.5-Codex merges Codex and GPT-5 stacks. Here is what the unified model means for real coding agents - latency, costs, prompt rewrites.

GPT-5.5 and 5.5 Pro for developers: pricing, which tier fits which workload, agentic gains from real evals, a regression to watch, and a migration playbook.

OpenAI Privacy Filter is an open-weight PII redactor. How to wire it into a local RAG ingestion pipeline and how it benchmarks against Presidio and regex.

RAG with Claude in production: structure-aware chunking, hybrid retrieval, citation tracking, and the prompt caching trick that makes RAG cheap enough to ship.

The agent reliability cliff: 85% per-step success compounds to about 20% over 10 steps. Why long chains collapse, and six patterns that fight the decay.
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