Technical articles on AI agent reliability, outcome-based routing, and production engineering.
Your AI agent passes every test, logs HTTP 200s, and then quietly returns garbage in production. Here's why that happens and how to fix it with outcome-aware routing.
Read article →A no-nonsense checklist for Python AI agents going to production. Error handling, retries, fallbacks, outcome tracking, cost monitoring, and how the pieces actually fit together.
Read article →Manual try/except fallback chains are fragile and static. Here's how Thompson Sampling routes between LLM paths based on real outcome signals, with CrewAI and LangChain examples.
Read article →Single-agent failures are isolated. Multi-agent failures compound. Here's how to instrument a 3-agent pipeline so you can actually debug it when things go wrong.
Read article →Token spend is the visible cost. Retries, failed calls, and over-provisioned models for simple tasks are where the real money goes. Here's how to measure and reduce it.
Read article →Eval suites are snapshots. Production is a stream. The failures that matter most are the ones your evals weren't written to look for.
Read article →try/except on RateLimitError only catches the crash. Here's how to handle rate limits before your agent dies, using outcome routing instead of static fallback logic.
Read article →GPT-4o for every call is expensive. GPT-4o-mini for every call degrades quality. Dynamic routing based on task complexity is the right answer, here's how to build it.
Read article →Every hardcoded routing decision encodes your intuition at one point in time. Thompson Sampling continuously updates model selection from outcomes. Here's how to implement it from scratch for LLM routing.
Read article →OpenClaw defaults to one model for everything. Here's how to wire Kalibr so your agent automatically routes heartbeat checks to cheap models and complex reasoning to capable ones, with real OpenClaw-specific code.
Read article →Stop paying gpt-4o prices for tasks gpt-4o-mini handles just as well. Three working approaches to automatic complexity routing: heuristics, classifier calls, and outcome-based Thompson Sampling.
Read article →Static routing rules go stale. Here's why outcome-based routing is a better way to stop using gpt-4o for every request, and how to set it up without writing if/else logic.
Read article →Not all LLM requests are equal, but most Python systems treat them as if they are. Here's how to route requests to cheaper models based on task complexity, from scratch and with Kalibr.
Read article →The most reliable way to cut LLM costs is to match model capability to task requirement. Here's a complete Python system to classify request complexity then route to the right model.
Read article →Automatic model downgrade for simple LLM requests is not a quality tradeoff if you do it on the right tasks. Here's how to detect simple requests, route them cheap, and verify quality.
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