Building Reliable AI Agents for Production

Production debugging needs uncertainty, baselines and multiple parallel theories because agents often mistake symptoms for causes, anchor on past incidents and lose evidence through sub-agent summaries.

Evals act as fuzzy unit tests for non-deterministic agents, combining scenarios, expected behavior, judges and aggregate scores, while the Instagram chatbot breach compromised 20,225 accounts.

PewDiePie's Ajax uses Alibaba's Quinn 3.5 9-billion-parameter model, Odysius fine-tuning, refusal removal through Heretic, supervised fine-tuning and GRPO after a 10-GPU home build.

Gemma 4 offers Apache 2-licensed 2B, 4B, 12B, 26B and 31B models that can run locally in browsers, on Android and iOS devices, on laptops or on a single commodity GPU.

Voice-agent debugging must align audio, transcripts, traces and session metrics, as a refund call hid 2.4 seconds of dead air, order 14 being misheard as order 40 and a flat robotic tone.

Reliable agent evaluation combines traces, deterministic code checks, calibrated LLM judges and human review, with a financial-analysis test finding six faithful and seven unfaithful reports out of 13.

Curating legal documents before retrieval controls duplicates, conflicts, freshness and sensitive data, and cleaning a corpus with 30% stale or duplicative content roughly doubled recall while improving task completion by 10–15%.