Most LLM agent demos die in production because teams skip the boring parts: tool contracts, eval harnesses, and observability. This post walks through the architecture, patterns, and evaluation loops that turn a clever prototype into a system you can actually operate.
Introduction A single language model, no matter how capable, can hallucinate, make reasoning errors, and exhibit hidden biases. The traditional solution in software engineering has always been peer review—multiple experts independently evaluate the same work, critique each other’s conclusions, and converge on a better answer. LLM Councils apply this same principle to AI systems: multiple language models independently reason about the same task, critique each other’s outputs, and converge on a higher-quality final answer through structured aggregation. ...