In r/artificial and r/datascience, agent builders emphasize that reliability comes from harness design, explicit state and tools, and a replayable evaluation gate, not just upgrading the model.
agents fucking suck, not because of the model, because of their harness (tools, system prompts etc)
spent months thinking i needed better models when the bottleneck was always tool descriptions and prompt structure.
the ReAct paper is worth a read before your interview, not because you'll cite it directly but because it gives you a, concrete mental model to talk through agent loops (think, act, observe)
For agentic systems design interviews, Id focus on making the non-LLM parts explicit: state, tools, constraints, and eval.
Once the agent can rewrite its own heuristics, you need a replayable eval set plus a shadow-run gate for every change.
my setup collects failure patterns from real tasks and feeds them back into updated rules/prompts automatically.
Otherwise it learns confidence faster than judgment and quietly gets worse on the edge cases where domain expertise actually matters.
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