i made these 5 mistakes while building my multi-agent system, You probably will too

ive just finished working on customer support agents. Ngl, for the longest time i also thought id eventually blame the model, the prompts or maybe some framework issue. But after working on it for a long time, i'm pretty convinced the deciding factor wasn't any of that, it wiring everything together and retrieval, which honestly looks clean on architecture diagrams but in production it really gets messy. Arbitrary user queries hitting arbitrary data pull back the wrong context if you're relying on naive similarity search. And the scary part is the answer still sounds convincing. That's why I slowly stopped obsessing over prompts and spent way more time on retrieval. Hybrid retrieval, context ranking and evidence tagging became normal. Without those, the agent eventually hallucinates its way into a support nightmare. These are the grounding checks i really didn't bother to work on and my multiagent llm keep on failing. 1/ Coverage Rate. How often is the retrieved context actually relevant? 2/ Evidence Alignment. Can every answer be traced back to supporting text? 3/ Freshness. Is it pulling the latest docs instead of something archived six months ago? 4/ Noise Filtering. Can it ignore irrelevant chunks buried inside huge documents? 5/ Escalation Thresholds. Does it know when to stop pretending and hand the conversation over to a human? One of my investors even made an interesting rule. If the answer couldn't be grounded, the agent wasn't allowed to answer. That one decision cut escalations by 40% and pushed CSAT up by double digits. Thing is the more of these systems i work on, the less i think AI agents fail because of the model. Most of the time they fail because they were never grounded properly in the first place.