last year i led an AI acceleration program at a company doing 62 million in revenue. we shipped two agents to production. fraud detection and publisher optimization. both working. both live.
the part that ate 80% of engineering time wasnt the model. wasnt the prompts. wasnt the data pipeline.
it was the workflow.
when the fraud agent flagged a suspicious publisher network, who got the alert? the analyst who should've caught it? the manager who reviews quarterly reports? me? without clear ownership the agent's findings just rot in a slack channel. we learned this month one. the agent surfaced a pattern across three markets. four analysts missed it for months. 30k in wasted ad spend. took three days to act because nobody knew who owned the output.
we ended up building what i call the boring layer. shared context that every agent reads from and writes to. approval flows with actual humans assigned. escalation rules. audit trails. spreadsheets, basically. not demo material.
the demo version of an AI agent is a chatbot doing magic. the production version is 20% model and 80% process engineering. routing decisions. ownership assignments. error handling when the agent's wrong. if you skip this layer, the agent is just expensive slack noise.
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