Originally published on AIdeazz — cross-posted here with canonical link.
My first production AI agent shipped with 131 tests in its evaluation harness. This wasn't a luxury; it was a non-negotiable step after a $0.03/run silent failure nearly cost me a client. I built the harness before writing new features, a decision driven by the fundamental limitations of traditional unit tests in multi-agent systems. Without it, I would have pushed a "working" agent that consistently failed to meet user intent under specific, common conditions, burning compute and trust.
The $0.03 Silent Failure
The agent's job was simple: process a user's natural language request for a specific report, query a database, and return the data. My initial unit tests covered the LLM's parsing of intent, the database query generation, and the final data formatting. All green. The agent worked perfectly in my dev environment with my curated test cases.
Then, a user asked for "last month's sales figures for product X." The agent returned "no data found." My logs showed the LLM correctly identified "product X" and "last month." The database query looked correct. But the user was seeing an empty result.
