The AI slop trap
Most enterprise AI agent projects are doomed from day one. Companies hire expensive consultants, write massive system prompts, throw a model into a basic script, and expect it to automate complex back-office operations. It works 60% of the time in demos, but fails catastrophically in production.
The three fatal mistakes
- No Decision Traces: When an agent fails, developers have no idea why. They don't know which prompt, tool call, or context chunk triggered the failure. Without decision traces, debugging is impossible.
- Poor Context Engineering: Dumping thousands of raw database rows into the context window and hoping the model figures it out. This leads to high latency, high token costs, and massive hallucinations.
- No Stateful Memory: Expecting an agent to operate without short-term conversation history and long-term vector-based retrieval.
To build enterprise-grade agents, you must treat harness engineering with the same discipline as traditional software engineering.