2 posts tagged with "agent-engineering"

Most agent systems fail in production not because the underlying model is incapable. They fail because the engineering around the model is improvised. The model makes a wrong turn at step three and nobody notices until step eight, when the final answer is confidently wrong and there are no guardrails to catch it. This is not a model problem. It is an architecture problem.

Agent engineering has gone through at least two full hype cycles in three years. AutoGPT and BabyAGI generated enormous excitement in spring 2023, then crashed against the reality of GPT-4's unreliable tool use. A second wave arrived with multi-agent frameworks and agentic RAG in 2024. Now, in 2026, more than half of surveyed engineering teams report having agents running in production — and most of them have also discovered that deploying an agent and maintaining a reliable agent are different problems. The teams that are succeeding are treating agent engineering as a structured discipline. The teams that are struggling are still treating it as a vibe.

Most engineers building AI agents spend 80% of their time thinking about which model to use and 20% thinking about everything else. That ratio should be flipped. The model is almost interchangeable at this point — the harness is what determines whether your agent actually works in production.

The equation is simple: Agent = Model + Harness. If you're not the model, you're the harness. And the harness is where nearly all the real engineering lives.