Your AI Agent Is Not the Product: A Reference Agent Harness Architecture for Safety-Critical Search and Rescue
In one test, a real LLM gave up on a search while 88% of the probability map was still unsearched. The model was not broken. My harness (the code that wraps the model and keeps it safe) was unfinished
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BotSailor
Building the infrastructure for omnichannel automation. Insights on APIs, webhooks, and scaling AI-driven customer journeys.
The idea that the agent itself isn’t the entire product is an important distinction, especially in a safety-critical environment like search and rescue. The surrounding harness—controls, verification, observability, permissions, and human oversight—can be just as important as the model making the decisions.
I also like the focus on designing for failure rather than assuming the agent will always behave correctly. In situations where mistakes can have serious consequences, having clear boundaries around what the AI can and cannot do feels essential. This is a strong reminder that reliable AI systems are built through good engineering around the model, not just better models.