A manager who staffs FDE work, protects technical quality, unblocks customer situations, and turns field patterns into repeatable deployment improvements and product inputs.

Any technology that seems smart enough that it seems to be intelligent in a general way, meaning it can do different things once only doable by humans, much better than most humans who are not expert in that one field can do that one activity (such as: write essays or literature, summarize documents, produce reports and analyses, create music, create images, code software, scrutinize research, suggest strategies, transcribe audio to text, translate text among arbitrary languages, converse fluently over spoken words, or hold a detailed an in-depth spoken dialog on just about any topic in the world at far above an average human level of knowledge for any given topic).

Such a technology is also typically much faster than even humans highly skilled in that field can doing its activities.

The technology's output may match the output quality of humans in many of its tasks. The technology's output is not necessarily at a higher quality than that of all humans highly skilled in that field who spend a much larger amount of time on the same exact task. However, it is quite possible that the technology's output is at higher quality than that of many highly skilled, skilled, or semi-skilled humans who spend a much larger amount of time on the same task.

How closely staging, sandbox, and production match in data shape, permissions, network rules, and system behavior. Poor parity is the root cause of most 'worked in staging' failures.

An engineer who turns AI capabilities into working customer applications by combining models, tools, data, evals, product code, and rollout support. Builds with real data, not synthetic examples.

The designed path when automation cannot proceed safely — routing to a human, using a simpler workflow, or reverting to manual processing.

A disagreement about which system or dataset is authoritative for a field or decision. Must be resolved before write-back — otherwise two systems diverge and nobody owns the reconciliation.

The decision between solving one customer's problem quickly and building a reusable capability that scales to the next ten accounts. FDEs face this on every custom build.

A reusable architecture for a class of deployments: system boundaries, integrations, security patterns, and common variants. Built from a real deployment, not invented from scratch.

The smallest end-to-end version of a workflow that proves the path from user input to useful output or action. Ship this before building the full thing.

An AI system that uses context and tools to perform steps in a workflow under defined constraints, rather than just answering a question.