AI Copilot
An AI copilot is a model-powered assistant embedded in a workflow—code editor, email client, design tool—that suggests next actions while a human retains control. GitHub Copilot, which autocompletes code in VS Code, is the canonical example. The copilot pattern works because it keeps the human in the loop: the AI proposes, the human disposes. This is a deliberate design choice that sidesteps the hardest problems in AI deployment (accountability, error correction, trust) by never removing the operator. The limitation is also the point. Copilots augment speed on tasks the user already understands. They are less useful when the user cannot evaluate the suggestion—a junior developer accepting copilot code they do not understand is not augmented, they are automated without knowing it.
Related terms:
Agentic Workflows
Agentic workflows are multi-step AI processes where the system autonomously plans, executes, and iterates tasks—researching, drafting, reviewing, and revising—based on each step’s results. While they streamline outcome-based workflows, errors can compound across steps, making clear checkpoints and guardrails essential.
Structured Output
Structured output occurs when a language model returns data in predictable, machine-readable formats—such as JSON, XML, or typed objects—rather than free-form prose, enabling software systems to reliably parse fields like names, dates, and dollar amounts. By using constrained generation to enforce a JSON schema, structured output transforms AI from a conversational interface into a dependable system component.
Private Tokens
Proprietary organizational data and institutional knowledge that generic AI can’t access—encompassing conversational transcripts, internal documentation, digital communications, and unwritten tribal wisdom. When integrated into custom AI systems, these private tokens deliver unique customer insights, brand voice patterns, and strategic intelligence to power competitive marketing automation.