Short, direct definitions of the terms that come up when someone is selling you AI. Each one answers the question first, then gives a business example, then links to the work behind it.
AI orchestration is the layer that coordinates multiple AI steps, tool calls, and human approvals into one reliable workflow. It decides what runs next, what gets passed between steps, and what happens when something fails partway through.
Read the definitionAn MCP server is a program that exposes tools, data, and prompts to AI assistants using the Model Context Protocol, an open standard. It lets an assistant like Claude query your systems through one defined, permissioned interface instead of needing a custom integration built for each assistant.
Read the definitionAgent observability means being able to reconstruct exactly what an AI agent did on a given run: what it was given, which tools it called with which arguments, what came back, what it produced, and what that cost. Without it, a wrong output is not debuggable, only re-runnable.
Read the definitionA multi-agent system is one where several specialised AI agents each handle part of a workflow and hand off to one another, coordinated by a routing or orchestration layer. It is worth the complexity when stages genuinely need different tools, models, or approval rules, and it is a common and expensive mistake when they do not.
Read the definitionHuman-in-the-loop AI is a design where a person reviews or approves AI output at defined decision points rather than the system acting entirely on its own. The AI does the gathering, lookup, and drafting; a person makes the call on anything with real consequences.
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