How this AI system operates — the functional modes (e.g. analytical, semantic, generative, agentic, perceptive, physical) it composes.
Acts — plans a goal into steps and carries them out, calling tools, browsing, scheduling, or composing multi-step workflows on the user's or operator's behalf. The output is an action taken in software, not a piece of content returned. Examples: booking a meeting, filing a return, escalating a ticket, running a campaign loop.
Decides — predicts, classifies, scores, or ranks from structured data. The system reads numeric or categorical inputs and returns a label, a score, or a ranking. It does not generate new content. Examples: a credit-risk score, a fraud flag, a queue-length forecast, a recommendation rank.
Creates — produces new text, images, audio, video, or code that did not exist before this system ran. The output is content authored by the system, not a label or score about existing data. Examples: drafting a paragraph in response to a prompt, generating an image from a description, composing audio, completing a snippet of code.
Senses — turns raw camera, microphone, or scanned-document signals into structured detections, transcriptions, or extracted fields. The system reads pixels, audio, or document images and returns things the rest of the pipeline can use. Examples: detecting a vehicle in a camera frame, transcribing a spoken sentence, reading the fields off a paper form.
Moves — acts on the physical environment. The system drives motors, valves, signals, or other actuators and changes something in the world. Examples: steering a robot, changing a traffic-signal phase, opening a gate, adjusting an HVAC valve, releasing a parking-garage barrier.
Understands and remembers — pulls meaning from text or speech, finds related ideas, and grounds the system in saved context. The system reads language and returns matches, intents, summaries, or links to relevant prior knowledge. It does not generate new content. Examples: matching a customer's question to the right help article, retrieving the policy clause that applies to a case, recognizing the topic of a recorded sentence.
Live API response from GET /schemas/ai@2026-05-06-beta/categories (this category) and GET /schemas/ai@2026-05-06-beta/elements?category_id=functional_modes.
{
"id": "functional_modes",
"name": [
{
"locale": "en",
"value": "Functional Modes"
}
],
"description": [
{
"locale": "en",
"value": "How this AI system operates — the functional modes (e.g. analytical, semantic, generative, agentic, perceptive, physical) it composes."
}
],
"prompt": [
{
"locale": "en",
"value": "What is the AI system doing?"
}
],
"authoring_guidance": [
{
"locale": "en",
"value": "Pick one or more functional modes."
}
],
"examples": [],
"sources": [
{
"type": "framework",
"title": "AI Taxonomy — An Operational Framework for Precision in AI Discourse",
"url": "https://dropleaf.app/d/AlXez8scbd",
"citation": "Narain Jashanmal, v1.1, January 2026."
}
],
"required": true,
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"locale": "en",
"value": "Description"
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"element_context": {
"id": "autonomy",
"name": [
{
"locale": "en",
"value": "Autonomy"
}
],
"description": [
{
"locale": "en",
"value": "How much this mode decides and acts on its own. The element name says what the mode does; this label says who chooses and who carries out the result."
}
],
"values": [
{
"id": "human_decides",
"name": [
{
"locale": "en",
"value": "Human decides"
}
],
"description": [
{
"locale": "en",
"value": "This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first."
}
],
"color": "#2A9D8F"
},
{
"id": "human_executes",
"name": [
{
"locale": "en",
"value": "Human executes"
}
],
"description": [
{
"locale": "en",
"value": "This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them."
}
],
"color": "#6A1B7A"
},
{
"id": "autonomous",
"name": [
{
"locale": "en",
"value": "Autonomous"
}
],
"description": [
{
"locale": "en",
"value": "This mode decides and acts on its own. No person reviews each decision or carries out the resulting action."
}
],
"color": "#E76F51"
}
]
}
}[
{
"id": "agentic_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Acting (Agentic AI)"
}
],
"description": [
{
"locale": "en",
"value": "Acts — plans a goal into steps and carries them out, calling tools, browsing, scheduling, or composing multi-step workflows on the user's or operator's behalf. The output is an action taken in software, not a piece of content returned. Examples: booking a meeting, filing a return, escalating a ticket, running a campaign loop."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "mode_agentic",
"variables": [
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},
{
"id": "analytical_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Deciding (Analytical AI)"
}
],
"description": [
{
"locale": "en",
"value": "Decides — predicts, classifies, scores, or ranks from structured data. The system reads numeric or categorical inputs and returns a label, a score, or a ranking. It does not generate new content. Examples: a credit-risk score, a fraud flag, a queue-length forecast, a recommendation rank."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "mode_analytical",
"variables": [
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},
{
"id": "generative_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Creating (Generative AI)"
}
],
"description": [
{
"locale": "en",
"value": "Creates — produces new text, images, audio, video, or code that did not exist before this system ran. The output is content authored by the system, not a label or score about existing data. Examples: drafting a paragraph in response to a prompt, generating an image from a description, composing audio, completing a snippet of code."
}
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"authoring_guidance": [],
"examples": [],
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"symbol_id": "mode_generative",
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{
"id": "perceptive_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Sensing (Perceptive AI)"
}
],
"description": [
{
"locale": "en",
"value": "Senses — turns raw camera, microphone, or scanned-document signals into structured detections, transcriptions, or extracted fields. The system reads pixels, audio, or document images and returns things the rest of the pipeline can use. Examples: detecting a vehicle in a camera frame, transcribing a spoken sentence, reading the fields off a paper form."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "mode_perceptive",
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},
{
"id": "physical_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Moving (Physical AI)"
}
],
"description": [
{
"locale": "en",
"value": "Moves — acts on the physical environment. The system drives motors, valves, signals, or other actuators and changes something in the world. Examples: steering a robot, changing a traffic-signal phase, opening a gate, adjusting an HVAC valve, releasing a parking-garage barrier."
}
],
"authoring_guidance": [],
"examples": [],
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"symbol_id": "mode_physical",
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},
{
"id": "semantic_mode",
"category_id": "functional_modes",
"title": [
{
"locale": "en",
"value": "Understanding (Semantic AI)"
}
],
"description": [
{
"locale": "en",
"value": "Understands and remembers — pulls meaning from text or speech, finds related ideas, and grounds the system in saved context. The system reads language and returns matches, intents, summaries, or links to relevant prior knowledge. It does not generate new content. Examples: matching a customer's question to the right help article, retrieving the policy clause that applies to a case, recognizing the topic of a recorded sentence."
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"symbol_id": "mode_semantic",
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