AI Components

Surfaces when and how AI has acted. Required at every point of autonomous action.

Explains why the AI made a decision, what data it used, and what its confidence was.

Surfaces statistical risk and data limitations at the point of output.

Shows model confidence inline. Prevents users from treating probabilistic output as fact.

Operator-defined confidence gate. Flags output below threshold before it reaches users.

Discloses when AI is ranking, pre-selecting, or framing choices on the user's behalf.

Intercepts consequential agent actions. Explicit approval required before execution.

Surfaces the footprint of agent runs — records affected, reversibility, downstream systems.

Communicates what a running agent is doing, how far it is, and whether it needs attention.

Requests access to new data sources or systems. User grants or denies per-session.

Escalates when the agent detects anomaly, scope breach, or decision fork requiring human input.

Surfaces agent error or pause states with concrete recovery paths and override controls.

Thumbs, regeneration, inline editing. Required after every substantive AI output.

Pre-run visualization of the agent network — who's in it, what each is authorized to do, how they relate.

Mid-run consent gate triggered when an orchestrator spawns a subagent. Tied to a specific delegation event.

Shows a multi-agent run in partial failure — which agents completed, stopped, or are still running.

Presents contradictory agent outputs and requires a human decision before the run can continue or close.

Immutable, timestamped log of agent actions. Required for any regulated or high-stakes workflow.

Shows energy, water, and carbon cost of AI inference at point of use.

Network-level audit with per-agent drilldown. Composes individual agent trails into a unified footprint view.

Token-by-token output rendering with cursor and completion indicators.

Communicates what the model is doing while reasoning, retrieving, or generating.

Primary text input for AI interaction. File attachment, context indicators, send controls.

Typed error patterns for timeout, refusal, hallucination-flagged, and rate-limit failures.

Context window usage, memory state, session history — making invisible state visible.

Zero-state onboarding: communicates what the surface does and offers calibrated starter prompts.

Generated outputs treated as first-class persistent objects — charts, documents, code, images.

Surfaces example actions and questions to help users understand what to ask.

Post-response recommendations that guide users toward logical next steps.

AI-assisted rewriting of user prompts to improve clarity before submission.

Structured visual controls for composing multi-step instructions without prompt engineering.

Chronological display of agent actions making multi-step execution legible.

Discrete tool call log — search, read, write, API calls — at execution level.

Expandable summaries that prevent overwhelming walls of agent reasoning text.

Separates agent reasoning from final outputs so users can skip to the result.

Timestamped record of what one agent passed to another — context, instructions, and scope granted.

Lists files, selections, and data the AI is using to produce responses.

Compact inline tokens showing active context inputs at a glance.

Displays the AI's working memory and persistent references in this session.

Controls for choosing which documents and files the AI should consider.