AI-Design Patterns
AI-Design Patterns
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Customers gain faster, safer decisions from agentic AI that explains its reasoning, communicates risk clearly, and keeps people in control so teams can automate complex work with confidence, not guesswork. Here are some AI Principles and design patterns that I have created to guide cross-functional teams.
Design Principles
Human-in-the-Loop by Default
Agents propose and act, but humans retain authority at key decision and escalation points.
Explain Before You Automate
Every recommendation includes the why, evidence, and assumptions—never outcomes without context.
Progressive Disclosure of Intelligence
Surface simple answers first, with deeper reasoning, data, and logs available on demand.
Confidence, Not Certainty
Agents communicate uncertainty through confidence scores and risk indicators, not false precision.
Learn from Human Overrides
Human corrections are signals, feeding continuous improvement and better future recommendations.
AI-Patterns
AI-Patterns
1. Discovery Audit Card
1. Discovery Audit Card
A compact agent observability component that makes autonomous AI discovery visible, inspectable, and trustworthy in real time.
2. Confidence Score Badge
2. Confidence Score Badge
A lightweight agent observability component that makes AI uncertainty explicit by attaching a clear confidence signal to every AI-generated insight, recommendation, or decision.
3. Progressive Disclosure
3. Progressive Disclosure
A structured information layering pattern that reveals AI system detail in intentional levels allowing users to access transparency without overwhelming them.
- Discovery - Audit Card Pattern
What it is
What it is
A compact agent observability component that makes autonomous AI discovery visible, inspectable, and trustworthyin real time.
When to use
When to use
-Assessment / Discovery phases
-AI-driven scans, audits, migrations, topology mapping
-Any agent acting before user intervention
What the user sees (at a glance)
What the user sees (at a glance)
-Run state + progress (e.g., 98% Complete)
-Color-coded system health (Green / Amber / Blue)
-Evidence counts (VMs · Dependencies · Warnings)
-Drill-down affordance (Run details →)
Outcome
Users trust autonomous agents because they can see progress, inspect impact, and intervene only when needed.
Why it matters
Why it matters
AI systems fail when users can’t see what the agent is doing, why, and with what impact.
This pattern converts opaque AI execution into clear run state, evidence, and risk signals—without overwhelming the user.
Key UX principles
Key UX principles
-Progressive disclosure -summary first, depth on demand
-Observability over explanation -show signals, not stories
-Consistency -same card works across agents, jobs, pipelines
-Human-in-the-loop -surface risks early, don’t interrupt flow
2. Confidence Score Badge
Agent Observability · Trust & Transparency
2. Confidence Score Badge
Agent Observability · Trust & Transparency
What it is
What it is
A lightweight agent observability component that makes AI uncertainty explicit by attaching a clear confidence signal to every AI-generated insight, recommendation, or decision.
When to use
-Any surface where AI produces insights, classifications, or recommendations
-Discovery, planning, and execution phases
-Contextual or ambient AI experiences where interruption would be costly
What the user sees (at a glance)
What the user sees (at a glance)
-Confidence level (High / Medium / Low)
-Numeric score (e.g., 94%)
-Subtle color cue aligned with system health
-Hover or expand affordance for explanation
Agent mapping
Agent mapping
-Confidence badge → Trust signal on agent output
-Score & level → Model + data certainty
-Tooltip / modal → Evidence, coverage, assumptions
-Cross-surface reuse → Jobs, documents, agents, insights
Examples
Examples
Discovery Dashboard - Asset Summary
342 VMs , High · 94%
1,247 Dependencies, Medium · 78%
23 Hidden Dependencies, Medium · 72%
Explanation reveals inferred vs observed signals
Progressive disclosure
-Lightweight (default)
-Confidence label + percentage
-Optional ⓘ icon for clarity
Deep inspection (modal)
-Signals used
-Data coverage gaps
-Inference vs observed data
-Model limitations
-Historical reliability (if available)
Outcomes
Outcomes
Users develop appropriate trust in AI systems—neither blind reliance nor unnecessary skepticism by understanding how sure the system is and why.
Why it matters
Why it matters
Agentic systems fail when users can’t distinguish high-certainty automation from probabilistic inference.
Without visible confidence, users either over-trust the system or ignore it entirely. The Confidence Score Badge ensures AI never overclaims certainty supporting Honest AI by making uncertainty visible, contextual, and inspectable.
Key UX principles
Key UX principles
-Honest AI never hide uncertainty
-Progressive disclosure confidence first, rationale on demand
-Calibration over persuasion help users decide, not comply
-Consistency same confidence language across all AI surfaces
3. Progressive Disclosure
Agent Observability · Information Architecture · Trust & Transparency
3. Progressive Disclosure
Agent Observability · Information Architecture · Trust & Transparency
What it is
What it is
A structured information layering pattern that reveals AI system detail in intentional levels allowing users to access transparency without overwhelming them.
Why it matters
Why it matters
Agentic systems generate deep, technical, and often noisy data.
If everything is shown at once, users disengage. If too much is hidden, trust erodes.
Progressive Disclosure balances clarity and depth by matching information granularity to user intent, expertise, and moment-in-flow.
When to use
When to use
-Any AI output with multi-layered reasoning or evidence
-Documents, agent runs, audits, diagnostics, or recommendations
-Mixed-audience systems (execs, operators, auditors)
What the user sees (at a glance)
What the user sees (at a glance)
-Expandable sections with clear depth indicators
-Explicit disclosure levels (Level 1–3)
-Color-coded hierarchy for orientation
-Persistent open/closed state across sessions
Key UX principles
Key UX principles
-Layered transparency, show enough, then invite more
-Persona-aware depth, different users, different needs
-Cognitive safety, no sudden jumps in complexity
-User control, depth is pulled, never pushed
Agent mapping
Agent mapping
-Level 1 → Executive / summary view
-Level 2 → Operator / power-user diagnostics
-Level 3 → Auditor / system trace & logs
-Persistent state → Notebook + job run mental models
Progressive disclosure behavior
Levels open independently
Expanding deeper levels does not collapse higher ones
Last-open state is remembered per user
Keyboard navigable (Tab / Arrow / Enter)
Outcomes
Progressive disclosure behavior
Levels open independently
Expanding deeper levels does not collapse higher ones
Last-open state is remembered per user
Keyboard navigable (Tab / Arrow / Enter)
Outcomes
Users get the right amount of truth at the right time, enabling trust, comprehension, and accountability without cognitive overload.
Level 1: Summary
Default: Expanded, Target users: All users
Purpose: Fast understanding and decision orientation.
Level 2: Detailed Analysis
Default: Collapsed, Target users: VM admins, power users
Purpose: Action planning and validation.
Level 3: Technical Logs
Default: Collapsed
Target users: Advanced users, auditors
Purpose: Verification, compliance, and debugging.
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