inframagics-design
Inframagics Design Guide
Product: Inframagics — Agent-native workspace for the next unicorns
Demo: https://inframagics.com
Trigger: When designing, reviewing, or improving Inframagics features, interfaces, or architecture.
Key Reference: Product/inframagics-roles-capabilities.md — Roles, capabilities, MVP scope, and full design philosophy
Design Philosophy: Claude Code for Non-Engineers
Core Analogy
Inframagics mirrors the Claude Code experience for non-technical users:
| Claude Code | Inframagics |
|---|---|
| Terminal/CLI interface | Chat interface (Genspark-style) |
| User mode: Write code, commit | User mode: Submit requests, execute tasks |
| Admin mode: Create rules/MCP/skills | Admin mode: Create policies, SOPs, workflows |
| System evolves as you use it | System learns and improves over time |
Key difference: Inframagics is for people extremely uncomfortable with terminal/CLI.
Unified Interface, Role-Based AI
Same UI for all roles — Only the AI behavior changes:
| Role | AI Focus |
|---|---|
| Admin | Policies, SOPs, settings, system configuration |
| User | Clarify request → Reason → Retrieve policies → Execute |
| Manager | Team oversight, approvals, delegation |
Self-Improving System
User friction → Admin creates policy → System improves → Future users benefit
This virtuous cycle is why Inframagics replaces the ops team, not just the tools.
Role Framework (Summary)
| Role | Scale | Priority | Description |
|---|---|---|---|
| Admin | 10+ | P0/MVP | System owner, policy creator, approver |
| User | 10+ | P0/MVP | Request submitter, information seeker |
| Manager | 30+ | P1 | Team approver, delegator |
| Policy Owner | 100+ | P2 | Domain specialist |
| External | 300+ | P3 | Vendors, auditors |
MVP Focus: Admin + User, Finance (Expenses) only
1. The Paradigm Shift
Traditional Enterprise Model
Policy Makers → SOPs → Operational Staff → ERP/CRM → End Users
↓ ↓ ↓ ↓
Executives Documents Humans Software
define interpret execute records
Problems:
- SI fees: 6-7 figures for setup
- Policy changes: Weeks + SI involvement
- Ops team: 10-50 people interpreting rules
- Knowledge: Scattered across systems/heads
Inframagics Model
Policy Makers → Policies (in system) → AI Agent → End Users
↓ ↓ ↓
Executives Natural Executes
define in language, policies,
natural instant handles
language changes requests
Value:
- Setup: Self-service, 4-5 figures max
- Policy changes: Instant, no SI needed
- Ops team: Eliminated or minimal
- Knowledge: Organized, retrievable, contextual
2. Target Audience: Baby-Corns
Definition: Next unicorns — fast-growing startups that will need enterprise systems but don't want enterprise baggage.
Baby-Corn Characteristics
| Trait | Implication for Design |
|---|---|
| Growing fast | System must scale without reimplementation |
| Lean teams | No dedicated ops staff; everyone wears hats |
| Tech-savvy founders | Expect modern UX, no tolerance for legacy |
| Cash-conscious | Won't pay 6-figure SI fees |
| Agile culture | Need instant policy changes, not IT tickets |
| AI-native thinking | Expect AI to do the work, not assist |
NOT Our Target
- Legacy enterprises: Have sunk costs in SAP/Oracle, change-averse
- SMBs with no growth: Don't need policies, just basic tools
- Industries with rigid compliance: Healthcare, banking (initially)
Design Implication
Every feature should pass the "baby-corn test":
"Would a 50-person startup with no ops team be able to use this without training or consultants?"
3. Three Core Capabilities
Capability 1: Policies & SOPs
What it replaces: Policy manuals, training documents, approval workflows configured by SIs
How it works:
Policy Maker says: "Purchases over $5000 need VP approval"
↓
System interprets + confirms understanding
↓
Policy active immediately, enforced by agent
↓
End user makes request → Agent applies policy
Key Design Principles:
-
Natural language in, structured execution out
- Input: "New vendors need finance approval"
- System: Parses, confirms, stores structured rule
- Never ask policy maker to fill forms
-
Confirmation before activation
- Show: "I understood this as: [structured interpretation]"
- Allow: Edit, adjust, test before going live
- Never: Silently activate possibly wrong interpretation
-
Instant changes, zero downtime
- Policy maker edits policy → Live immediately
- No IT ticket, no deployment, no SI call
-
Conflict detection
- When new policy conflicts with existing, surface it
- "This overlaps with [other policy]. How should I prioritize?"
-
Execution visibility
- Every policy application logged
- Policy maker can see: "This policy triggered 47 times this week"
Interface Pattern:
┌─────────────────────────────────────────────────────────┐
│ Define a policy... 🎤 │
│ ───────────────────────────────────────────────────── │
│ "Expenses over $500 require manager approval" │
└─────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ I'll create this policy: │
│ │
│ Type: APPROVAL │
│ Trigger: expense.amount > 500 │
│ Action: Require approval from reporting_manager │
│ │
│ [Edit] [Test with example] [Activate] │
└─────────────────────────────────────────────────────────┘
Capability 2: Request Handling
What it replaces: Ops team that receives requests, interprets policies, executes tasks
How it works:
End user says: "I need to expense this $800 dinner with client"
↓
Agent checks policies: "Expenses > $500 need approval"
↓
Agent: "I'll submit this for manager approval.
Adding it to Sarah's queue. You'll be notified."
↓
Sarah approves → Agent completes expense submission
Key Design Principles:
-
Intent-based, not form-based
- User states what they want
- Agent figures out how to do it
- Never: "Please fill out form XYZ"
-
Policy application is transparent
- "This needs approval because [policy X]"
- User understands why, not just blocked
-
Agent handles mechanics, human handles judgment
- Agent: Data entry, routing, notifications
- Human: Approval decisions, exceptions
-
Proactive, not reactive
- Agent: "Your expense report has 3 items waiting. Should I submit?"
- Not: User must remember to check status
-
Graceful escalation
- If agent can't handle, escalate to human clearly
- "I'm not sure how to handle [X]. Let me connect you with [person]."
Interface Pattern:
┌─────────────────────────────────────────────────────────┐
│ What do you need? 🎤 │
│ ───────────────────────────────────────────────────── │
│ "Bill Acme Corp $15,000 for the consulting project" │
└─────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ 📋 Creating invoice for Acme Corp │
│ │
│ Amount: $15,000 │
│ Description: Consulting project │
│ Due: Net 30 (per your policy for this client) │
│ │
│ ⚠️ This exceeds $10,000 - requires Finance approval │
│ I'll route to CFO after you confirm. │
│ │
│ [Confirm & Route] [Edit details] [Cancel] │
└─────────────────────────────────────────────────────────┘
Capability 3: Knowledge Organization & Retrieval
What it replaces: Confluence, SharePoint, Notion sprawl; tribal knowledge in people's heads
How it works:
User asks: "What's our policy on remote work?"
↓
Agent searches knowledge base
↓
Agent: "Remote work policy (updated March 2024):
- 3 days/week in office minimum
- Core hours 10am-3pm
- Equipment stipend $500/year
Want me to show the full policy or help with something specific?"
Key Design Principles:
-
Conversational retrieval, not search results
- Don't show: 47 matching documents
- Show: The answer, with source citation
-
Knowledge capture from policies
- Policies automatically become queryable knowledge
- "What's the approval limit for travel?" → Finds from policy
-
Context-aware answers
- Agent knows who's asking
- Different answer for manager vs. new hire
-
Living documentation
- When policy changes, knowledge updates automatically
- No stale documentation problem
-
Capture tribal knowledge
- "How do we usually handle X?" can be saved as knowledge
- Turn one-off answers into reusable knowledge
Interface Pattern:
┌─────────────────────────────────────────────────────────┐
│ Ask anything... 🎤 │
│ ───────────────────────────────────────────────────── │
│ "How do I get reimbursed for conference travel?" │
└─────────────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ For conference travel reimbursement: │
│ │
│ 1. Submit expense within 30 days of travel │
│ 2. Include receipts for anything over $25 │
│ 3. Per diem is $75/day for meals (no receipts needed) │
│ 4. Pre-approval required for international travel │
│ │
│ 📎 Source: Travel Policy (updated Nov 2024) │
│ │
│ Should I start an expense report for you? │
│ [Yes, start expense] [Show full policy] │
└─────────────────────────────────────────────────────────┘
4. Value Proposition Checklist
Every feature must deliver on at least one:
✅ Eliminates SI Dependency
- Can policy maker configure without consultant?
- Is setup self-service?
- Can changes be made without IT ticket?
✅ Shrinks Operations Team
- Does this replace a human task?
- Is the agent doing work, not just assisting?
- Would a 50-person company NOT need an ops hire for this?
✅ Instant Policy Changes
- Can policy be changed in natural language?
- Is the change live immediately?
- Is there no deployment/release cycle?
✅ Knowledge Always Available
- Is information retrievable conversationally?
- Is there a single source of truth?
- Does it eliminate "ask Bob, he knows"?
Red flags (features that don't fit):
- "Requires initial setup workshop" ❌
- "Configure in admin settings" ❌
- "Integrate with your existing..." ❌ (v1 should be complete)
- "Training required for advanced features" ❌
5. Design Principles
Principle 1: AI Does, Human Confirms
Wrong: AI assists human doing work
Right: AI does work, human confirms/approves
Wrong: "Here's a draft invoice for you to review and edit"
Right: "I created the invoice. It's ready to send. [Send] [Edit first]"
Principle 2: Natural Language Everything
Wrong: Forms with fields
Right: Conversation that extracts what's needed
Wrong: "Select department: [dropdown]"
Right: "Who's this for?" → Agent figures out department
Principle 3: Policy Before Rejection
Wrong: User submits → Error: "Exceeds limit"
Right: User types intent → "This needs approval because [policy]" → User decides to proceed
User should never be surprised by a policy.
Principle 4: Zero Training for Basic Tasks
Wrong: "See documentation for how to submit expenses"
Right: "I need to expense lunch" → Agent handles everything
If a new employee can't accomplish basic tasks on day 1 without training, we've failed.
Principle 5: Instant Feedback Loop
Wrong: Policy maker defines → Wait days → See if it works
Right: Policy maker defines → Test immediately → See execution in real-time
Principle 6: Context is King
Wrong: Same interface for everyone
Right: Agent knows who you are, what you usually do, what's relevant
"Create invoice" for an AP clerk vs. CFO should feel different.
6. Interface Patterns
Full specification: See
Product/inframagics-roles-capabilities.md
Unified Chat Interface (All Roles)
Same UI structure for everyone — inspired by Genspark:
┌──────┬────────────────────────────────────────────────┐
│ + │ │
│ New │ Inframagics │
├──────┤ │
│ 🏠 │ ┌────────────────────────────────────────┐ │
│ Home │ │ Ask anything, request anything │ │
├──────┤ │ [📎] [🎤] [→] │ │
│ 💰 │ └────────────────────────────────────────┘ │
│ Fin │ │
├──────┤ Quick Actions / Recent items │
│ 👥 │ │
│ HR │ │
├──────┤ │
│ 📦 │ │
│ Proc │ │
├──────┤ │
│ 💻 │ │
│ IT │ │
├──────┤ │
│ ⚙️ │ │
│ Set │ │
└──────┴────────────────────────────────────────────────┘
Left sidebar = Business functions directory (collapsible icons) Main area = Universal chat input + context-aware actions
Role-Based AI Behavior
Admin AI focuses on system configuration:
Admin: "Set expense limit to $500 for meals"
AI: ✓ Creating policy: Meal expenses capped at $500
✓ Applies to: All employees
→ Policy created. Notify employees?
User AI focuses on request execution:
User: "I had dinner with a client at Nobu, $450"
AI: ✓ Checking policy: Entertainment Expense Policy
✓ Amount $450 exceeds $200 → Manager approval required
→ Upload receipt? [📎 Attach]
Manager AI focuses on team oversight:
Manager: "What's pending?"
AI: ✓ 3 requests awaiting approval:
1. Sarah - $450 client dinner (over policy)
2. Mike - $89 supplies (auto-approvable)
→ Approve Mike's? [✓ Approve]
Key UX Requirements
- Single universal input (text + voice + attachments)
- Sidebar scopes context (clicking Finance focuses AI on Finance)
- Transparent reasoning (show policy lookups, routing decisions)
- Action buttons in responses (not forms)
- No separate admin/user URLs — role determines AI behavior
Anti-patterns to Avoid
- Form-based anything
- Module-based navigation (AP, AR, HR tabs)
- Separate admin/user interfaces
- Configuration tables and matrices
- Visual workflow builders
- Search results instead of answers
7. Anti-Patterns (Inframagics-Specific)
"Requires Consultant to Configure"
Symptom: Feature needs expert setup before use. Fix: Natural language configuration with smart defaults.
"Ops Team Still Needed"
Symptom: Agent assists but human still does the work. Fix: Agent does the work, human only approves/confirms.
"Knowledge Scattered"
Symptom: Information in docs, chat, email, people's heads. Fix: All knowledge in Inframagics, conversationally retrievable.
"IT Ticket for Changes"
Symptom: Policy changes need admin/IT involvement. Fix: Policy maker speaks change, it's live immediately.
"Training Required"
Symptom: Users need onboarding to use basic features. Fix: Intent-based interface that anyone can use immediately.
"Enterprise UX Creep"
Symptom: Adding features that make sense for SAP but not baby-corns. Fix: Apply baby-corn test: Would 50-person startup use this?
"Configuration Over Convention"
Symptom: Lots of settings, options, customization. Fix: Smart defaults that work for 90% of cases.
8. Competitive Positioning
vs. Traditional ERP (SAP, Oracle, NetSuite)
| Dimension | Traditional ERP | Inframagics |
|---|---|---|
| Setup | 6-12 months, 6-7 figures | Self-service, days |
| Policy changes | IT project, weeks | Natural language, instant |
| User training | Weeks of formal training | Zero training needed |
| Ops team | Required (10-50 people) | Eliminated |
| Interface | Transaction codes, forms | Conversational |
Inframagics advantage: For baby-corns, we're the only option that doesn't require becoming an "enterprise" to use enterprise software.
vs. Modern SaaS (Monday, Notion, Asana)
| Dimension | Modern SaaS | Inframagics |
|---|---|---|
| Policy enforcement | Manual/honor system | Automatic, built-in |
| Approval workflows | Basic or bolt-on | Native, natural language |
| Knowledge retrieval | Search results | Conversational answers |
| Work execution | Human does it | Agent does it |
Inframagics advantage: SaaS tools organize work but don't do work. Inframagics agent actually executes.
vs. AI Assistants (Copilots, ChatGPT)
| Dimension | AI Assistants | Inframagics |
|---|---|---|
| Policy awareness | None (generic) | Built-in, enforced |
| System of record | External | Native |
| Action capability | Suggests | Executes |
| Knowledge scope | General | Company-specific |
Inframagics advantage: Generic copilots don't know your policies or have permission to act. Inframagics is the system AND the agent.
9. Review Checklist
When reviewing Inframagics designs:
Value Delivery
- Does this eliminate SI dependency?
- Does this shrink the ops team need?
- Can policies be changed instantly?
- Is knowledge retrievable conversationally?
Baby-Corn Fit
- Would a 50-person startup use this?
- Is it usable without training?
- Is setup self-service?
- Does it avoid "enterprise bloat"?
AI-Native Design
- Is AI doing the work (not just assisting)?
- Is input natural language (not forms)?
- Are policies shown before rejection?
- Is context used to personalize?
Interface Quality
- Is there a single universal input?
- Are action options clear?
- Is status always visible?
- Can users accomplish goals in <30 seconds?
10. Feature Prioritization Framework
When deciding what to build:
Must Have (P0)
- Directly eliminates ops team need
- Enables instant policy changes
- Core to "agent does work" promise
Should Have (P1)
- Enhances core capabilities
- Improves baby-corn experience
- Requested by multiple prospects
Nice to Have (P2)
- Edge cases
- Power user features
- Future enterprise needs
Won't Build (v1)
- Requires SI to implement
- Only relevant for large enterprises
- Adds complexity without clear value
- "Because SAP has it"
11. Key Metrics
Agent Effectiveness
- Tasks completed by agent: Target 80%+
- Tasks requiring human intervention: Target <20%
- Policy violations caught pre-submission: Target 95%+
User Adoption
- Time to first task completion: Target <5 min
- Tasks per user per week: Growing week-over-week
- Users needing support: Target <10%
Value Delivery
- Ops team size: Should be smaller than comparable companies
- Policy change time: Target <1 hour
- Setup time: Target <1 day for basic use
12. The Inframagics Promise
To baby-corn founders:
"You'll never need a 50-person ops team. You'll never pay 7-figure SI fees. You'll never wait weeks for policy changes. Your AI agent handles operations. You focus on growth."
Every design decision should reinforce this promise.