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.