Arckit Agent Inventory

You are helping an enterprise architect create a comprehensive AI agent inventory that catalogs all existing AI agents across the programme — documenting capabilities, security posture, and human oversight levels.

User Input

$ARGUMENTS

Instructions

Note: Before generating, scan projects/ for existing project directories. For each project, list all ARC-*.md artifacts, check external/ for reference documents, and check 000-global/ for cross-project policies. If no external docs exist but they would improve output, ask the user.

Prerequisites

• ADMP (ADM Preliminary / Architecture Vision, recommended — inherits from TOGAF if available)
• APP (Application Inventory, recommended)
• PRIN (Principles, recommended — from 000-global)

1. Determine inventory scope:

   Ask the user: "What scope should this agent inventory cover? (e.g., all agents across the organisation, a specific project, or a specific environment like production/staging)"

   Use the user's input and $ARGUMENTS to determine scope.

2. Read existing artifacts from the project context:

   RECOMMENDED (read if available, note if missing):

   • PRIN (Architecture Principles, in 000-global)
     • Extract: AI governance standards, agent policy requirements, risk tolerance thresholds
   • ADMP (ADM Preliminary / Architecture Vision)
     • Extract: Agent programme scope, migration phases, existing agent references
   • APP (Application Inventory)
     • Extract: Application context, hosting platforms, integrations, lifecycle and ownership details

   OPTIONAL (read if available, skip silently if missing):

   • AAGR (Agent Architecture Specification)
     • Extract: Agent capability requirements, safety requirements, architecture patterns
   • AAOV (Agent Governance Framework)
     • Extract: Oversight model, approval workflow, audit requirements
   • AASE (Agent Security Architecture)
     • Extract: Threat models, security controls, isolation requirements
   • AAIN (Agent Integration Patterns)
     • Extract: Agent-to-agent communication patterns, orchestration models
   • AAMT (Agent Maturity Assessment)
     • Extract: Current maturity level, improvement targets

3. Identify the target project:

   • Use the ArcKit Project Context (above) to find the project matching the user's input (by name or number)
   • If no match, create a new project:
     1. Use Glob to list projects/*/ directories and find the highest NNN-* number (or start at 001 if none exist)
     2. Calculate the next number (zero-padded to 3 digits, e.g., 002)
     3. Slugify the project name (lowercase, replace non-alphanumeric with hyphens, trim)
     4. Use the Write tool to create projects/{NNN}-{slug}/README.md with the project name, ID, and date — the Write tool will create all parent directories automatically
     5. Also create projects/{NNN}-{slug}/external/README.md with a note to place external reference documents here
     6. Set PROJECT_ID = the 3-digit number, PROJECT_PATH = the new directory path

4. Read the template (with user override support):

   • First, check if .arckit/templates-custom/agent-inventory-template.md exists in the project root
   • If found: Read the user's customized template (user override takes precedence)
   • If not found: Read .arckit/templates/agent-inventory-template.md (default)
   • Then, read .arckit/templates/_partials/RENDERING.md and resolve the template's <!-- DOC-CONTROL-HEADER --> marker to the Document Control partial it selects, applying the ${organisation_name} and ${default_classification} substitutions. Remove the marker and its comment from the output — a rendered artefact must never contain either.

   Tip: Users can customize templates with /skill:arckit-customize agent-inventory

5. Catalog agents from all sources:

   Search for existing agent definitions across:

   • Agent design documents (ARC-*-AAGR-*.md)
   • Agent operational views (ARC-*-AAOV-*.md)
   • Agent security evaluations (ARC-*-AASE-*.md)
   • Code repositories and deployment configurations
   • Any external documents referencing AI agents
   • Known agent frameworks in use (LangGraph, CrewAI, AutoGen, etc.)

   For each agent found, document:

   • Agent ID: Unique identifier (AGT-001, AGT-002, etc.)
   • Name: Human-readable agent name
   • Purpose: What this agent does (1-2 sentences)
   • Model: Underlying LLM or model family (GPT-4, Claude, open-source, etc.)
   • Deployment: Current deployment status (Prod, Staging, Dev, Planned)
   • Owner: Business or technical owner
   • Risk Level: Critical, High, Medium, Low
   • Oversight Level: Human-in-the-loop, Human-on-the-loop, Autonomous with Audit

   If fewer than 3 agents are found, ask the user: "I've identified fewer than 3 agents. Do you have additional agents to include, or should I proceed with the agents found?"

6. Build the agent inventory:

   A. Agent Register:

   • Create a complete table of all agents with ID, name, purpose, model, deployment status, owner, risk level, and oversight level

   B. Capability Matrix:

   • For each agent, document: tools available, skills/capabilities, memory type (Session, Durable, Vector), and output types (Text, API, File, Action)

   C. Agent Dependencies (Mermaid flowchart):

   • Create a dependency map showing how agents interact
   • Include orchestration relationships, data flows, and tool-sharing patterns
   • Use Mermaid flowchart TD syntax

   D. Security Classification:

   • For each agent, document: data sensitivity level, access level, isolation method, and whether audit is required
   • Reference security evaluations from AASE documents if available

   E. Agent Lifecycle:

   • Document current status (Active, In Development, Deprecating, Retired), creation date, last updated date, and version
   • Include lifecycle stage rationale

   F. Human Oversight Level:

   • Define oversight model per agent:
     • Human-in-the-loop: Critical decisions require explicit human approval
     • Human-on-the-loop: Human monitors and can intervene but doesn't approve each action
     • Autonomous with Audit: Agent operates independently with comprehensive logging and periodic review
   • Link oversight level to risk classification

7. Read the quality checklist:

   Before writing the file, read .arckit/references/quality-checklist.md and verify all Common Checks plus the AAGI per-type checks pass. Fix any failures before proceeding.

8. Write the output:

   • Write to projects/{project-dir}/ARC-{PROJECT_ID}-AAGI-v1.0.md
   • Use the exact template structure from agent-inventory-template.md
   • Include Mermaid dependency diagram
   • Include all sections even if some are marked as "No agents found" or "TBD"

IMPORTANT - Auto-Populate Document Information Fields:

Before completing the document, populate document information fields:

Auto-populated fields

• [PROJECT_ID] → Extract from project path (e.g., "001")
• [VERSION] → Start with "1.0" for new documents
• [DATE] / [YYYY-MM-DD] → Current date in YYYY-MM-DD format
• [DOCUMENT_TYPE_NAME] → Document purpose
• ARC-[PROJECT_ID]-AAGI-v[VERSION] → Generated document ID
• [STATUS] → "DRAFT" for new documents
• Classification → comes from the resolved Document Control header, not from a placeholder. _partials/RENDERING.md fixes the ladder from the artefact's own regime; ${default_classification} applies only where that regime falls through to user config.

User-provided fields

• [PROJECT_NAME] → Full project name
• [OWNER_NAME_AND_ROLE] → Document owner

Revision History

| 1.0 | {DATE} | ArcKit AI | Initial creation from `/skill:arckit-agent-inventory` command |

Generation Metadata Footer

**Generated by**: ArcKit `/skill:arckit-agent-inventory` command
**Generated on**: {DATE}
**ArcKit Version**: {ARCKIT_VERSION}
**Project**: {PROJECT_NAME} (Project {PROJECT_ID})
**AI Model**: [Actual model name]

9. Summarize what you created:

• Total number of agents inventoried (AGT-001, AGT-002, etc.)
• Breakdown by deployment status (Prod, Staging, Dev, Planned)
• Breakdown by risk level (Critical, High, Medium, Low)
• Breakdown by oversight level (Human-in-the-loop, Human-on-the-loop, Autonomous)
• Number of unique tools across all agents
• Agent dependency count (number of agent-to-agent connections)
• Agents requiring audit (count)
• Suggested next steps (e.g., "Run /skill:arckit-agent-security to assess security posture" or "Run /skill:arckit-agent-design for new agent architecture")

Example Usage

User: /skill:arckit-agent-inventory Catalog all AI agents in the data processing pipeline

You should:

• Check prerequisites (ADMP, APP, PRIN recommended)
• Find project directory (e.g., projects/001-data-pipeline/)
• Search for existing agent definitions across documents and configurations
• Generate comprehensive agent inventory:
  • Agent register with AGT-001 through AGT-005 (data ingestion, validation, transformation, analysis, reporting)
  • Capability matrix showing tools (MCP clients, file systems, APIs, vector stores)
  • Dependency flowchart showing orchestration chain
  • Security classification (sensitivity levels, isolation methods)
  • Lifecycle status (active, in development, deprecated)
  • Oversight levels per agent (risk-based)
• CRITICAL - Token Efficiency: Use the Write tool to create projects/001-data-pipeline/ARC-001-AAGI-v1.0.md
  • DO NOT output the full document in your response (this exceeds 32K token limit!)
• Show summary only (see Output Instructions below)

Important Notes

• Agent inventory is the foundation for all subsequent agent architecture work (design, governance, security, maturity)

• Every agent MUST have an owner — unowned agents are a governance risk

• Risk classification drives oversight: Critical/High risk agents require Human-in-the-loop or Human-on-the-loop

• Low risk agents can operate autonomously with audit logging

• Security classification is mandatory — every agent needs sensitivity, access, isolation, and audit status defined

• Dependency map must show all agent-to-agent communication — orphan agents indicate design gaps

• Use Mermaid flowchart TD syntax for dependency diagrams (GitHub-renderable)

• Reference agent architecture reference from .arckit/references/agent-architecture-reference.md for design patterns, memory architectures, and security models

• Cross-reference existing AAGR, AAOV, AASE documents to avoid duplicating definitions

• Traceability: All agents must link back to ADMP programme scope and PRIN governance principles

• Markdown escaping: When writing less-than or greater-than comparisons, always include a space after < or > (e.g., < 3 seconds, > 99.9% uptime) to prevent markdown renderers from interpreting them as HTML tags or emoji

Integration with Other Commands

• Output: Feeds into /skill:arckit-agent-design (architecture for new or modified agents based on inventory gaps)
• Output: Feeds into /skill:arckit-agent-security (security assessment for each inventoried agent)
• Output: Feeds into /skill:arckit-agent-governance (oversight framework built on inventory)
• Output: Feeds into /skill:arckit-agent-integration (integration patterns between inventoried agents)
• Output: Feeds into /skill:arckit-agent-maturity (maturity assessment across all agents)

Quality Checks

Before writing the output file, verify:

• Agent Register: ≥3 agents documented with all required fields
• Capability Matrix: All agents have tools, skills, memory, and output types defined
• Dependency Diagram: Mermaid flowchart present with all agent-to-agent connections
• Security Classification: All agents have sensitivity, access level, isolation, and audit status
• Lifecycle Status: All agents have status, dates, and version
• Oversight Level: All agents have explicit oversight model defined

Output Instructions

CRITICAL - Token Efficiency:

1. Generate Agent Inventory

Create the comprehensive agent inventory following the template structure with all sections.

2. Write Directly to File

Use the Write tool to create projects/{project-dir}/ARC-{PROJECT_ID}-AAGI-v1.0.md with the complete agent inventory.

DO NOT output the full document in your response. This would exceed token limits.

3. Show Summary Only

After writing the file, show ONLY a concise summary:

## Agent Inventory Complete ✅

**Project**: [Project Name]
**File Created**: `projects/[PROJECT]/ARC-{PROJECT_ID}-AAGI-v1.0.md`

### Inventory Summary

**Agents**: [Number] agents catalogued
- Production: [Number]
- Staging: [Number]
- Development: [Number]
- Planned: [Number]

**Risk Levels**:
- Critical: [Number]
- High: [Number]
- Medium: [Number]
- Low: [Number]

**Oversight**:
- Human-in-the-loop: [Number]
- Human-on-the-loop: [Number]
- Autonomous with Audit: [Number]

**Dependencies**: [Number] agent-to-agent connections mapped

### What's in the Document

- Agent Register (complete table with ID, name, purpose, model, owner, risk, oversight)
- Capability Matrix (tools, skills, memory, outputs per agent)
- Dependency Map (Mermaid flowchart)
- Security Classification (sensitivity, access, isolation, audit)
- Agent Lifecycle (status, dates, versions)
- Oversight Levels (per-agent governance model)
- Traceability (ADMP, APP, PRIN links)

### Next Steps

- Run `/skill:arckit-agent-security` to assess security posture for inventoried agents
- Run `/skill:arckit-agent-design` to design architecture for new or modified agents
- Run `/skill:arckit-agent-governance` to establish oversight frameworks

Statistics to Include:

• Total agents in register
• Agents by deployment status
• Agents by risk level
• Agents by oversight level
• Total agent-to-agent dependencies
• Agents requiring audit

Generate the agent inventory now, write to file using Write tool, and show only the summary above.

Suggested Next Steps

After completing this command, consider running:

• /skill:arckit-agent-design -- Design architecture for new or modified agents
• /skill:arckit-agent-security -- Assess security posture for inventoried agents