Arckit Agent Design

You are helping an enterprise architect design an AI agent architecture specification following a structured, traceable framework.

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.

1. Read existing artifacts from the project context

MANDATORY (warn if missing):

• AAGI (Agent Inventory, recommended) — Extract: agent catalogue, current capabilities, lifecycle status, and operational context
  • If missing: warn user to run /skill:arckit-agent-inventory first
• REQ (Requirements, recommended) — Extract: FR/NFR for agent capabilities, tool requirements, memory requirements, guardrail requirements
  • If missing: warn user to run /skill:arckit-requirements first

RECOMMENDED (read if available, note if missing):

• PRIN (Architecture Principles, in 000-global) — Extract: AI/agent principles, technology constraints, compliance requirements
• STKE (Stakeholder Analysis) — Extract: Agent user personas, operator roles, governance stakeholders
• RISK (Risk Register) — Extract: AI-specific risks, tool security risks, data handling risks
• AIPB (AI Playbook) — Extract: AI ethics principles, model governance, bias considerations

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

• HLDR (High-Level Design Review) — Extract: existing system architecture the agent will integrate with
• SECD (Secure by Design) — Extract: security controls applicable to agent systems
• AAGI (Agent Inventory) — Extract: existing agents, capability baseline, ownership, and operational status

2. 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

3. Interactive Configuration

Before generating the agent design, ask the user for key parameters. Skip any question where the user has already provided a clear answer in their arguments.

Gathering rules (apply to all questions in this section):

• Ask the most important question first; fill in secondary details from context or reasonable defaults.
• Maximum 2 rounds of questions. After that, pick the best option from available context.
• If still ambiguous after 2 rounds, choose the (Recommended) option and note: "I went with [X] — easy to adjust if you prefer [Y]."

Question 1 — header: Pattern, multiSelect: false

"What agent architecture pattern best describes this agent?"

• Single Agent (Recommended): Single LLM core with tools — best for focused tasks, simple domains
• Chain: Sequential pipeline of specialized agents — best for multi-step reasoning, complex workflows
• Multi-Agent: Parallel workers with a coordinator — best for complex domains, parallelization
• Hierarchical: Supervisor + worker agents — best for coordinated multi-agent systems, dynamic task allocation

Question 2 — header: Scope, multiSelect: false

"What is the primary scope of this agent?"

• Task Automation: Automating repetitive workflows (data processing, report generation, routine decisions)
• Knowledge Work: Research, analysis, synthesis (document analysis, market research, decision support)
• Creative: Generation and ideation (content creation, design, brainstorming)
• Decision Support: Recommendation and evaluation (risk assessment, prioritization, planning)

4. Read the template

Read the template (with user override support):

• First, check if .arckit/templates-custom/agent-design-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-design-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-design

5. Gather agent design information

Agent identity and purpose:

• Agent name: Short, descriptive name (e.g., "Research Analyst Agent")
• Agent ID: Unique identifier (e.g., "AGT-001")
• Purpose: One-sentence description of what the agent does
• Domain: Domain of expertise (e.g., "financial analysis", "code review")

Architecture decisions (from Questions or context):

• Architecture pattern: Single / Chain / Multi-Agent / Hierarchical (from Question 1)
• Scope: Task Automation / Knowledge Work / Creative / Decision Support (from Question 2)
• LLM model: Primary model (e.g., "Claude Sonnet 5 (session default)")
• Hosting: Local / Cloud / Hybrid

Tool inventory (minimum 3 tools):

Extract from REQ artifacts or define new:

• Tool ID: Unique identifier (e.g., "T-001")
• Tool name: Descriptive name
• Tool type: MCP / REST / Function / Database
• Permissions: Read / Write / Execute
• Input schema: Key parameters
• Output schema: Return format

Memory design:

• Session memory: In-memory context, session-scoped
• Durable memory: Persistent storage, cross-session
• Vector memory: Semantic search, embedding-based retrieval

Guardrail requirements (minimum 2 guardrails):

• Output validation: Regex / LLM-based / Heuristic checks
• Input sanitization: Prompt injection prevention
• Rate limiting: Usage controls
• Content filtering: Safety boundaries

Testing approach:

• Unit tests: Tool contract validation
• Integration tests: End-to-end agent flow
• Security tests: Prompt injection, jailbreak resistance

6. Auto-Populate from Existing Artifacts

CRITICAL: To create a high-quality, integrated agent design, extract data from existing ArcKit artifacts:

6.1 Extract Requirements → Tool Contracts

If projects/{project_id}/ARC-*-REQ-*.md exists:

Read the file and extract:

• FR (Functional Requirements) → Map to Tool Capabilities
  • Example: "FR-010: Query knowledge base" → Tool T-001 (Knowledge Base MCP)
• NFR (Non-Functional Requirements) → Map to Guardrails and Memory
  • Example: "NFR-S-001: Respond within 5 seconds" → Guardrail: timeout
  • Example: "NFR-P-002: Remember user preferences" → Memory: Durable layer

6.2 Extract Risks → Guardrail Configuration

If projects/{project_id}/ARC-*-RISK-*.md exists:

Read the risk register and extract:

• Security risks → Guardrails (prompt injection, data exfiltration)
• Reliability risks → Redundancy patterns, fallback mechanisms
• Compliance risks → Audit logging, data retention policies

6.3 Extract Stakeholders → Agent Persona

If projects/{project_id}/ARC-*-STKE-*.md exists:

Read stakeholder analysis and extract:

• End users → Agent interaction patterns, tone, complexity level
• Operators → Monitoring requirements, admin capabilities
• Governance → Approval workflows, audit requirements

7. Detect Version

Before generating the document ID, check if a previous version exists:

1. Look for existing ARC-{PROJECT_ID}-AAGR-v*.md files in the project directory
2. If no existing file: Use VERSION="1.0"
3. If existing file found:
   • Read the existing document to understand its scope
   • Compare against current inputs and agent requirements
   • Minor increment (e.g., 1.0 → 1.1): Same pattern — updated tools, refined guardrails, corrected details
   • Major increment (e.g., 1.0 → 2.0): Pattern changed (single → multi-agent), fundamentally different tools, new memory architecture
4. Use the determined version for document ID, filename, Document Control, and Revision History
5. For v1.1+/v2.0+: Add a Revision History entry describing what changed from the previous version

8. Construct Document Control Metadata

• Document ID: ARC-{PROJECT_ID}-AAGR-v{VERSION} (e.g., ARC-001-AAGR-v1.0)

Populate document control fields:

• document_id: Constructed from format above
• project_id: From Step 2
• project_name: From Step 2
• version: Determined version from Step 7
• author: "ArcKit Agent Design Command"
• date_created: Current date (YYYY-MM-DD)
• date_updated: Current date (YYYY-MM-DD)
• generation_date: Current date and time
• ai_model: Your model name

9. Generate Agent Architecture Specification

CRITICAL INSTRUCTIONS FOR QUALITY:

1. This is a LARGE document (architecture diagram + tool matrix + orchestration design, 600-1200+ lines). You MUST use the Write tool to create the file. DO NOT output the full document to the user (you will exceed token limits).

2. Follow the template structure with all 7 sections:

   Section 1: Agent Architecture Overview

   • Architecture pattern selection with justification
   • Architecture pattern comparison table (Single / Chain / Multi-Agent / Hierarchical)
   • Component diagram (Mermaid C4) showing: LLM Core, Tool Layer, Memory, Guardrails
   • Agent capabilities summary table

   Section 2: Tool Contracts (minimum 3 tools)

   • Tool contract matrix with: Tool ID, Name, Type, Permissions, Input Schema, Output Schema
   • MCP server configurations
   • Tool dependency graph (Mermaid)

   Section 3: Memory Architecture

   • Memory layer table: Session / Durable / Vector
   • Storage technology, retention policy, access patterns
   • Memory flow diagram (Mermaid)

   Section 4: Orchestration Design

   • Orchestration table: Component, Framework, Role, Handoff
   • Flow diagram (Mermaid flowchart) showing node transitions
   • Error handling and retry strategies
   • State management approach

   Section 5: Guardrail Configuration (minimum 2 guardrails)

   • Guardrail table: Name, Type, Threshold, Action
   • Prompt injection prevention strategy
   • Output validation pipeline
   • Rate limiting and quota management

   Section 6: Testing Strategy

   • Test matrix: Unit / Integration / Security / Performance
   • Coverage targets and tooling
   • Evaluation framework (benchmarks, golden datasets)

   Section 7: Traceability

   • AAGI → AAGR → REQ traceability links
   • Cross-references to related artifacts

3. Auto-populate from artifacts (from Step 6):

   • Tool contracts from ARC--REQ-.md
   • Guardrails from ARC--RISK-.md
   • Agent personas from ARC--STKE-.md

4. Mermaid diagram requirements:

   • C4Component diagram for architecture overview
   • Flowchart for orchestration design
   • All diagrams must use consistent styling
   • Include element descriptions in Person(), Component(), etc.

10. Quality Checks

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

AAGR-specific quality checks:

• Architecture pattern defined: One of Single, Chain, Multi-Agent, or Hierarchical — not left as placeholder
• Component diagram present: Valid Mermaid C4Component diagram with LLM Core, Tool Layer, Memory, Guardrails
• Tool contracts: At least 3 tools defined with complete schema (ID, name, type, permissions, input, output)
• Memory architecture: All 3 layers (Session, Durable, Vector) documented with storage and retention
• Orchestration diagram: Valid Mermaid flowchart with at least 3 nodes
• Guardrails: At least 2 guardrails defined with type, threshold, and action
• Testing strategy: At least 3 test types covered (Unit, Integration, Security)
• No placeholder text: No remaining [Name], [Model], [MCP servers], or [Schema] tokens

11. Use Write tool to create the agent design file

• CRITICAL: Because agent designs are large documents (600-1200+ lines), you MUST use the Write tool to create the file
• Do NOT output the full content in your response (this will exceed token limits)
• Use Write tool with the full agent design content
• Path: projects/{PROJECT_ID}-{project-name}/ARC-{PROJECT_ID}-AAGR-v{VERSION}.md

CRITICAL - Auto-Populate Document Control Fields:

Before completing the document, populate ALL document control fields in the header following the same pattern as other ArcKit commands.

Step 0: Detect Version

ADRs and agent designs follow the same version detection logic:

Creating a new document (default): Use VERSION="1.0"

Updating an existing document (user explicitly references an existing document):

1. Look for existing ARC-{PROJECT_ID}-AAGR-v*.md files in projects/{project-dir}/
2. If no existing file: Use VERSION="1.0"
3. If existing file found:
   • Read the existing document
   • Minor increment (e.g., 1.0 → 1.1): Same pattern — updated tools, refined guardrails
   • Major increment (e.g., 1.0 → 2.0): Pattern changed, fundamentally different architecture
4. Use the determined version for document ID, filename, Document Control, and Revision History

Step 1: Populate Revision History

| 1.0 | {DATE} | ArcKit AI | Initial creation from `/skill:arckit-agent-design` command | [PENDING] | [PENDING] |

Step 2: Populate Generation Metadata Footer

The footer should be populated with:

**Generated by**: ArcKit `/skill:arckit-agent-design` command
**Generated on**: {DATE} {TIME} GMT
**ArcKit Version**: {ARCKIT_VERSION}
**Project**: {PROJECT_NAME} (Project {PROJECT_ID})
**AI Model**: [Use actual model name]
**Generation Context**: [Brief note about source documents used]

12. Show summary to user (NOT full document)

## Agent Architecture Specification Created

**Agent**: {agent_name}
**Document**: projects/{PROJECT_ID}-{project-name}/ARC-{PROJECT_ID}-AAGR-v1.0.md
**Document ID**: ARC-{PROJECT_ID}-AAGR-v1.0

### Architecture Pattern

**Pattern**: {Single Agent / Chain / Multi-Agent / Hierarchical}
**Justification**: {One-sentence rationale}

### Components

- **LLM Core**: {model} — {primary model}
- **Tool Layer**: {N} tools — {tool names}
- **Memory**: Session + Durable + Vector
- **Guardrails**: {N} guardrails — {guardrail names}

### Tool Contracts

| Tool ID | Name | Type |
|---------|------|------|
| T-001 | {name} | {type} |
| T-002 | {name} | {type} |
| T-003 | {name} | {type} |

### Memory Architecture

| Layer | Storage | Retention |
|-------|---------|-----------|
| Session | In-memory | Session only |
| Durable | {DB} | Indefinite |
| Vector | {Vector DB} | Configurable |

### Orchestration

- **Framework**: {LangGraph/CrewAI/etc}
- **Nodes**: {N} nodes in pipeline
- **Error handling**: {Strategy}

### Guardrails

| Guardrail | Type | Action |
|-----------|------|--------|
| {name} | {type} | {action} |
| {name} | {type} | {action} |

### Testing Strategy

| Test Type | Coverage | Tool |
|-----------|----------|------|
| Unit | Tool contracts | pytest |
| Integration | Agent flow | {framework} |
| Security | Prompt injection | {tool} |

### Traceability

- **AAGI**: {N} references
- **REQ**: {N} requirements traced
- **RISK**: {N} risks addressed

### Next Steps

- [ ] Review and validate architecture with stakeholders
- [ ] Create detailed designs for each component
- [ ] Set up tool contracts and MCP servers
- [ ] Implement guardrails and safety layer
- [ ] Develop testing framework
- [ ] Run `/skill:arckit-agent-integration` for multi-agent coordination
- [ ] Run `/skill:arckit-agent-security` for security review

### Files Created

📄 `projects/{PROJECT_ID}-{project-name}/ARC-{PROJECT_ID}-AAGR-v1.0.md` ({line_count} lines)

Important Notes

• Token Limit: Agent designs are large documents. Always use Write tool to create the file, never output full content
• Minimum requirements: At least 3 tool contracts, 2 guardrails, all memory layers documented
• Mermaid diagrams: Must use valid syntax — C4Component for architecture, flowchart for orchestration
• 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
• Template customization: Users can override the template by placing their own agent-design-template.md in .arckit/templates-custom/
• Versioning: Always check for existing versions before creating a new file — increment appropriately

Suggested Next Steps

After completing this command, consider running:

• /skill:arckit-agent-integration -- Design integration contracts for multi-agent systems
• /skill:arckit-agent-security -- Design security architecture for agents