ia-agent-native-architecture

name	ia-agent-native-architecture
class	meta
description	Design agent-native applications where agents replace UI users as the primary actor. Use when designing MCP tools, agent-loop architectures, system prompt design, hooks policy, shared-workspace file patterns, or self-modifying agent systems.

Agent-Native Architecture

Core Principles

Five principles govern agent-native design. For detailed explanations, examples, and test criteria, see core-principles.md.

Principle	One-line test
Parity	Can the agent achieve every outcome the UI allows?
Granularity	Changing behavior means editing prose, not refactoring code
Composability	Can a feature be added by writing a new prompt, without new code?
Emergent Capability	Can the agent handle open-ended requests it wasn't designed for?
Improvement Over Time	Does the app work better after a month, even without code changes?

Focus Area Selection

1. Design architecture - Plan a new agent-native system from scratch
2. Files & workspace - Use files as the universal interface, shared workspace patterns
3. Tool design - Build primitive tools, dynamic capability discovery, CRUD completeness
4. Domain tools - Know when to add domain tools vs stay with primitives
5. Execution patterns - Completion signals, partial completion, context limits
6. System prompts - Define agent behavior in prompts, judgment criteria
7. Context injection - Inject runtime app state into agent prompts
8. Action parity - Ensure agents can do everything users can do
9. Self-modification - Enable agents to safely evolve themselves
10. Product design - Progressive disclosure, latent demand, approval patterns
11. Mobile patterns - iOS storage, background execution, checkpoint/resume
12. Testing - Test agent-native apps for capability and parity
13. Refactoring - Make existing code more agent-native
14. Anti-patterns - Common mistakes and how to avoid them
15. Success criteria - Verify your architecture is agent-native
16. Hooks patterns - Hook events, decision control, MCP matchers, async hooks

Wait for response before proceeding.

Reference Routing

Response	Action
1, "design", "architecture", "plan"	Read architecture-patterns.md, then apply Architecture Checklist below
2, "files", "workspace", "filesystem"	Read files-universal-interface.md and shared-workspace-architecture.md
3, "tool", "mcp", "primitive", "crud"	Read mcp-tool-design.md
4, "domain tool", "when to add"	Read from-primitives-to-domain-tools.md
5, "execution", "completion", "loop"	Read agent-execution-patterns.md
6, "prompt", "system prompt", "behavior"	Read system-prompt-design.md
7, "context", "inject", "runtime", "dynamic"	Read dynamic-context-injection.md
8, "parity", "ui action", "capability map"	Read action-parity-discipline.md
9, "self-modify", "evolve", "git"	Read self-modification.md
10, "product", "progressive", "approval", "latent demand"	Read product-implications.md
11, "mobile", "ios", "android", "background", "checkpoint"	Read mobile-patterns.md
11a, "icloud", "storage", "documents", "file state", "entitlement"	Read mobile-storage.md
11b, "background task", "battery", "on-device", "cloud routing"	Read mobile-execution.md
11c, "model tier", "token budget", "cost-aware", "batch", "caching"	Read mobile-cost.md
12, "test", "testing", "verify", "validate"	Read agent-native-testing.md
13, "review", "refactor", "existing"	Read refactoring-to-prompt-native.md
14, "anti-pattern", "mistake", "wrong"	Read anti-patterns.md
15, "success", "criteria", "verify", "checklist"	Read success-criteria.md
16, "hook", "hooks", "PreToolUse", "decision control", "async hook"	Read hooks-patterns.md
0, "quick start", "getting started", "overview", "introduction"	Read quick-start.md

After reading the reference, apply those patterns to the user's specific context.

Architecture Review Checklist

When designing an agent-native system, verify these before implementation:

Core Principles

• Parity: Every UI action has a corresponding agent capability
• Granularity: Tools are primitives; features are prompt-defined outcomes
• Composability: New features can be added via prompts alone
• Emergent Capability: Agent can handle open-ended requests in its domain

Tool Design

• Dynamic vs Static: For external APIs where agent should have full access, use Dynamic Capability Discovery
• CRUD Completeness: Every entity has create, read, update, AND delete
• Primitives over Workflows: Tools expose atomic capabilities; compose workflows in prompts
• API as Validator: Use z.string() inputs when the API validates, not z.enum()
• Eval Gate: 10 Q/A pairs in CI (read-only, multi-hop, closed-data), 9/10 pass threshold. See mcp-tool-design.md Evaluation section.

Files & Workspace

• Shared Workspace: Agent and user work in same data space
• context.md Pattern: Agent reads/updates context file for accumulated knowledge
• File Organization: Entity-scoped directories with consistent naming
• Context Durability: Incremental progress writes (WAL pattern) so interrupted tasks resume from last checkpoint

Agent Execution

• Completion Signals: Agent has explicit complete_task tool (not heuristic detection)
• Partial Completion: Multi-step tasks track progress for resume
• Context Limits: Designed for bounded context from the start
• Validate-Before-Run: Agent previews planned actions before executing destructive operations

Context Injection

• Available Resources: System prompt includes what exists (files, data, types)
• Available Capabilities: System prompt documents tools with user vocabulary
• Dynamic Context: Context refreshes for long sessions (or provide refresh_context tool)
• Trust levels for loaded content: System prompt distinguishes trusted (developer-authored) from untrusted (user input, retrieved docs, tool outputs); untrusted text is data, never instructions. See dynamic-context-injection.md Trust Levels section for the prompt-injection defense details.

UI Integration

• Agent -> UI: Agent changes reflect in UI (shared service, file watching, or event bus)
• No Silent Actions: Agent writes trigger UI updates immediately
• Capability Discovery: Users can learn what agent can do

Governance

• Approval Gates: Destructive or irreversible actions require user confirmation
• Audit Trail: Agent actions logged with timestamp, tool, and outcome
• Scope Boundaries: Agent cannot access resources outside its designated workspace

Hooks & Governance Automation

• Event Coverage: Only 6 hook events fire in agent context (PreToolUse, PostToolUse, PermissionRequest, PostToolUseFailure, Stop/SubagentStop); session lifecycle logic lives in the orchestrator
• Decision Gates: PreToolUse hooks enforce tool-level policy (allow/deny/ask/defer) instead of hardcoded checks
• Completion Gating: SubagentStop hooks block premature completion when verification steps remain
• MCP Matchers: Regex patterns target tools by server and operation for capability-based security
• Two-Tier Config: Shared policy committed, personal overrides git-ignored, per-hook disable toggles

Mobile (if applicable)

• Checkpoint/Resume: Handle iOS app suspension gracefully
• iCloud Storage: iCloud-first with local fallback for multi-device sync
• Cost Awareness: Model tier selection (Haiku/Sonnet/Opus)

When designing architecture, explicitly address each checkbox in the plan.