agent-native-architecture

What It Does

Codifies the five principles that distinguish "agent-native" architecture
from "agent-as-afterthought" — the patterns the
yellow-review:review:agent-native-reviewer agent checks against. This skill
is the canonical reference; the reviewer agent applies it.

When to Use

• Authoring or modifying agent tool definitions, system prompt construction,
  or LLM-integration scaffolding.
• Designing a feature where users and agents will both act on shared data.
• Evaluating whether a UI action has a corresponding agent capability.
• Deciding whether a tool should be a primitive or a workflow.

Usage

The Five Principles

1. Action Parity — Every UI action has an equivalent agent tool. If a
   user can rename a project via a button, the agent must have a
   rename_project tool. Exceptions are intentional human-only flows
   (CAPTCHA, 2FA, OAuth consent, biometric auth) and purely cosmetic UI
   (animations, theme toggling).

2. Context Parity — Agents see the same data users see. The system
   prompt must include available resources (files, entities, recent
   activity), capability mappings (which tool does what), and domain
   vocabulary. Static system prompts that don't reflect runtime state
   produce agents that don't know what exists.

3. Shared Workspace — Agents and users operate in the same data space.
   Agent file operations use the same paths as the UI; the UI observes
   changes the agent makes (via shared store, file watching, or reactive
   binding). Anti-pattern: agent writes to agent_output/ while user works
   in documents/ — separate sandboxes break collaboration.

4. Primitives over Workflows — Tools are composable primitives whose
   inputs are data, not decisions. A store_item(key, value) tool is
   correct; a process_feedback(message) tool that internally categorizes

   • prioritizes + notifies is a workflow tool that strips the agent of
     reasoning agency.

   Exception: Workflow tools are acceptable when they wrap
   safety-critical atomic sequences (a payment must charge + record +
   receipt as one unit) or external orchestration the agent shouldn't
   step-through (a deploy tool).

5. Dynamic Context Injection — System prompts include runtime app
   state, not just static instructions. The prompt at build-time names
   the agent's role; the prompt at request-time names the user's current
   project, recent files, available entities, and which capabilities apply
   to this specific call.

The Noun Test

For every domain noun in the app (project, document, task, message,
report — whatever the entities are), verify the agent can:

1. Know what it is — domain vocabulary in the system prompt
2. Interact with it — at least one tool whose target is this noun
3. Discover the capability — the tool is named in the prompt or
   surfaced in onboarding

A noun that fails all three is a critical gap for must-have entities.

Anti-Pattern Catalog

Anti-Pattern	Signal	Fix
Orphan Feature	UI action with no agent tool	Add tool; document in prompt
Context Starvation	Agent unaware of resources or terms	Inject resources + vocabulary into prompt
Sandbox Isolation	Agent reads/writes separate data space	Use shared workspace
Silent Action	Agent mutates state, UI doesn't update	Reactive binding or file watching
Capability Hiding	Users can't discover agent capabilities	Surface in agent responses
Workflow Tool	Tool encodes business logic	Extract primitives; orchestrate in prompt
Decision Input	Tool accepts decision enum	Accept data; let agent decide

Priority Tiers

Not every gap is equal — prioritize findings by impact:

• Must have parity: Core domain CRUD, primary user workflows, actions
  that modify user data.
• Should have parity: Secondary features, read-only views with
  filtering/sorting.
• Low priority: Settings/preferences UI, onboarding wizards, admin
  panels, purely cosmetic actions.

Critical findings only apply to must-have and should-have tiers; low-priority
gaps are observations.

Stack-Specific Tool Locations

Stack	Agent tools live at
Vercel AI SDK (Next.js)	tool() in route handlers; tools param in streamText/generateText
LangChain / LangGraph	@tool decorators; StructuredTool subclasses
OpenAI Assistants	tools array in assistant config
Claude Code plugins	agents/*.md, skills/*/SKILL.md, tool lists in frontmatter
Rails + MCP	tool() in MCP server definitions; .mcp.json
Generic	tool(, function_call, tools:, tool registration patterns

What This Skill Doesn't Cover

• CLI agent-readiness — see yellow-review:review:cli-readiness-reviewer
  and yellow-review:review:agent-cli-readiness-reviewer for CLI-specific
  agent optimization (non-interactive defaults, structured output, etc.).
• Agent authoring conventions — see
  yellow-core:create-agent-skills for how to write Claude Code agents and
  skills.
• MCP integration patterns — see yellow-core:mcp-integration-patterns
  for ruvector and morph integration patterns.