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Agentic Feature Design

Designing features for the "Action Era" that are AI-accessible by default

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Agentic Feature Design

Features in LivestockAI must now be designed for dual consumption: Humans (UI) and Agents (MCP/API).

1. The "Headless First" Rule

Every feature must be fully functional via Server Functions before any UI is built.

Test: Can an agent complete the entire user story using only bun run check-feature-x.ts (a script calling server functions)? If yes -> Agent Ready. If no (logic lives in React components) -> Refactor immediately.

2. Agent-Ready Server Functions

Server functions are the tools we give to agents. Document them accordingly.

export const createBatchFn = createServerFn({ method: 'POST' })
  .inputValidator(batchSchema)
  .handler(async ({ data }) => {
    /* ... */
  })

/**
 * @description Creates a new livestock batch.
 * @workflow
 * 1. Check `getFarmFacilities` for space.
 * 2. `createBatchFn`
 * 3. Log initial `feed_record` if applicable.
 */

3. The "Intention" Pattern

Agents operate on Intent, not just data. Instead of generic CRUD (updateBatch), expose semantic actions (graduateBatch, quarantineBatch).

// ❌ Generic
updateBatch(id, { status: 'sold' })

// ✅ Semantic (Agent Friendly)
markBatchAsSold(id, { date, price, customer })

Semantic actions allow agents to:

  1. Understand the consequences of the action.
  2. Perform specialized validation (e.g. "Can't sell a quarantined batch").

4. Approval Loops (Human-in-the-Loop)

Agents need a standard way to ask for permission for high-stakes actions (e.g., ordering feed, selling stock).

The ApprovalRequest Entity:

  • agentId: Who is asking?
  • action: JSON payload of the server function to call.
  • summary: Human-readable explanation ("I want to order 50 bags of Starter Feed").
  • status: PENDING | APPROVED | REJECTED

5. Metadata for Context

All major entities (Batches, Sales) should have an ai_metadata JSONB column. Agents use this to store reasoning ("Predicted harvest date change due to low feed intake"). Do not show this raw JSON to users, but use it to power UI "Insights".

Related Skills

  • three-layer-architecture - Where the logic lives
  • feature-structure - How to organize the files

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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日本語の概要は準備中です。原文の説明を表示しています。

diegosouzapw/awesome-omni-skill622026年3月2日 更新

2agent

無料日本語概要

Configures 2-Agent workflow between PM and implementation roles. Use when user mentions 2-Agent, 2エージェント, PM連携設定, Cursor設定, Cursor連携, 2-agent運用. Do NOT load for: 単独運用, ワークフロー実行, ハンドオフ処理.

diegosouzapw/awesome-omni-skill622026年3月2日 更新

Appends relevant hashtags to journal entries based on content analysis. Intended for LLM execution.

日本語の概要は準備中です。原文の説明を表示しています。

diegosouzapw/awesome-omni-skill622026年3月2日 更新

add-agent

無料

Add a new AI coding agent to Agent Sandbox. Creates all required files (Dockerfile, templates, CI, docs) and wires the agent into the CLI, proxy, and build system.

日本語の概要は準備中です。原文の説明を表示しています。

diegosouzapw/awesome-omni-skill622026年3月2日 更新

Add a new property to the AI agents database. Use when the user wants to add, create, or introduce a new column, property, field, or feature to track across all agents in the comparison matrix. Handles all four required steps - database updates, groups.json, table display, and GitHub issue templates.

日本語の概要は準備中です。原文の説明を表示しています。

diegosouzapw/awesome-omni-skill622026年3月2日 更新

Add new Agent templates to the meta-agent-skills framework.

日本語の概要は準備中です。原文の説明を表示しています。

diegosouzapw/awesome-omni-skill622026年3月2日 更新

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