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🚀 Official Creator Guide

How to Build & Register a Production AI Agent

From concept to verified listing: Design your agent around the "One Job Done Well" rule, build a robust ReAct core loop in 50 lines of Python, package for 1-click execution, and publish to the open-source aiagent.org registry.

• Author: Ashish C Singh • Published: November 2026 • 8 min read 100% Open-Source & Free
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1

Design a Focused Agent (The "One Job Done Well" Rule)

The most successful agents on our registry are not generic conversation bots. They are purpose-built autonomous operators designed to solve a single high-friction task without hallucinating or running off the rails.

Archetype Real-World Goal Required Tooling Failure Mode to Avoid
Autonomous SWE Resolves GitHub issues, patches AST, and validates against test suites. Git CLI, File Editor, Pytest Rewriting whole files instead of pinpoint diffs.
DevOps & SRE Triages pod crash loops, checks logs, and drafts rollback runbooks. Kubectl API, Datadog, Bash Executing destructive writes without human-in-the-loop gates.
Data & Text-to-SQL Translates business questions into optimized, benchmarked SQL queries. Postgres Read-only, Schema AST Unbounded queries without LIMIT or explain analyze.
Browser Automation Navigates dynamic portals, multi-step checkout forms, and scraping. Playwright, Chromium DOM, Vision Fragile coordinate clicking; use accessible element IDs.
2

Build the Core Loop (50 Lines of Clean Python)

A production agent consists of three simple pieces:

agent.py
import os, sys, subprocess
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))

# 1. Deterministic Tool Layer (Outside the LLM)
def inspect_git_diff() -> str:
    """Returns uncommitted git changes in the current workspace."""
    out = subprocess.getoutput("git diff")
    return out if out.strip() else "Workspace is clean. No diff detected."

def run_tests() -> str:
    """Executes the test suite and returns the test summary."""
    return subprocess.getoutput("pytest -q")

tools = [inspect_git_diff, run_tests]

# 2. Strict Persona & Privilege Bounds
config = types.GenerateContentConfig(
    system_instruction=(
        "You are GitFixer, an autonomous engineering agent. "
        "Inspect the git diff, run local unit tests, and propose concise fixes. "
        "Never run destructive commands like 'git reset --hard'."
    ),
    tools=tools,
    temperature=0.2
)

# 3. Perception-Action ReAct Loop
def run_agent(goal: str):
    print(f"🤖 [GitFixer Started] Goal: {goal}")
    chat = client.chats.create(model="gemini-2.5-flash", config=config)
    response = chat.send_message(goal)
    print("\n" + response.text)

if __name__ == "__main__":
    if not os.environ.get("GEMINI_API_KEY"):
        print("❌ Please run: export GEMINI_API_KEY='your_key'")
        sys.exit(1)
    run_agent("Inspect the workspace diff, run tests, and report status.")
3

Package for 1-Click Execution (Pip, NPM, or Docker)

Developers will only use your agent if they can test it in 10 seconds. Provide a single working CLI command in your manifest.

⚡ Zero-Docker Python

Python Pip Package

Expose a console script via pyproject.toml or setup.py.

pip install my-agent && my-agent start
⚡ Zero-Docker Node / TS

NPX / NPM Binary

Expose a "bin" property in your package.json for instant zero-install runs.

npx my-agent-cli
🐳 Sandboxed Containers

Docker Container

Use for agents that execute arbitrary code, need custom OS binaries, or browser sandboxes.

docker run -it -p 3000:3000 org/agent
4

Author Your Agent Manifest (your-agent.json)

Create a JSON file named strictly after your agent ID (e.g. agents/git-fixer.json).

agents/git-fixer.json
{
  "id": "git-fixer",
  "name": "GitFixer AI",
  "slug": "git-fixer",
  "tagline": "Autonomous agent that inspects uncommitted diffs, runs test suites, and crafts clean commits.",
  "category": "coding",
  "categoryLabel": "Coding & SWE",
  "framework": "standalone",
  "frameworkLabel": "Python / Standalone",
  "author": "YourGitHubUsername",
  "github": "https://github.com/YourUsername/git-fixer",
  "stars": "100+",
  "license": "MIT",
  "verified": true,
  "featured": false,
  "recommendedModels": [
    "gemini-2.5-flash",
    "claude-3-5-sonnet"
  ],
  "toolsRequired": [
    "Git CLI",
    "Pytest Runner"
  ],
  "tags": [
    "git",
    "swe",
    "testing",
    "autonomous"
  ],
  "description": "GitFixer examines unstaged and staged changes, runs local test suites, and crafts clean conventional commits.",
  "quickstart": {
    "type": "Python Pip",
    "command": "pip install git-fixer && git-fixer review"
  }
}
Manifest Validation Rules:
  • id and slug must match the filename and only contain lowercase letters, numbers, and hyphens (^[a-z0-9-]+$).
  • github must start with https://github.com/.
  • category must be one of: coding, research, multiagent, productivity, devops, data.
  • No string may contain unescaped raw HTML, <script> tags, or JavaScript pseudo-protocols.
5

Submit to the Registry (Two Methods)

Choose the workflow that fits your team:

Method 1 (Fastest)

Instant GitHub Issue Web Form

Zero Git cloning needed. Fill out our structured issue form on GitHub. Our automation validates your submission and opens the manifest PR automatically.

Open Web Submission Form →
Method 2 (GitOps)

Direct Pull Request

Fork ashishcsingh/aiagents, copy template.json into agents/<id>.json, run the local linter, and open a PR.

node scripts/validate.js
Fork on GitHub →
6

The Distribution Moat (What Happens After Merge)

When your manifest is merged, your agent instantly receives global distribution across three surfaces:

🤖 Live App Store

Appears on aiagent.org/agents with interactive filtering and 1-click terminal execution copy.

📄 llms.txt Indexing

Indexed in aiagent.org/llms.txt so reasoning models (Claude, ChatGPT, Gemini) cite and recommend your agent.

🔍 Global Cmd+K

Searchable instantly across all 135+ documentation pages, benchmarks, and tutorials on aiagent.org.

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