Standalone agentss

--- name: autonomous-agent-patterns description: "Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex)." risk: critical source: community date_added: "2026-02-27" --- # 🕹️ Autonomous Agent Patterns > Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex). ## When to Use This Skill Use this skill when: - Building autonomous AI agents - Designing tool/function calling APIs - Implementing permission and approval systems - Creating browser automation for agents - Designing human-in-the-loop workflows --- ## 1. Core Agent Architecture ### 1.1 Agent Loop `` ┌─────────────────────────────────────────────────────────────┐ │ AGENT LOOP │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Think │───▶│ Decide │───▶│ Act │ │ │ │ (Reason) │ │ (Plan) │ │ (Execute)│ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ ▲ │ │ │ │ ┌──────────┐ │ │ │ └─────────│ Observe │◀─────────┘ │ │ │ (Result) │ │ │ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ ` `python class AgentLoop: def __init__(self, llm, tools, max_iterations=50): self.llm = llm self.tools = {t.name: t for t in tools} self.max_iterations = max_iterations self.history = [] def run(self, task: str) -> str: self.history.append({"role": "user", "content": task}) for i in range(self.max_iterations): # Think: Get LLM response with tool options response = self.llm.chat( messages=self.history, tools=self._format_tools(), tool_choice="auto" ) # Decide: Check if agent wants to use a tool if response.tool_calls: for tool_call in response.tool_calls: # Act: Execute the tool result = self._execute_tool(tool_call) # Observe: Add result to history self.history.append({ "role": "tool", "tool_call_id": tool_call.id, "content": str(result) }) else: # No more tool calls = task complete return response.content return "Max iterations reached" def _execute_tool(self, tool_call) -> Any: tool = self.tools[tool_call.name] args = json.loads(tool_call.arguments) return tool.execute(**args) ` ### 1.2 Multi-Model Architecture `python class MultiModelAgent: """ Use different models for different purposes: - Fast model for planning - Powerful model for complex reasoning - Specialized model for code generation """ def __init__(self): self.models = { "fast": "gpt-3.5-turbo", # Quick decisions "smart": "gpt-4-turbo", # Complex reasoning "code": "claude-3-sonnet", # Code generation } def select_model(self, task_type: str) -> str: if task_type == "planning": return self.models["fast"] elif task_type == "analysis": return self.models["smart"] elif task_type == "code": return self.models["code"] return self.models["smart"] ` --- ## 2. Tool Design Patterns ### 2.1 Tool Schema ``python class Tool: """Base class for agent tools""" @property def schema(self) -> dict: """JSON Schema for the tool""" return { "name": self.name, "description": self.description, "parameters": { "type": "object", "properties": self.getparameters(), "required": self.getrequired() } } def execute(self, **kwargs) -> ToolResult: """Execute the tool and return result""" raise NotImplementedError class ReadFileTool(Tool): name = "readfile" description = "Read the contents of a file from the filesystem" def getparameters(self): return { "path": { "type": "string", "description": "Absolute path to the file" }, "startline": { "type": "integer", "description": "Line to start reading from (1-indexed)" }, "end_line": { "type": "integer", "de