🚨 Agent security alerts often drown in noise. 🧭 AgentLoop Audit separates real risks from noise: ꔷ Full session & tool facts ꔷ Contextual risk analysis ꔷ One-click evidence location ꔷ Impact traced by secret, app & user 🔗  #AIAgent# #Security# #AgentSecurity# #AISecurity# #LLMOps#

🎮 Game agents can demo well but fail in production. 🔍 A 3-day, 7-step AgentLoop method: • Trace evidence, not claims • Evaluate tools & API facts • Fix root causes with Skill guardrails • Regress with datasets & alerts #AIAgent# #GameDev# #AgentLoop# #LLMOps# #CodingAgent#

Securing AI Agents on Alibaba Cloud: The Constraint Infra ️ Solve Agent chaos with a robust governance layer: ✅ Dynamic Control: Hot-update Prompts/rules via Nacos. ✅ Granular Governance: Token limits & multi-agent security. ✅ Proven in Prod: StarOps SRE Agent runs high-risk tasks safely within these boundaries. ✅ Self-Evolving: Rules iterate via AgentLoop data flywheel. Build safer, smarter Agents! 🚀 #AI# #AlibabaCloud# #Nacos# #Higress# #StarOps# #AgentLoop#

CI Harness v2 - an agent loop that thinks like a performance engineer 💫 Our first production run produced four merged pull requests into Better Auth, including one test job that went from 24 seconds to 4.5s. Built on @mastra @ClickHouseDB @hatchet_dev @render ✨

We wrote our own agent loop rather than point a general coding agent at our network. An independent analysis of a popular agent loop put ~1.6% of the code in AI decision logic and ~98.4% in permission gates, context management, tool routing, and recovery. A general coding agent keeps its safety in the system prompt as a polite request. Ours lives in code, and a test covers it. Kavan Smith, Senior Network Automation Engineer, on building agent-deploy: Open roles on the team:

# Useful but Little-Known Features of Claude Agent SDK 🌍 Define custom functions as agent tools running in-process using the @tool decorator and create_sdk_mcp_server. Give your agents access to databases, external APIs, and domain-specific logic without managing separate processes. 📌 Title: In-Process Custom Tools (create_sdk_mcp_server / @tool) 🔗 URL: 🧩 Overview Custom tools let you define your own functions that Claude can call during conversations. In Python, use the `@tool` decorator; in TypeScript, use the `tool()` helper. Define a name, description, input schema, and async handler, then wrap tools in `create_sdk_mcp_server` and pass them to `query()`. Tools run in-process — no separate process needed. Tool names follow the `mcp__{server_name}__{tool_name}` convention. Use `readOnlyHint` annotations to enable parallel execution, `isError` for recoverable failures, and return images (base64), resources, or structured content. 🛠 Usage Define tools with the `@tool` decorator and bundle them into an in-process MCP server. ```python from typing import Any import httpx from claude_agent_sdk import tool, create_sdk_mcp_server, query, ClaudeAgentOptions, ResultMessage @tool( "get_temperature", "Get the current temperature at a location", {"latitude": float, "longitude": float}, ) async def get_temperature(args: dict[str, Any]) -> dict[str, Any]: async with httpx.AsyncClient() as client: response = await client.get( "", params={ "latitude": args["latitude"], "longitude": args["longitude"], "current": "temperature_2m", }, ) data = response.json() return { "content": [ {"type": "text", "text": f"Temperature: {data['current']['temperature_2m']}"} ] } # Wrap in an in-process MCP server weather_server = create_sdk_mcp_server( name="weather", version="1.0.0", tools=[get_temperature], ) # Pass to query() async for message in query( prompt="What's the temperature in Tokyo?", options=ClaudeAgentOptions( mcp_servers={"weather": weather_server}, allowed_tools=["mcp__weather__get_temperature"], ), ): if isinstance(message, ResultMessage) and message.subtype == "success": print(message.result) ``` 🏗 Integration into Production Systems - Return `{"is_error": True}` instead of throwing exceptions to keep the agent loop alive and let Claude attempt recovery - Set `readOnlyHint=True` on side-effect-free tools to enable parallel execution with other read-only tools - Return images via `{"type": "image", "data": base64_str, "mimeType": "image/png"}` content blocks - Use `structuredContent` to return machine-readable JSON alongside content (note: not supported by Python `@tool` — requires standalone MCP server) 💡 Use Cases 🗄 Database access: let agents execute DB queries directly 🌐 External API integration: expose internal APIs or third-party services as tools 📊 Data visualization: generate charts and return them as base64 images 🔧 Domain logic: encapsulate calculations, conversions, and validations as callable tools ⚠️ Caveats - Python dict schema treats all keys as required; for optional parameters, omit from schema, describe in the description, and read with `args.get()` - `structuredContent` is not forwarded by Python's `@tool` decorator (use a standalone MCP server) - Tool search is on by default and defers SDK MCP tool schemas; with it disabled, every tool in the `tools` array consumes context on every turn - Annotations are metadata, not enforcement — a `readOnlyHint=True` tool can still write if the handler does so ✨ In-process custom tools let you freely extend agent capabilities with your own functions, without the overhead of managing external processes. #ClaudeAgentSDK# #AIAgent#

# Practical ways to use the Claude Agent SDK 🔧 Define your internal APIs, databases, and domain logic with `@tool` and let Claude call them. Custom Tools lets you define your own functions as in-process MCP servers using the `@tool` decorator, making them callable by Claude during conversations. 📌 Title: Providing Custom Tools to Claude 🔗 URL: 🧩 Overview Define tools with `@tool` (Python) / `tool()` (TypeScript) specifying name, description, schema, and handler. Wrap with `create_sdk_mcp_server` and pass to `mcp_servers`. Annotate side-effect-free tools with `readOnlyHint: true` for parallel execution. 🛠 How to use it Define tools with `@tool` (Python) / `tool()` (TypeScript) specifying name, description, schema, and handler. Wrap with `create_sdk_mcp_server` and pass to `mcp_servers={"weather": server}`. Tools are exposed to Claude as `mcp__weather__get_temperature`. 🏗 Practical usage - Define internal APIs (customer info, inventory, order status) as `@tool` and build agents that respond to natural language queries. - Add `readOnlyHint: true` to read-only tools for parallel execution and lower latency. - Catch exceptions in handlers and return `is_error=True` so the agent loop continues and Claude retries or tries alternatives. - Return chart images via `image` blocks (base64) for Claude to analyze visually. 💡 Use cases 🏢 Natural language access to internal APIs 📊 Chart generation + image analysis pipeline 🔄 Fault-tolerant autonomous retry ⚠️ Watch out Uncaught exceptions in handlers crash the entire `query()`. Always handle errors. Use `tools: ["Read","Grep"]` to restrict which built-in tools are available. #ClaudeAgentSDK# #AI#

Gemini 4 Carbon is a Monster. Using a multi-agent loop in Google Antigravity, Carbon rebuilt a playable c. 1780 Stein-Schule fortepiano from CAD drawings and X-ray radiographs, running 61-key escapement physics inside a 3D model of Toronto's Koerner Hall (RCM) with seat-specific spatial acoustics.