🚨 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:

# 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#

From governed web retrieval and managed coding sandboxes to physical-lab standards and agent security, here’s what moved across the agent stack this cycle: 1️⃣ AWS added domain and published-date filters to Amazon Bedrock AgentCore Web Search, giving agents per-request controls over source selection and recency. The service also expanded to AWS’s Ireland and Tokyo regions. 2️⃣ Google Cloud introduced Gemini Enterprise for Legal in preview, with specialized skills, legal-system connectors, partner agents and the Gemini Enterprise platform for legal workflows. 3️⃣ Snowflake made Cortex Agents Coding Agent generally available. Its managed sandbox exposes bash, file operations, web search and SQL execution, so customers do not have to host an agent loop themselves. 4️⃣ Oracle Health expanded its U.S. Clinical AI Agent with professional-fee coding, clinician-controlled dictation and chart review. Clinicians remain responsible for reviewing, editing and signing documentation. 5️⃣ Anthropic opened a research preview of the Model Hardware Standard, a shared specification for agents to safely operate programmable lab and manufacturing devices. It is model-agnostic and supports access through protocols including MCP. 6️⃣ Following incidents in third-party evaluation environments, Anthropic said it paused external cyber evaluations of pre-release models, which have since resumed under new practices. It also deployed a classifier that blocks, before a tool call runs, model attempts to aggressively probe or escape a testing environment or unexpectedly obtain internet access. 7️⃣ On September 1, Anthropic launched Claude Fable 5.1, its generally available version of the same underlying model as limited-access Claude Mythos 5.1. The model is designed for long-horizon agentic coding and research. Cache reads cost $0.25 per million tokens, 75% less than Fable 5; Anthropic estimates this cuts typical workload costs by 25% and highly agentic workloads by up to approximately 45%. 8️⃣ Google introduced agentic video understanding for Gemini 3.7 Flash, 3.6 Flash and 3.5 Flash-Lite via the Gemini API and Gemini Enterprise Agent Platform. Google reports up to 88% lower token use, up to 66% lower analysis cost and up to 7% higher accuracy in its benchmarks. 9️⃣ Agent-security startup AIR emerged from stealth with $50 million across two seed rounds. TechCrunch reports that it is building a platform to discover enterprise agents, continuously vet their skills, tools and add-ons, and block their interaction with software or external sources that fail its security criteria. 🔟 CrowdStrike introduced SafeMind, a family of security models and harnesses that it says will operate natively in the Falcon platform. It includes Red Tempest for advanced offensive red-team scenarios and Blue Solano for defensive protection, built using NVIDIA Nemotron open models.

NVIDIA pitching a max single-threaded CPU for agents is basically an admission that the agent loop is bottlenecked by sequential reasoning, not GPU throughput. Which is real, but most of the agent slowdowns we actually hit aren't compute at all, they're the loop stalling on a tool call while some external API or sandbox takes its time. A faster core doesn't touch that.

A useful but little-known OpenAI API feature 🗜 Ever had a long-running AI agent crash halfway because it ran out of context window? There's a fix for that. OpenAI's "Compaction" automatically compresses the conversation context of long-running agents, preventing context overflow. It's essential for agents that need to keep going through many steps. 📌 Title: Compaction 🔗 URL: 🧩 Overview When agents take many steps, the conversation history grows until it hits the context window limit. Traditionally, you'd have to manually truncate or summarize history yourself. Compaction automates this: it preserves the important information while compressing older exchanges to free up context space. 🛠 How to use it Enable compaction in your Responses API configuration. When the context approaches the threshold, the model automatically summarizes and compresses past conversation turns. No custom compression logic needed on your side. Just plug it into your agent loop. 🏗 Building it into production ・Coding agents: sessions that read and write dozens of files won't stall out from context overflow. ・Research agents: tasks that traverse many sources to gather information can run to completion instead of dying mid-way. ・Long customer support sessions: complex inquiries with many back-and-forth turns stay stable while retaining past context. ・Data processing pipelines: agents running multi-stage analysis steps within a single session. 💡 Use cases 🤖 Multi-step coding agents 🔬 Extended research and investigation tasks 💬 Complex customer support conversations 📊 Multi-stage data analysis pipelines ⚠️ Watch out Compression can lose fine-grained details and nuances from earlier in the conversation. Put critical instructions and rules in the system prompt where they won't be compressed. Compaction itself also consumes tokens, so it's unnecessary overhead for short tasks. ✨ The agent that's supposed to get smarter the longer it runs shouldn't lose its memory halfway through. Try Compaction on your longest-running agents first. #OpenAI# #LLM#