🤖 Your AI agent finds it. Adds it to cart. And pays for it. On Shopify, AEON Agentic Checkout brings the full shopping journey into an agent workflow, powered by AEON AI Card for controlled spending. The next step for AI isn't just knowing what to buy. It's being able to buy it. #AIpayment# $AEON

If your agent executes code, you want to run it in an isolated environment. I learned this the hard way. I also know a couple of people who let OpenClaw go nuts and had to wipe out their laptops after a few days to clean up the mess. Two rules you must always follow: 1. Agent that runs code → isolated environment 2. Agent that controls a browser → isolated environment But that generates a new problem: that environment has to start quickly, use as little memory as possible, and give you enough control to operate it. This is hard to achieve, but you don't need to reinvent the wheel: Cube Sandbox is an open-source MicroVM sandbox developed by Tencent Cloud. Think of it as a production-grade agent infrastructure that you can use to run agents at scale. Cube Sandbox uses RustVMM and KVM to run each sandbox with hardware-level isolation. • It cold-starts in under 60ms • It uses less than 5MB of RAM overhead • You can launch tens of thousands of sandboxes within a minute Here is what you can do with it: 1. Capture, clone, and restore sandbox state with snapshots and rollback 2. Deploy sandbox clusters through Kubernetes 3. Run workloads on ARM infrastructure 4. Observe what happens inside agent executions 5. Govern ingress and egress traffic 6. Persistent volumes and cross-machine pause/resume Here is the GitHub repository: You can inspect the code and try it out with your own agent workflow. #ad#

HELLO GUYS I AM STARTING AGENT TUNING BUSINESS I AM LOOKING FOR MY FIRST BATCH OF FREE CLIENTS TO USE IN CASE STUDIES I will audit your entire agent workflow and provide custom tools + skills built for your setup so your agent LEVELS UP REPLY 👇 IF INTERESTED PLEASE 🫡

# Useful but Little-Known Features of OpenAI Agent SDK 🌍 When your agent crashes mid-task, do you have to start over from scratch? Durable execution integrations preserve agent progress across failures, letting you resume right where you left off. 📌 Title: Durable Execution Integrations 🔗 URL: 🧩 Overview The OpenAI Agent SDK supports integration with multiple durable execution orchestrators. **Temporal** enables durable, long-running workflows. **Dapr** is a vendor-neutral CNCF orchestrator with automatic failure recovery. **Restate** provides a lightweight durable agent framework supporting processes, containers, and serverless. **DBOS** preserves agent progress using SQLite or Postgres. All four integrate with the standard `Runner` interface and support human-in-the-loop patterns (pause, approve, resume). 🛠 How to use it ```python # Temporal integration # pip install temporalio from temporalio.contrib.openai_agents import openai_workflow # Dapr integration # Set up Dapr CLI and runtime, then: # dapr run -- python agent_workflow.py # Restate integration # pip install restate-sdk # Deploy agents following Restate docs # DBOS integration # pip install dbos from dbos import DBOS # See official documentation for each orchestrator: # Temporal: # Dapr: # Restate: # DBOS: ``` 🏗 Building it into production ・Ensure failure resilience for long-running agents (research, data processing) ・Use human-in-the-loop patterns to pause agents awaiting approval and resume after sign-off ・If you already have orchestration infrastructure (Temporal/Dapr), run agents on top of it ・In serverless environments, use Restate or DBOS for lightweight agent durability 💡 Use cases 🔄 Long-running research agents that auto-resume after failures ✅ Workflow automation with human approval steps 🏗 Agent orchestration in microservices architectures 💾 Checkpointing agent progress for recovery ⚠️ Watch out Each orchestrator has its own infrastructure requirements (Temporal server, Dapr runtime, Restate service, DBOS database), so factor in operational cost and complexity when choosing. For vendor neutrality, consider Dapr (CNCF) or Restate. If you have existing workflow infrastructure, Temporal is a natural fit. For minimal setup, DBOS works well. Integration maturity varies, so thoroughly validate before production deployment. ✨ With durable execution, build agents that survive crashes and never lose progress. #OpenAIAgentSDK# #AIAgent#

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 What happens when your agent workflow takes hours or days — and the process crashes midway? Durable execution integrations let you build resilient long-running and human-in-the-loop workflows. 📌 Title: Running agents – Durable execution integrations 🔗 URL: 🧩 Overview Durable execution integrations persist agent execution state so that workflows can resume from the last checkpoint after process crashes or server restarts. This is essential for human approval workflows (waiting hours to days) and long-running batch processes. The SDK supports integration with Temporal, Dapr, Restate, and DBOS. 🛠 Usage This example combines DBOS with the Agent SDK. Initialize with `DBOS()`, then define an agent via `Agent(name="approval-agent", instructions="...")`. Inside a `@DBOS.workflow()`-decorated async function `expense_approval_workflow(report_id)`, call `await input=...)` to analyze the expense report, then `await DBOS.recv(f"approval-{report_id}", timeout_seconds=86400 * 7)` to wait up to 7 days for approval. If `approval["approved"]` is `True`, call ` again to process the approved report. 🏗 Practical Patterns **Human Approval Workflows** For processes requiring human sign-off — expense reports, content publishing, contract reviews — durable execution preserves state while waiting for approval. Even if the server restarts during the wait, processing resumes automatically when approval arrives. **Auto-Recovery from Failures** Temporal, Dapr, Restate, and DBOS all automatically resume from the last checkpoint after crashes. If a failure occurs mid-LLM call, already-completed steps are not re-executed, preventing duplicate work and wasted tokens. **DBOS for Small-Medium Projects** Temporal and Dapr require dedicated orchestration infrastructure. DBOS works with just SQLite (local dev) or Postgres (production) — no separate orchestration server needed. This makes it the pragmatic choice for teams that want durability without operational overhead. **Staged Agent Pipelines** Multi-step pipelines (research, analyze, generate report, review, approve) can checkpoint after each step. If step 4 fails, you resume from step 4 — not from scratch. 💡 Use Cases 📋 Expense approval flows waiting days for manager sign-off 📝 Content publishing pipelines with editor review and approval gates 🔄 Long-running batch processing with crash recovery (hundreds of documents) 🏢 Contract review workflows waiting for legal team confirmation ⚠️ Caveats - Framework choice depends on your infrastructure. Use Temporal if you already run it; choose DBOS for a lightweight start on new projects. - Persisting full LLM responses increases storage costs. Design your checkpoints to save only the information needed for resumption. - Always set timeouts on human approval steps. Indefinitely waiting workflows cause resource leaks. - DBOS with SQLite is great for local development but use Postgres in production for reliability and concurrency. ✨ With durable execution, build long-running workflows without fearing process failures! #OpenAIAgentSDK# #AIAgent#

Today's video is about an interesting multi-agent workspace that I've gotten a lot of comments about, Orca! I install it, explore some of the core features, try out the worktrees with multiple agents, and then use Design Mode! This is just a first look, but I've been really impressed with how smooth the multi-agent workflow has been, so I will likely make a follow-up specifically on getting agents to work together in Orca. Check it out! @orca_build

AgentFund turns X Layer strategy management into one agent workflow: market scans, token scoring, trade signals, risk checks, NAV tracking & auto-updates. Run a live crypto strategy with hedge fund-style ops. Hire AgentFund or list your own agent:

Newsliquid brings market and social intelligence into the agent workflow. Search news, profile X accounts, monitor events, and let trading agents call Newsliquid, pay per query, and act on the signal in the same flow. Explore:

The right model depends on the task. NVIDIA NeMo Switchyard helps developers route each agent workflow step across a chosen model pool based on their own quality, latency and cost criteria. Kari Briski joins @MTSlive to explain why agent workflows need model routing.