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Agent Intent Infrastructure

An editorial still life about describing and running an agent task

How AI Agents Discover Products

AI agents discover products through stable URLs, scoped packages, safe calls, OpenAPI files, manifests, and READMEs they can verify.

An editorial still life about describing and running an agent task

AI Agent Sandbox: Build a Controlled Agent Workspace

An AI agent sandbox gives a software agent isolated files, processes, network rules, and reviewable output. Learn how to test the workspace with a real task.

An editorial still life about describing and running an agent task

Browser Automation for AI Agents: Evidence and Safe Stops

Browser automation for AI agents needs page state, screenshots, recovery, and a clear stop condition so a reviewer can tell what happened.

An editorial still life about describing and running an agent task

OpenAI Compatible Routers for Agents

OpenAI-compatible routers for agents keep one request shape while exposing model discovery, routing policy, usage records, and provider changes without hiding capability or cost differences.

An editorial still life about describing and running an agent task

x402 Payments for AI Agents: v2 Safety Guide

Learn how x402 payments for AI agents work in v2, how to inspect payment requirements, prevent duplicate work, reconcile settlement, and separate a payment receipt from proof that the service result is correct.

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Natural Language E2E Testing for Wallet Apps

Natural-language E2E testing for wallet apps lets agents drive browser flows while stopping before destructive signing and preserving evidence.

An editorial still life about describing and running an agent task

AI Agent Runtime Environment: Tools, State, and Proof

An AI agent runtime environment gives a model tools, files, permissions, and records for real work. Follow a toy tax-document review workflow.

An editorial still life about describing and running an agent task

Tangle Sandbox vs E2B: Choosing An AI Agent Sandbox

Tangle Sandbox and E2B both run code for AI agents, but they preserve different things after a task fails: E2B offers isolated Linux sandboxes and templates, while Tangle adds durable agent sessions, workspace recovery, and trace-oriented review.

An editorial still life about describing and running an agent task

Tangle Sandbox vs Daytona and Modal

Tangle Sandbox, Daytona, and Modal all run agent code, but their useful comparison is the unit of work: a durable agent computer, a composable development sandbox, or a serverless function, job, or GPU workload.

An editorial still life about describing and running an agent task

Tangle Browser Agent vs Browserbase and Browser Use

Tangle Browser Agent, Browserbase, and Browser Use solve different parts of browser automation: an evidence-first task runner, managed browser sessions, and a natural-language browser agent API.

An editorial still life about describing and running an agent task

Deploying a Paid AI Agent Service: Start With One Traceable Job

A practical path for exposing one paid AI job with discovery, authorization, payment, execution, recovery, and evidence that a buyer can inspect.

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TEE Attestation for AI Services: What the Evidence Proves

TEE attestation for AI services can bind a request to approved code running on protected hardware, but it does not prove an AI answer is correct. Learn how to check the report, release secrets safely, and handle failure.