Blog

Agent Intent Infrastructure

Agent discovery map showing llms.txt, manifests, OpenAPI, packages, health checks, and runtime surfaces

How AI Agents Discover Products

AI agents discover products through stable URLs, scoped packages, safe calls, OpenAPI files, manifests, and READMEs they can verify.
Agent runtime diagram showing sandbox files, processes, network policy, snapshots, and evidence

AI Agent Sandbox: Runtime, Policy, and Evidence

An AI agent sandbox gives an autonomous agent a real isolated machine with files, processes, network controls, snapshots, credentials, and policy boundaries.
Browser automation evidence panel with DOM, screenshots, traces, and stop conditions

Browser Automation for AI Agents

Browser automation for AI agents needs DOM evidence, screenshots, recovery loops, and reproducible traces, not only a model clicking through a page.
Model router diagram showing discovery, health checks, fallback, and usage attribution

OpenAI Compatible Routers for Agents

OpenAI-compatible routers help agents discover models, route calls, attribute usage, and change providers without rewriting every call site.
x402 payment sequence showing request, 402 challenge, payment proof, retry, and paid response

x402 Payments for AI Agents

x402 lets AI agents pay per HTTP request through a 402 challenge, payment proof, and retry flow. For Tangle, it is the payment edge for callable Blueprint services, not a replacement for verification.
Wallet application test run showing browser state, wallet prompt, screenshot evidence, and safe stop

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.
Layered agent runtime stack with router, sandbox, browser, payments, and verification gates

Agent Runtime Environments

Agent runtime environments combine model routing, sandboxed execution, browser control, memory, evaluation, payments, and proof surfaces.
Agent sandbox comparison showing code execution, workspace state, browser evidence, and paid service rails

Tangle Sandbox vs E2B: Choosing An AI Agent Sandbox

Tangle Sandbox and E2B both run code for AI agents, but they optimize for different failure modes. E2B is a strong code sandbox; Tangle is built for persistent agent workspaces, browser evidence, traces, and paid service execution.
Decision matrix comparing developer workspaces, serverless jobs, and autonomous agent workspaces

Tangle Sandbox vs Daytona and Modal

Daytona, Modal, and Tangle all run code, but they optimize for different jobs: developer workspaces, serverless compute, and evidence-bearing agent workspaces.
Comparison of hosted browser infrastructure, browser agent library, and evidence-first browser CLI

Tangle Browser Agent vs Browserbase and Browser Use

Browserbase, Browser Use, and Tangle Browser Agent solve different browser automation layers: hosted browsers, agent-loop libraries, and evidence-first browser task runs.
Paid AI agent service architecture with discovery, router, x402 payment, Blueprint execution, and verification

How To Deploy A Paid AI Agent Service

Deploying a paid AI agent service means exposing a stable API, attaching auth and payment, routing work, checking health, and verifying results before launch.
TEE attestation diagram separating code identity, execution boundary, payment checks, and result verification

TEE Attestation for AI Services

TEE attestation can prove code identity and execution boundary for AI services. It cannot prove task quality, model correctness, or payment fairness by itself.