Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production.
To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows.
Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion.
For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser
Google has introduced Agent Executor, an open source runtime aimed at helping enterprises run AI agents more reliably at scale, as attention shifts from building agent prototypes to managing the operational challenges of putting them into production.
To address those production-related challenges, the runtime, according to the company, comes with capabilities that are geared towards supporting long-running and distributed agent workflows.
Typically, long-running agent workflows are AI-driven tasks that execute over extended periods, from minutes to days, often involving multiple steps, system interactions, pauses for human input, or recovery from interruptions before reaching completion.
For such workloads, the runtime includes support for durable execution, allowing workflows to resume after outages or human approvals, along with secure sandboxing for isolating agent components, session consistency controls for distributed workflows, and connection recovery features intended to preser
The entire machine-payment ecosystem now runs almost entirely on a single stablecoin. More than 98% of all settlements made by AI agents in the past year were processed in Circle’s USDC, according to a new report from crypto investment firm Keyrock — a concentration that researchers say carries risks the industry has largely ignored. Related […]
As MCP crosses 97 million monthly SDK downloads and AI agents move into production workflows, authentication has become the most critical infrastructure decision teams face. This guide ranks the eight leading platforms — WorkOS, Stytch, Auth0 by Okta, Composio, Nango, Arcade, TrueFoundry, and Cloudflare — on spec compliance, enterprise identity depth, integration breadth, and real-world fit for 2026 deployments.
The post Best Authentication Platforms for AI Agents and MCP Servers in 2026 appeared first on MarkTechPost.
Stablecoins became the default settlement layer for AI agents as crypto payment rails can handle sub-dollar transactions more efficiently, says a report from Keyrock.
The post AI agents are starting to pay with crypto as Coinbase, Stripe and Visa want in, Keyrock report says appeared on BitcoinEthereumNews.com.
Artificial intelligence (AI) agents autonomously spending money online is still a tiny market, but some of the world’s largest tech, payments and crypto firms are already racing to build the infrastructure for it, Keyrock said in a new report. The crypto trading and investment firm estimated that AI agents settled over $73 million across roughly 176 million transactions on blockchain rails between May 2025 and April 2026. The volumes remain negligible compared to traditional finance (TradFi). Visa, for example, alone processes $14.5 trillion annually. But the significance lies less in the headline U.S. dollar value and more in how quickly the infrastructure stack is forming, the report argued. Global firms such as Coinbase (COIN), Stripe, Google (GOOG) and Visa (V) all rolled out competing systems for machine-to-machine payments. The broader
