Gate News message, April 23 — The OpenAI Codex team is focused on optimizing the OpenAI model experience in OpenClaw, with Codex engineering lead Tibo Sottiaux collaborating with OpenClaw creator Peter Steinberger. Codex product lead Nik Pash discovered a critical authentication flaw: when OpenClaw was configured to use Codex harness with OpenAI models, the authentication process failed and the system silently fell back to Pi harness, causing users to believe Codex harness was functioning normally when it was not.
Pash submitted two pull requests to address the issue: one to fix the authentication bridge and another to prevent silent fallback. The improvements stem entirely from switching the underlying runtime adapter (harness) that governs how OpenClaw communicates with the model API, while the agent’s prompt and higher-level workflow logic remained unchanged.
Agent behavior showed marked differences before and after the fix. With Pi harness, the agent performed shallow polling on each heartbeat: reading heartbeat files, checking Discord, returning HEARTBEAT_OK, and ignoring other instructions. It sometimes inferred operations to execute but failed to issue tool calls. After switching to Codex harness, the agent entered a full work loop: reading workspace context, parsing task lists, checking repositories, executing edits, and attempting verification. Subsequent heartbeats could resume progress rather than repeat work.
Disclaimer: The information on this page may come from third parties and does not represent the views or opinions of Gate. The content displayed on this page is for reference only and does not constitute any financial, investment, or legal advice. Gate does not guarantee the accuracy or completeness of the information and shall not be liable for any losses arising from the use of this information. Virtual asset investments carry high risks and are subject to significant price volatility. You may lose all of your invested principal. Please fully understand the relevant risks and make prudent decisions based on your own financial situation and risk tolerance. For details, please refer to
Disclaimer.
Related Articles
GoPlus AgentGuard Launches Checkup Mode to Strengthen AI Agent Web3 Security
Gate News message, April 23 — GoPlus AI security component AgentGuard has officially released its Checkup feature, designed to address security blind spots in AI agents' autonomous decision-making and asset control, elevating AI agent security from "fragmented interception" to "systematic
GateNews21m ago
Anthropic Launches /ultrareview for Claude Code: Multi-Agent Cloud-Based Code Review
Gate News message, April 23 — Anthropic has introduced /ultrareview (research preview), a cloud-based multi-agent code review feature for Claude Code. Users can type /ultrareview in the CLI to launch a group of review agents in a remote sandbox that work in parallel to examine diffs between the
GateNews50m ago
OpenAI Launches ChatGPT Workspace Agents for Enterprise Workflow Automation
Gate News message, April 23 — OpenAI announced the rollout of workspace agents in ChatGPT on April 22, introducing shared AI agents designed to automate complex tasks and extended workflows across tools and teams within an organization. The agents are powered by Codex and operate in a cloud-based en
GateNews51m ago
Alibaba Cloud Launches JVS Crew, Enterprise-Grade AI Agent Platform
Gate News message, April 23 — Alibaba Cloud officially released JVS Crew, an enterprise-grade AI Agent construction platform designed with an "integration-first" approach. The platform enables enterprises to quickly embed AI Agent capabilities into existing apps, SaaS services, or smart hardware
GateNews1h ago
Taiwan banks team up to build local AI! Finance’s large language model goes live by the end of the year at the fastest
CITIC Financial Holding, led by CITIC Financial Holding’s 16 financial institutions, announced the launch of the “Financial Large Language Model FinLLM” project. The first release of the banking model is expected to be published in August, and in 2026 Q1, AI agents based on FinLLM will be introduced. Training will begin in May, with a budget of approximately 40–70 million yuan. Due to regulatory and localization needs, local data training will be the core, strengthening sovereign AI, building shared infrastructure, and extending to inclusive finance. The plan has been incorporated into the national AI development plan and has received cross-ministry support.
ChainNewsAbmedia2h ago
Google CEO: Capital expenditures in 2026 will reach $185 billion; ramping up investment in the era of AI agents
Google CEO Sundar Pichai announced at Google Cloud Next in Las Vegas on April 22 that Google plans to invest $175 billion to $185 billion in 2026 in capital expenditures to build the infrastructure needed for autonomous AI agents, up from $31 billion in 2022.
MarketWhisper3h ago
