#OpenAI推出智能体API公测 On September 10, 2026 (U.S. time), OpenAI announced that the Agents API has entered public beta, followed by concentrated reporting in China on September 11. This interface opens up the “harness” that supports Codex and the Enterprise ChatGPT to all developers. With a single call, users can create long-running intelligent agents. OpenAI will host the session, perform context compression, handle tool orchestration, coordinate sub-agents, and provide sandboxing. Billing is based only on tokens and tool calls.
The main reason is that long-task agent engineering is too heavy: enterprises need cross-hour and cross-day automation, which requires self-managed state, recovery, file execution, and multi-agent collaboration—leading to high costs and poor stability. OpenAI productized its internal Codex execution infrastructure, lowering the development barrier, and shifting from “selling model APIs” to “selling agent runtime capabilities.”
Higher agent usage volumes directly benefit reasoning compute and cloud services: Nvidia (NVDA)📈 and Microsoft (MSFT), leveraging Azure and the OpenAI ecosystem📈; enterprises’ data orchestration stands to benefit as well, which can be seen in PLTR📈. On the Korea side, increased needs for long context and high-bandwidth storage drive demand for Samsung Electronics and SK hynix📈. In the short term, if public beta expands the developer base, the compute, cloud, and storage supply chain could see more upside; however, restrictions such as usage-based pricing, data compliance, and U.S.-region data residency may suppress the performance of pure concept stocks. Some richly valued agent-related ideas may correct📉.
Over the medium to long term, agent standardization will amplify demand for reasoning tokens, HBM, and enterprise automation, supporting NVDA, MSFT, Samsung Electronics, SK hynix, and PLTR overall📈. The risks are slower monetization, price wars, and valuation pullbacks. For concept stocks with high volatility, it’s advisable to buy in batches rather than chasing gains.
$NVDA
$SAMSUNG
$SKHYNIX
The main reason is that long-task agent engineering is too heavy: enterprises need cross-hour and cross-day automation, which requires self-managed state, recovery, file execution, and multi-agent collaboration—leading to high costs and poor stability. OpenAI productized its internal Codex execution infrastructure, lowering the development barrier, and shifting from “selling model APIs” to “selling agent runtime capabilities.”
Higher agent usage volumes directly benefit reasoning compute and cloud services: Nvidia (NVDA)📈 and Microsoft (MSFT), leveraging Azure and the OpenAI ecosystem📈; enterprises’ data orchestration stands to benefit as well, which can be seen in PLTR📈. On the Korea side, increased needs for long context and high-bandwidth storage drive demand for Samsung Electronics and SK hynix📈. In the short term, if public beta expands the developer base, the compute, cloud, and storage supply chain could see more upside; however, restrictions such as usage-based pricing, data compliance, and U.S.-region data residency may suppress the performance of pure concept stocks. Some richly valued agent-related ideas may correct📉.
Over the medium to long term, agent standardization will amplify demand for reasoning tokens, HBM, and enterprise automation, supporting NVDA, MSFT, Samsung Electronics, SK hynix, and PLTR overall📈. The risks are slower monetization, price wars, and valuation pullbacks. For concept stocks with high volatility, it’s advisable to buy in batches rather than chasing gains.
$NVDA
$SAMSUNG
$SKHYNIX