Agents API

Headlines · 3

1. 19h
   What Is an Agent Harness? The Scaffolding Around LLMs, Explained

   According to a DeveDigest report, the term "Agent Harness" has become a buzzword in AI development after DeepSeek released an open-source desktop client called DeepSeek Harness and OpenAI launched an Agents API at its developer conference. The article explains that the model supplies only reasoning, while the harness is the surrounding system — execution loop, tool calling, context and memory management, sandbox permissions and replayable logs — that lets an agent actually finish real work. It cites testing by data analytics company Databricks finding that the same model plugged into different coding-agent harnesses can differ by more than 2x in per-task cost with no change in task quality, partly because a harness's system prompts and tool declarations consume tokens.

   DoNews · 🔥 6
2. Oct 8
   OpenAI says Jev inspired its Decisions API, built in about a week

   In a Latent Space podcast interview, OpenAI's computer-use product and engineering lead Ari Weinstein and API product lead Nikunj Handa discussed the DevDay (Sept 29) launches: Dot, GPT-6.1 Sol, computer use in the Agents API, and the Decisions API. Handa said the Decisions API was inspired by Jev and was not originally planned; the team had a working prototype in about a week, without retraining a model — reusing Luna weights, constraining outputs to structured results and optimizing first-decision latency for fast classification tasks such as support-ticket triage, and pairing with GPT Live. Weinstein said the biggest improvement in computer use over the past year is that agents now debug and retry better, and can act via page structure, accessibility data and Playwright or by writing code rather than only clicking through screenshots.

   36氪 人工智能 · 🔥 0
3. Oct 8
   OpenAI productizes multi-agent with Dots; o1 co-founder says gains overstated

   At DevDay 2026, OpenAI pushed multi-agent work into its product line, launching Dots agents that can work around the clock: a personal Dot can call Codex for coding tasks, users and their Dots collaborate inside a shared Space, enterprise Specialist Dots take on specific duties, and an Agents API exposes harnesses, multi-agent control and computer-use capabilities to developers. OpenAI researcher Noam Brown, an early architect of o1 and later reasoning models, told podcast host Dwarkesh Patel that a system of 10,000 agents solved a Millennium Prize problem in 88 hours using about 130 billion tokens, but he would not credit even 10% of that to multi-agent coordination, saying a very strong general model did the real work. He said tests so far show 4 agents roughly doubling speed at double the cost, 16 agents behaving similarly but slightly less efficiently, gains running somewhat below linear and depending heavily on the task, and coordination efficiency at the 10,000-agent scale not yet well measured.

   InfoQ 中文 AI&大模型 · 🔥 0

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