Last week was all about the harness. Gone are the times when the models were all things-in-themselves. Now, as with early Homo erectus, the tools are the factor of survival. If we look at the types of harness-related posts and announcements, we will see three trends.

Harness products

Several companies have released either agents or infra for agents at once.

OpenAI has released the execution framework that their Codex (the CLI one) is based on. Unsurprisingly called Harness, it is their answer to the Claude Agent SDK and the GitHub Copilot SDK. The Harness provides the execution loop, memory, tools, and other necessities of agentic life. You can use it in three different ways. First, you can just use codex exec to run non-interactive jobs. Second, you can use the Codex SDK for building workflows. And last, you can use app-server to build apps that require conversation handling. One point worthy of attention is that they claim that the Harness improved the performance of the GPT-5.6 Sol model from 13.3% to 38.3% on ARC-AGI-3. It’s unclear what harness (not capitalized) was used as a baseline, though.

Google finally GA’d their Antigravity inside Gemini Enterprise Standard, Plus and Standard Emerging Market subscriptions, meaning that it can now be used in an enterprise setting. The proposition is the usual one for such products: pooled tokens, budget caps, MCP control, Visual Studio Code and JetBrains IDEs. There are still features lacking, such as per-team and per-user spending caps, but it’s definitely progress.

With AWS you can now give your agent your money. Following the creation of the Agentic Payments Alliance, they’ve put the Bedrock AgentCore Payments service into general availability. The service supports the Machine Payments Protocol and x402, giving an agent both normal payment and microtransaction support. Honestly, I would give an agent only money I intend to burn (I don’t have any), but at least it has strict deterministic payment-limit guardrails.

Agent performance

Now, an area somewhat more interesting for me personally. Recent results have shown that a good harness can raise the quality of agentic work AND reduce the costs, so it is something that organizations should pay close attention to. Let’s see what last week brought.

First, NVIDIA has published results on their Agentic Variation Operators (AVO) harness. This harness let Claude Opus 5 solve all 183 levels across all 25 public ARC-AGI-3 environments. A 100% result on one of the hardest benchmarks we have right now. With the baseline harness, the performance drops to just around 30% of the tasks. The main components that allowed them to achieve this result are their memory management layer and a supervisor component that controls the primary agent when it drifts. It must be noted that although ARC-AGI-3 is open-ended, it is still winnable, and the supervisor can use this information to steer the agent. The real world is more complex, so it is yet to be seen whether this pattern will survive there.

Second, Inherent’s Faraday Agent, running on Qwen 3.6 (27B) and finetuned using reinforcement learning, has supposedly outperformed Claude Opus 4.8 and GPT-5.5 at reproducing the results from scientific papers without prior knowledge of the outcome. It sounds impressive, but remember that reproducing the results from a paper is a task with an immediate, known reward (the results were either reproduced or not), so it follows the pattern of agents working well in bounded environments with clear rewards. They are doing less well in the real open world.

Third, a research paper has shown that a strong model that builds a harness for a weaker one can improve the performance of the latter quite significantly. On Theory-of-Mind benchmarks, the improvement was almost 2x, from 0.49 to 0.91. How does it do that? The answer is simple: it converts non-deterministic model execution to reproducible logic, thus removing the most common failure modes from the weak model itself. Much like the now common practice of solving not the problem introduced by the agent, but its approach to this problem, so it doesn’t happen again.

We’ve seen that we can improve the performance, but the pressing matter for a lot of organizations now is…

Cost optimization

And yes, a good harness can do this as well. For example, OpenAI’s harness not only improved the results, but also decreased token consumption sixfold. That’s a lot. We see similar results from other sources. Some of them report that the difference in cost per task between organizations with good and bad harnesses and processes can be as much as 1000x.

One way to achieve such an improvement is to select the best model for the job. By best, I mean the cheapest one that can still complete it. Selecting such a model is tedious, so it’s natural that companies started to use and release model routers. Most recently, Snowflake introduced one into their Cortex AI. Using the Cortex AI Gateway that sends the request either to a frontier model or to an open-weight model, they improved token efficiency threefold for dbt workflows. The exact mechanism is not published, but the classifier is trained on historical data and thus works well for the well-trodden path. How it will perform on untypical requests is not clear, but we can expect that there will be minor classes of tasks on which the performance will actually worsen.

So, we see that a good harness is a pretty damn powerful thing, but it is also often context-specific. Building harnesses for specific verticals, jobs and tasks is starting to be all the rage. We can expect interesting developments in this area.