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To find out whether an AI agent update really improves task success, compare the old and new versions on the same representative tasks, under the same conditions, using success criteria written before the test. Then check task-level changes, regressions, run-to-run consistency, grader quality, and operational costs—not just the average score.
1. Decide what the evaluation should tell you
Write down the decision the results will support: shipping the update, continuing to tune it, or investigating a possible regression. For each task, define observable conditions that count as success before running either version. Do not change the rubric after seeing which version performs better.
For a code-repair agent, for example, a task may require both a test that verifies the requested fix and checks that confirm unrelated behavior still works. This distinction is central to the SWE-bench evaluation design.
2. Build a task set that resembles the work
Choose tasks from the agent’s intended workload. Include ordinary cases, difficult cases, and known failure modes. Keep each task’s instructions and initial state the same for both versions. Public benchmarks can provide useful coverage, but they do not automatically predict performance on a team’s private tasks; add representative internal cases and, where feasible, keep some cases out of tuning.
#1 Best Overall
- High-Performance AI Processor：The MS-02 Ultra features an Intel Core Ultra 9 285HX (24C/24T, up to 5.5 GHz, 13 TOPS NPU), delivering fast and efficient performance for AI inference, algorithm development, and media workloads. A PCIe x16 expansion slot supports desktop-class GPU upgrades for advanced model training and accelerated computing tasks. It's ideal for creators, engineers, and teams handling intensive parallel workloads.
- 4 × M.2 PCIe 4.0 + 4 × DDR5 SODIMM slots:Four DDR5 SODIMM slots support up to 256 GB of memory, while ECC helps maintain data integrity in mission-critical environments. Four PCIe 4.0 M.2 slots support up to 24 TB of storage, supporting RAID 0/1/5/10, combining high-speed performance with data protection. It allows for the creation of independent scratch disks, media libraries, and project drives, providing high-throughput for production workflows.
- PCIe & USB 4.0 v2: Up to three PCIe slots can be equipped, including a dual-slot x16 GPU. The main slot supports PCIe 5.0, meeting the needs of high-bandwidth creative and computing workloads. USB 4.0 v2 (80Gbps) supports high-bandwidth external storage and displays.
- Ultra-fast Networking: Wi-Fi 7 further enhances wireless performance with next-generation speeds and low-latency stability. Intelligent bandwidth switching optimizes throughput in different network environments, ensuring optimal performance for enterprise or local networks. Dual 25GbE ports (providing up to approximately 3.125 GB/s bandwidth, about 25 times faster than traditional 1GbE), enabling seamless large-scale file transfers and parallel computing. 10GbE and 2.5GbE ports, with support for Intel vPro technology, ensure enterprise-grade remote management and deployment flexibility.
- Server-grade thermal architecture: Utilizing a dedicated CPU/GPU airflow design, equipped with a 6-pipe dual-fan cooler, it maintains stable performance even under sustained loads, delivering up to 140W Turbo power while maintaining a 100W TDP, and operating with noise levels as low as 36 dB. An integrated 350W power supply ensures stable and reliable output for demanding computing tasks and fully loaded extended configurations.
Match the test environment to the conditions in which the agent will be used. “Web task success” can mean different things depending on whether a benchmark uses a controlled offline site or the live web: WebArena uses self-hosted sites, while WebVoyager evaluates live websites, as described in OpenAI’s computer-using agent overview. AgentBench likewise spans eight interactive environments, illustrating that a result is tied to the environments and tasks tested, not to every kind of agent work.
3. Keep the comparison controlled
Change only the update being evaluated. Record the baseline and candidate configuration, including the model, prompt, tools, environment snapshot, task data, resource budget, retry rules, stopping rules, and grader version. Keep these fixed between runs. If the prompt, tool access, budget, or environment also changes, you cannot confidently attribute the result to the agent update.
4. Measure success and regressions separately
Report the share of tasks that meet the predefined success criteria, but do not rely on that aggregate alone. Show outcomes by task or category so an overall gain cannot conceal losses on important work.
Also test behavior that worked before the update, and track failures to follow important policies as a separate outcome. In SWE-bench, FAIL_TO_PASS tests check that the requested issue is fixed, while PASS_TO_PASS tests check that previously passing behavior remains intact; both are required for a task to count as resolved. For other agents, create equivalent checks for critical existing behavior.
Rank #3
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
If you use severity weights or confidence intervals, choose the weighting and statistical method before interpreting the results. There is no universal procedure established for every agent evaluation.
5. Repeat tasks when outcomes vary
Some agents produce different outcomes on repeated attempts. Run the same task instances more than once when that variability matters, and report the number of attempts and how you combined them. State whether the score is pass@1 or another statistic. OpenAI’s 2025 ChatGPT Agent system-card material describes pass@1 over a fixed subset and averaging four tries per instance for a particular setup; that is an example of a disclosed protocol, not a general rule to use four attempts.
Rank #4
- FAST RUNS IN THE FAMILY — The 16-inch MacBook Pro with the M5 Pro or M5 Max chip brings next-generation speed and powerful on-device AI to personal, professional, and creative tasks. With all-day battery life, double the starting storage,* and a breathtaking Liquid Retina XDR display, it’s pro in every way.*
- BUCKLE UP — Along with a next-generation CPU, faster unified memory, and up to 2x faster SSD storage,* M5 Pro and M5 Max feature a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance and on-device training capabilities. So you can blaze through demanding workloads at mind-bending speeds.
- BUILT FOR AI — Apple silicon, and every major component that powers it, is designed to run demanding on-device AI workloads like LLM inference and training. And Apple Intelligence helps you write, express yourself, and get things done effortlessly with groundbreaking privacy protections at every step.*
- ALL-DAY BATTERY LIFE — MacBook Pro delivers the same exceptional performance whether it’s running on battery or plugged in.*
- MACOS RUNS APPS FAST — All your go-to apps run lightning fast in macOS, including built-in apps like FaceTime and Messages. Plus, built-in virus protection and free software updates help keep your Mac running smoothly and securely.
6. Check whether the tasks and grader are trustworthy
Review prompts, tests, setup, and execution traces rather than treating a benchmark name as a quality guarantee. Look for ambiguous or contradictory instructions, requirements that are unnecessarily tied to one implementation, inadequate test coverage, misleading prompts, and environment failures. Inspect successful traces as well as failures: an agent may pass a test without actually meeting the user’s goal.
Benchmark flaws can materially affect results. In its 2024 SWE-bench Verified announcement, OpenAI described a 500-sample human-screened subset and said 93 Python-experienced software developers helped screen samples. A later OpenAI audit of SWE-Bench Pro’s 731-task public split reported that its analysis pipeline flagged 200 tasks (27.4%) as broken, while human annotation identified 249 (34.1%). Those figures describe reviews of particular datasets; they are not general benchmark error rates or recommended evaluation sizes.
Best Value
- 【High-Performance APU】The MS-S1 MAX features an AMD Ryzen AI Max+ 395 APU, integrating a Zen 5 architecture CPU (up to 5.1GHz, 16C/32T, 64M L3 Cache), an RDNA 3.5 GPU, and an NPU (50 TOPS). The total system output is 126 TOPS. It provides powerful parallel computing capabilities for demanding AI workflows. It is ideal for running local LLMs, multimodal models, and computationally intensive tasks
- 【128GB UMA Memory】Equipped with up to 128GB of LPDDR5x-8000MT/s unified memory, it enables the CPU and GPU to access a shared, high-bandwidth memory pool with extremely low latency. Ideal for large-scale AI inference, 3D workloads, and complex timelines in video editing. It eliminates traditional VRAM bottlenecks, ensuring smoother data transfer during high-intensity computations. The UMA design maximizes performance stability under high loads
- 【Flexible Expansion】The MS-S1 MAX features USB4 V2 (up to 80Gbps), dual 10GbE LAN, HDMI 2.1 (up to 8K60), a full-length PCIe x16 expansion slot, and dual M.2 slots supporting up to 16TB RAID 0/1. Wi-Fi 7 provides stronger signal coverage and a more stable wireless experience. The slide-out design facilitates upgrades and maintenance. It easily adapts to personal, studio, or rack-mount enterprise environments
- 【High-Efficiency Cooling System】Utilizing an aerospace-grade aluminum alloy chassis, copper base plate, six heat pipes, dual turbine fans, and advanced PCM thermal conductive material, it maintains stable cooling performance even under continuous load. This system supports 130W continuous power and 160W peak power operation, with a built-in 320W power supply. It boasts multiple global certifications including CCC, FCC, UL, CE, and UKCA, ensuring stable and reliable operation in various environments
- 【Cluster Design】Two MS-S1 MAX units can be configured as a dual-unit cluster to run a large 235B Q4 model locally, achieving an output speed of 10.87 tok/s. Supporting 2U rack deployment, multiple MS-S1 MAX units can be cascaded into a distributed cluster to create a high-efficiency AI computing center. A cluster of four MS-S1 MAX units successfully ran a DeepSeek-R1 671B Q4 large model. A reserved cluster power-on interface allows for unified start-up and shutdown
7. Compare operational tradeoffs alongside success
Measure the costs and constraints that matter for the intended use, such as latency, tool calls, token or compute use, human intervention, and policy violations. Keep these as separate measures from task success: an update can complete more tasks while making each one slower or more expensive. Set acceptable thresholds for the application; benchmark descriptions do not establish universal deployment cutoffs.
8. Make a decision that matches the evidence
A stronger case for shipping exists when gains appear on representative target tasks, the task set and grader are credible, critical regressions are absent, and operational tradeoffs remain acceptable. If results are close or inconsistent, coverage is weak, or failures point to grading or setup problems, gather more evidence or use a limited rollout with monitoring rather than claiming a reliable improvement.
As OpenAI puts it, “Ultimately, an eval should provide meaningful signal through benchmarks that are hard to game, easy to trust, and genuinely reflective of model capability or alignment.” The principle applies beyond coding: the evaluation should measure the work the agent is meant to do, in conditions that make the result interpretable.
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