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Surge finds higher thinking effort beats coding-agent harness add-ons

Surge said raising Claude Code's thinking effort to xhigh lifted its sudo L7 score by 11.3 points, more than any harness add-on it tested, with Superpowers gaining 2.4 and /goal plus /code-review 3.3.

AI & modelsAnalysisRachel NwosuPublished: 5 November 20263 min readSources 25
Surge finds higher thinking effort beats coding-agent harness add-ons

Surge tested several coding-agent setups on software engineering tasks from its sudo L7 benchmark, holding the underlying model fixed at Opus 5.5 in Claude Code. The company said running vanilla Claude Code at xhigh thinking effort improved its score by 11.3 points over medium effort. That was larger than the gain from any individual harness configuration in the experiment.

Surge said the harness comparisons ran at medium thinking effort. Superpowers, a skills plugin that forces plan-and-test discipline before coding, added 2.4 points while raising cost by 10%. Claude Code's built-in /goal and /code-review plugins added 3.3 points at 1.8 times the cost. Compound Engineering, a plan, work and review workflow with specialized reviewer agents, cost eight times as much as vanilla and produced no statistically clear improvement.

Surge described the most effective harness additions as worth 2 to 3 points. The /goal and /code-review pair reached 3.3 points, the top harness result. The cost gap matters for teams that assemble reviewer agents, skills plugins and planning loops, often at several times the token cost of vanilla Claude Code. Surge's result points to a cheaper lever first: raise thinking effort before adding orchestration.

What extra thinking bought

At xhigh effort, the model often did more work to make its answer trustworthy, Surge said. Compared with medium effort, xhigh improved the sudo L7 rubric by 14.9 points on Verification and Thoroughness, 9.8 points on Persistence and 7.0 points on Thought Partnership.

Surge gave an example from a video-editing application. A code path that spliced two clips together would crash if the first clip was missing, and the user asked the agent to make that clip optional. At medium effort, the agent fixed the immediate cause and reported success. At xhigh effort, the agent kept investigating and found that the first fix would send execution down a fallback path that also assumed the first clip existed, so the application would still crash. On that task, an extra 29 cents made the difference between a broken fix and a working solution, according to Surge.

More agents did not mean better judgment

Surge's opening example showed the limit of orchestration. One harness with 37 additional skills spawned 17 agents: five reviewed the plan for feasibility, scope and security; five looked for ways to simplify the implementation; and seven reviewed the resulting code. None noticed that the confirmation email went out when someone registered, before they had paid. Following the user's instructions would have given unpaid registrants the Zoom passcode.

The benchmark is designed to test engineering judgment, not only correctness. Surge said sudo L7 tasks take place in real codebases, many licensed from operating companies, with prompts that are often underspecified. Expert-written rubrics evaluate functional correctness alongside verification, practical judgment and communication across eight dimensions. A passing test suite does not necessarily mean the agent did a good job.

What Surge recommends

Surge's practical advice is to try the simpler lever first. If a team is spending an afternoon engineering a complicated agent setup, it should ask whether it has tried higher thinking effort, the company said.

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Sources

25
  1. 01We Tested Coding Agent Harnesses. Thinking Harder Beat All of ThemEN
  2. 02New open-source benchmark reveals reasoning as the biggest bottleneck for AI in cloud operationsEN
  3. 03Which coding-agent CLI is best for headless workflow automation?EN
  4. 04iOS Simulator MCP benchmark: eight tools for AI agentsEN
  5. 05Does a code knowledge graph help coding agents? 360 trials found no clear gainEN
  6. 06Open models can do 90% of agentic work at a twentieth of the cost of a frontier LLM: GartnerEN
  7. 07Stop teaching leadership. Start letting people practise itEN
  8. 08Anthropic's text watermark is easy to bypass on several older Claude modelsEN
  9. 09Pangram’s new models spot AI fakes in images and video, it saysEN
  10. 10GPT-6 Astra sees the matrix and reprograms other AI models directly through their weightsEN
  11. 11Xpeng unveils GroundingPI pre-trained model, outperforming GPT-6 Astra in perception benchmarksEN
  12. 12Nvidia is happy to take your money whether you're using open or closed models, says Jensen HuangEN
  13. 13New Research Tests Whether AI Models Know Their Own ThoughtsEN
  14. 14Raising the quality of insights in agentic analyticsEN
  15. 15Leading European Financial Services Organizations Embrace Open Models to Build Proprietary IntelligenceEN
  16. 16Google Launches LiteRT Tensor API for Building, Customizing, and Optimizing ML Models for On-DeviceEN
  17. 17Was I wrong about open models & cyber risks?EN
  18. 18Cities Worldwide Build and Scale AI Services With NVIDIA Physical and Agentic AIEN
  19. 19Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI ArchitecturesEN
  20. 20Free AI models suddenly look much worse: leaderboard update delivers a massive rankings hitEN
  21. 21Launch HN: Momentic (YC W24) – Agents that catch bugs by using your appEN
  22. 22Artificial Analysis v5 and the benchmarker's dilemmaEN
  23. 23NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World ModelsEN
  24. 24Hiding Capabilities in AI ModelsEN
  25. 25NVIDIA Open Models Power a New Wave of European Public Sector ProjectsEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Rachel Nwosu

Rachel Nwosu

AI, models and technology

Rachel Nwosu covers AI, models and technology for FLASH24, working from public model documentation, benchmark releases and repository histories rather than press summaries, and she skips announcements that arrive without reproducible numbers. She checks training-data claims against dataset cards and reruns reported metrics where code is available. She spends much of her week interviewing researchers and engineers, tracking model launch calendars, and comparing vendor benchmarks with independent evaluations. Outside the desk she runs 3D printers, restores old computers, and tests how models learn from internet junk. She does not publish benchmark figures she cannot trace to a source.

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