# Hugging Face — Same Model, Different Harness: CUGA, Hermes, and Deep Agents — When to Pick CUGA?

- Company: Hugging Face (huggingface.co)
- Announced: 2026-11-03T15:57:39+00:00
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://huggingface.co/blog/ibm-research/cuga-harness
- Record: https://forck.live/items/31068-same-model-different-harness-cuga-hermes-and-deep-agents-when-to-pick-cuga
- Subject: Platform
- Models affected: GLM-5.3-flash, GLM-5.2
- License: Apache 2.0

IBM researchers benchmarked three open-source agent harnesses with the same open-weight model and found harness choice moved AppWorld task completion from 30% to 81%. The comparison tested CUGA, Nous Research's Hermes and LangChain's Deep Agents on 90 AppWorld tasks in 30 scenarios, each run with GLM-5.3-flash and one attempt per task. IBM says the results are not a verdict on the other two harnesses because neither was built for multi-app API work, and the point is that a harness has to fit the job. CUGA is IBM's open-source harness for enterprise work, built to enforce rules the model cannot override and to deploy inside a customer's environment. IBM reports consistency figures of 20 scenarios for CUGA, 8 for Hermes and 2 for Deep Agents, where a scenario passes only if all three of its variants pass. The company states three limits: the 90-task sample may not generalize, the setups were not identical, and the runs used no policies so they do not measure policy enforcement.

## Evidence

Verbatim from https://huggingface.co/blog/ibm-research/cuga-harness:

> There are nearly 200 agent harnesses to choose from. We gave three of them the same open-weight model and 90 AppWorld tasks, and changing the harness moved task completion from 30% to 81%. For enterprise use, CUGA adds what this benchmark does not test: rules that the harness enforces and deployment inside your own boundary.
> 
> Community lists such as awesome-agent-harness and best-of-Agent-Harnesses track nearly 200 of them, and they make one point clearly: the harness matters, not only the model. In one analysis that best-of-Agent-Harnesses cites, the same GLM-5.2 model went from 23% to 52% on SWE-bench Pro when only the harness changed.
> 
> So the right question is not "which harness is best?" but "which harness fits my workload, with my model?" Hermes from Nous Research is built for always-on personal assistants, and Deep Agents from LangChain for long, multi-step tasks such as research. Both are excellent at those jobs.
> 
> Enterprise work is a different job. Agents act on business systems, such as CRM and payments, and they must follow rules that hold whatever the model decides.

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