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Selecting a foreground custom agent leaves the session model unchanged (Windows 1.1.23)

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#4,097 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
55/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
github
Área
ai, desktop

Línea de trabajo

Start by reproducing the issue in the GitHub Copilot desktop app with the repository's .github/agents/apex.agent.md profile, checking the model picker before and after selecting APEX. Compare the effective model with standalone Copilot CLI, then determine whether the mismatch is only in the picker or also affects inference; done means the declared model is applied or precedence differences are documented.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Short summary

Selecting a repository custom agent in the desktop app leaves the session model unchanged, despite an explicit
model: in the agent profile. The same coordinator profile switches models in standalone Copilot CLI.

Affected version or release

GitHub Copilot app 1.1.23, verified through Windows installation metadata.

Installation context
  • Windows 11 x64, build 26200.
  • Local repository, Interactive session, repository agents under .github/agents/.
  • Comparison client: standalone Copilot CLI 1.0.86 running in Ubuntu/WSL2.
  • The desktop app's embedded engine version has not been established. This is not an identical-engine comparison.
What happened?

After selecting APEX, the app showed both APEX and GPT-6 Astra / Medium. Sending a short prompt started the
session and returned a response, but the picker still showed Astra. MAI-Code-1.1-Flash was available in the model picker.

Selecting APEX in standalone CLI changed the model to mai-code-1.1-flash. In the desktop app, manually selecting MAI
worked and allowed us to continue testing. We did not need to replace the agent's model setting with Auto.

The observation concerns the session's displayed selection. We have not captured inference telemetry proving whether
the mismatch is limited to the UI or also affects the actual model request.

Steps to reproduce

Our installed .github/agents/apex.agent.md begins with:

---
name: APEX
description: Fast coordinator for APEX status, resume, and direct specialist handoff.
target: github-copilot
model: mai-code-1.1-flash
user-invocable: true
disable-model-invocation: true
tools:
  - ask_user
  - apex/status
  - apex/nextTask
  - apex/projectCreate
  - apex/projectList
  - apex/projectUse
  - apex/projectDelete
  - apex/gateDecide
---

This is the tested profile excerpt, not a separately verified minimal reproduction; its body contains APEX workflow
instructions and its project has an APEX MCP server.

  1. Open the repository in the desktop app and start an existing local repository session in Interactive mode.
  2. With GPT-6 Astra selected, select APEX using /agent or the agent picker.
  3. Check the model picker before and after a short prompt such as test.
  4. Observe that APEX is selected but the model picker remains on Astra.
  5. In standalone CLI, select APEX with the same coordinator declaration and observe the switch to MAI.

We initially encountered a separate missing-agent error in a new worktree. The model-selection observation above
persisted after creating a working local-repository session; this report is not about that startup error.

Expected behavior

Selecting a custom agent should apply its declared model when available, or clearly explain that an explicit session
selection takes precedence. The picker should show the effective model. If desktop and CLI intentionally use different
precedence rules, please document those differences.

Additional context
Lenguaje dominante
Sin datos de lenguaje
Estrellas
2.1k
Forks
157
Métricas de merge de PR
Sin PR fusionados en 30 d

Guía de contribución

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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