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building with hermes agent : hardening the permission approval bridge
ANIRUDDHA A · 2026-05-18 · via DEV Community

i found a bug that could crash your ai agent and fixed it

i contributed a bug fix to the hermes agent project that makes the permission approval system more robust and reliable. the change handles an edge case where the permission request could return none instead of a valid response, which could cause the agent to crash or behave unexpectedly.

the fix adds proper error handling so that when the permission system gets an unexpected none response, it defaults to denying the request safely. this is a small but important improvement that makes hermes agent more stable in production environments.

why this matters for the community

when you run hermes agent and it needs to ask for permission before running a command, it uses a bridge system to communicate with the approval callback. if something goes wrong in that communication and the system gets a none response, the old code would fail silently or throw an error.

by adding a simple check that returns deny when response is none, we ensure that the agent always has a safe fallback. this prevents crashes and keeps the permission system predictable. users can trust that their commands will either be approved or denied, never left hanging.

this kind of defensive coding is important for an agent that runs on your own infrastructure and handles sensitive operations. every edge case handled well means fewer surprises in production.

code

here are the files i changed:

the main fix in acp_adapter/permissions.py adds a guard clause that checks for none and returns deny safely.

        if response is None:
            return "deny"

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the test file tests/acp/test_permissions.py adds a regression test that ensures this behavior is covered going forward.

Image descr iption

you can find the full merged pull request here:

GitHub logo fix(permissions): handle None response from ACP request_permission #13457

This PR hardens the ACP ? Hermes permission-approval bridge by safely handling an unexpected None result from equest_permission, preventing attribute errors and defaulting to a safe deny.

Fixes #13449

  • [x] ?? Bug fix (non-breaking change that fixes an issue)
  • [ ] ? New feature (non-breaking change that adds functionality)
  • [ ] ?? Security fix
  • [ ] ?? Documentation update
  • [x] ? Tests (adding or improving test coverage)
  • [ ] ?? Refactor (no behavior change)
  • [ ] ?? New skill (bundled or hub)
  • Return "deny" when equest_permission resolves to None in the approval callback.
  • Add a unit test covering the None response case to ensure the callback denies safely.
  1. Connect via an ACP client that sends an empty response to permission requests.
  2. Verify the permission is denied rather than throwing an exception.
  • [x] I've read the Contributing Guide
  • [x] My commit messages follow Conventional Commits
  • [x] I searched for existing PRs to make sure this isn't a duplicate
  • [x] My PR contains only changes related to this fix/feature (no unrelated commits)
  • [x] I've run pytest tests/ -q and all tests pass
  • [x] I've added tests for my changes (required for bug fixes, strongly encouraged for features)
  • [x] I've tested on my platform
  • [x] I've updated relevant documentation (README, docs/, docstrings) � or N/A
  • [x] I've updated cli-config.yaml.example if I added/changed config keys � or N/A
  • [x] I've updated CONTRIBUTING.md or AGENTS.md if I changed architecture or workflows � or N/A
  • [x] I've considered cross-platform impact (Windows, macOS) per the compatibility guide � or N/A
  • [x] I've updated tool descriptions/schemas if I changed tool behavior � or N/A

and the repository is here:

Hermes Agent

Hermes Agent ☤

Documentation Discord License: MIT Built by Nous Research 中文

The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's not tied to your laptop — talk to it from Telegram while it works on a cloud VM.

Use any model you want — Nous Portal, OpenRouter (200+ models), NovitaAI (AI-native cloud for Model API, Agent Sandbox, and GPU Cloud), NVIDIA NIM (Nemotron), Xiaomi MiMo, z.ai/GLM, Kimi/Moonshot, MiniMax, Hugging Face, OpenAI, or your own endpoint. Switch with hermes model — no code changes, no lock-in.








A real terminal interface Full TUI with multiline





how it works

the approval callback function handles permission requests from the ACP system. when a command needs approval, it runs a coroutine to get the response from the permission handler.

in the try block, if the coroutine completes successfully, the code checks if the response is none. if it is none, the function immediately returns deny.

this approach is straightforward but effective. it means that even in unusual situations where the permission system does not return a proper response, the callback always produces a valid result. the agent continues running instead of crashing.

the test confirms this behavior by mocking the coroutine to return none and verifying that the callback returns deny.

my tech stack

the changes were made using python with the standard library asyncio module for handling asynchronous permission requests. the test uses pytest with mock objects to verify the behavior without needing a full system setup.

lessons learned

working on this fix taught me a few things about building reliable agent systems.

first, edge cases matter. in a system that handles permissions, every unexpected input should have a clear handling path. crashing is never the right answer.

second, testing edge cases is just as important as testing happy paths. the regression test ensures that this none response case keeps working correctly as the codebase evolves.

third, the hermes agent codebase is well structured and welcoming to contributions. the team uses copilot for code review, which helps catch potential improvements early.

i am glad to have contributed to an open source project that gives developers control over their own AI agents. the hermes agent challenge is a great way to get involved and learn about agentic systems.


thanks for reading.

if you want to explore hermes agent or contribute to it, check out the github repository linked above.

and thanks again ...