Finding
Hermes debugging stays chaotic when every red-text failure is treated as a unique emergency instead of being classified into a repeatable failure taxonomy.
Current
A real Hermes installation will regularly encounter errors from tools, providers, shells, browser automation, cron jobs, MCP servers, credentials, file access, rate limits, and malformed prompts. The weak point is not the presence of errors; red text is normal in agentic operations. The gap appears when agents jump straight to panic fixes without first naming the failure class, likely boundary, owner, and verification path, causing repeated trial-and-error and weak learning across sessions.
Suggested
- Add a first-response failure taxonomy to debugging work. Exact change: add a “Red Text Classification” section to the
systematic-debuggingskill or debugging runbook requiring every error to be labeled before fixing as one of: configuration, dependency, permission, credential, network, provider/model, tool misuse, prompt/schema, filesystem, timeout/rate-limit, or upstream service failure. - Create an error-pattern capture habit after repeated failures. Exact change: update the Optimizer Agent review prompt with: “Scan recent sessions for the same error class appearing 2+ times; recommend a skill patch, config check, cron adjustment, or runbook entry instead of another one-off workaround.”
- Add a verification step that matches the error class. Exact change: patch
SOUL.mdor the task completion checklist with: “After fixing red text, verify the smallest affected boundary: rerun the exact command for shell/tool failures, validate config for configuration failures, run a pinned workflow test for cron/MCP failures, or perform a provider smoke test for model/API failures.”
Impact
This turns error handling into operational signal rather than noise. A shared failure taxonomy helps Hermes agents choose the right tool, avoid random retries, and preserve reusable fixes in skills or runbooks. Over time, repeated red text becomes a map of where the installation is brittle, which improves reliability without exposing private logs or implementation details.
Effort
Small — this requires one debugging skill or runbook patch, one optimizer review habit, and one verification checklist line. No new infrastructure is needed; the improvement comes from classifying errors before acting on them.
Public page note
Safe public content includes the maturity principle, generic error categories, debugging habits, and public-safe examples of classification and verification. Internal-only content includes raw stack traces, private logs, credentials, provider responses, filesystem paths, config values, chat excerpts, customer data, and exact operational incidents.