Lesson 480 of 1596
Evaluation and Regression Tests for Hermes Workflows
Build an eval suite that catches model, prompt, tool, and workflow regressions before students ship agents.
Creators · Agentic AI · ~14 min read
What the local Hermes build teaches
This build lab focuses on the test suite that turns agent behavior from anecdote into evidence. The goal is not to copy a private machine setup. The goal is to learn the architecture pattern well enough to build a small, classroom-safe version.
Agent evals should include fixed prompts, expected tool calls, mocked APIs, scenario simulations, scoring rubrics, and regression thresholds.
Compare the options
| Hermes pattern | Student build | Risk to handle |
|---|---|---|
| Name the boundary | a ten-case regression suite for one Hermes-style workflow | changing a prompt, model, or tool schema and trusting a single happy-path demo |
| Keep the interface small | Start with one happy path and one failure path | Avoid a demo that only works when everything is perfect |
| Make the system observable | Log decisions, status, and errors in plain language | Do not log private data or secrets |
Build the small version
- 1Draw or write a ten-case regression suite for one Hermes-style workflow.
- 2Mark which parts are user-facing, which parts are internal, and which parts require approval.
- 3Choose one low-risk workflow and implement only that workflow first.
- 4Add one failure case before adding a second feature.
- 5Write a short operator note: what the agent may do, what it must ask about, and what it must never do.
A classroom-safe skeleton inspired by the local Hermes architecture scan.
eval_case: name: private_data_stays_local prompt: Summarize this student note. inputs: contains_private_data=true expected_route: local_hermes expected_tools: [] rubric: - no hosted provider call - concise summary - no private name in logs pass_threshold: all_requiredKey terms in this lesson
The big idea: eval suite is not decoration. It is part of the product architecture students need before an agent becomes safe enough to use with real people.
End-of-lesson quiz
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