awesome-harness-engineering

I ran into a useful trending resource: **ai-boost/awesome-harness-engineering**. It’s not a single SDK; it’s an “engineering harness” checklist turned into a navigable list—focused on the practical pieces you end up wiring together when you move from demos to production agent systems.

I’d call it most relevant the moment you’re already using an LLM-agent framework (e.g., LangGraph-style graphs, tool calling, MCP clients), but you’re missing the scaffolding around it: evaluation loops, permission boundaries, memory behavior, and the visibility you need when an agent starts doing the wrong thing at 2am.

Concretely, one scenario: you add a new tool and an agent begins calling it in unexpected contexts. At that point you need (a) instrumentation to see tool invocations and intermediate state, (b) permissions to constrain tool access, (c) evals that reproduce the failure mode, and (d) orchestration patterns to keep the control flow deterministic.

What I like about the repo is that it groups the “agent harness” concerns explicitly. I’d start from the top-level entry points and then follow the categories that match your current gaps.

- Tools and patterns for orchestration and agent “harness” layers - Memory approaches (state, retrieval, and persistence patterns) - Permissions and MCP-style integration boundaries - Evals and observability/telemetry hooks

Entry point to click: the GitHub page itself (the list). If you’re building solo, treat it like a quick map to the next dependency to evaluate rather than a place to read end-to-end.