Agent Harness engineering resources: Microsoft Agent Framework, Anthropic evals, papers
goyalshaliniuk · x · 2026-11-01
A curated set of resources for building reliable AI agents:
- Microsoft Agent Framework / Agent Harness docs: the harness is the runtime scaffolding that turns an LLM into a working agent — driving model/tool calls, managing conversation state and context, applying approval policies, and sustaining multi-step tasks. Its architecture composes a chat client, a chat pipeline (function invocation, message injection, history persistence, optional compaction), agent/context providers (session instructions, tools, memory, todo state), and middleware (approvals, observability, bounded looping).
- Anthropic's "Demystifying Evals for AI Agents": guidance on agent evaluation.
- Snowflake Harness Engineering plus a research paper on AI harness engineering.
The author's thesis: reliable agents aren't built by picking the most powerful model, but by engineering the system around it — the model provides intelligence; the harness provides structure, control, and reliability.
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