LLM harnesses - fix prompt and context bottlenecks with orchestration

Executive Summary

Prompt and context engineering becomes the human bottleneck when scaling LLM apps. Build a harness - an orchestration layer that runs prompts, manages context, tests flows, and captures workflows as a second brain to automate AI logic. The source identifies an acute operational bottleneck from prompt plus context engineering. At the same time there is broad LLM adoption across product teams, proliferation of vector DBs and hosted models, and increasing cost pressure from token usage. These factors make an orchestration harness valuable now - it reduces repeated human work, controls token spend by trimming context programmatically, and adds observability enterprises demand for AI workflows. The source frames the harness as a second brain the author ran before the term became popular, solving the exact bottleneck of human-managed prompt and context engineering. A harness product can combine orchestration, deterministic test suites, context window management, and provenance capture to create a workflow-first product. That lets it build a data moat of customer workflow recipes and runtime logs, enabling model-agnostic optimization and runbook libraries that are hard to replicate with point tools alone.