is the orchestration based on folder structure/ICM better that creating a knowledge graph and a semantic layer to get the required context from the knowledge graph. Which will yield better results if we evaluate both. What are the pros and cons of each approach?

Ornith-1.0: the open-source coding LLM that matches Claude Opus 4.7 — and runs on your laptop. This open-weights agentic-coding model hits 77.5 on Terminal-Bench and 82.4 on SWE-bench Verified, beating Claude Opus on both, then ships a 9B size you can `ollama run` locally. MIT licensed, four sizes, and it taught itself the harness.

I run a chunk of my commercial follow-up through agents now, and the wall I keep hitting isn't the writing — it's that the agent doesn't know where the relationship actually stands. Who owes whom a reply, whether we're warm or stalled, what the last real signal was. So I either hold all of that in my head (and I become the bottleneck), or I dump the whole thread history into context and the message comes back generic and expensive. For those of you running outreach or follow-up through agents: how do you represent relationship state as something structured the agent reads and acts on — a status field, a per-contact context file, a "next best touch" the system proposes? And just as important, how do you keep the output from feeling automated on the other end? I'm after the structural fix, not a better prompt. What actually lives in your setup, and what did you try that quietly failed?