Hey HN. I've been running 10 agent pipelines in parallel for a few weeks. Agents come up with app ideas and ship them autonomously. The result is ~30 production apps with working payments and live users and most importantly apps that (in my humble opinion!) don't suck. I wanted to share the architecture and see how others are tackling orchestration.
To make this possible, I built Frequency, a config-driven orchestration layer where workflows are YAML state machines. State is repo-local JSON (no database, no message queue), agent calls run in git worktrees, and about 40% of steps are agent calls with 60% deterministic scripts.
The hardest problem for me was tackling cross-pipeline coordination. Marketing needs to know deploy finished and bug fixes need to flow back through build etc. We ended up with declarative predicates rather than imperative callbacks. The blog covers the three predicate types that handle the patterns we've hit.
Still figuring out: agent quality at scale (Claude reviewing Claude's code is tautological), non-deterministic throughput (2-5/5 pass rate depending on the day), and token limits are the main ceiling right now (which I guess is the better problem to have).
Curious what patterns others are seeing for multi-agent coordination. Are all of you solving this differently?
I'd love to learn more and hear feedback about what I can do differently, and if this tool would be useful to anyone else.
I built a software factory that runs ttself · HackerTrans
To make this possible, I built Frequency, a config-driven orchestration layer where workflows are YAML state machines. State is repo-local JSON (no database, no message queue), agent calls run in git worktrees, and about 40% of steps are agent calls with 60% deterministic scripts.
The hardest problem for me was tackling cross-pipeline coordination. Marketing needs to know deploy finished and bug fixes need to flow back through build etc. We ended up with declarative predicates rather than imperative callbacks. The blog covers the three predicate types that handle the patterns we've hit.
Still figuring out: agent quality at scale (Claude reviewing Claude's code is tautological), non-deterministic throughput (2-5/5 pass rate depending on the day), and token limits are the main ceiling right now (which I guess is the better problem to have).
Curious what patterns others are seeing for multi-agent coordination. Are all of you solving this differently?
I'd love to learn more and hear feedback about what I can do differently, and if this tool would be useful to anyone else.