There also blue team / red team that works.
The idea is always the same: help LLM to reason properly with less and more clear instructions.
All of these models are kinda toys as long as you have to manually send a minder in to deal with their bullshit. If we can do it via agents, then the vendors can bake it in, and they haven't. Which is just another judgement call about how much autonomy you give to someone who clearly isn't policing their own decisions and thus is untrustworthy.
If we're at the start of the Trough of Disillusionment now, which maybe we are and maybe we aren't, that'll be part of the rebound that typically follows the trough. But the Trough is also typically the end of the mountains of VC cash, so the costs per use goes up which can trigger aftershocks.
After 6 months in production and 1100+ learned patterns: fewer moving parts, better debugging, more reliable output. Built a full production crawler this way — 26 extractors, 405 tests — without sub-agents. Orchestrator acts as gatekeeper that redispatches uncompleted work.
There are no models that can do all the mentioned steps in a single usable context window. This is why subagents or multi-agent orchestrators exist in the first place.
The key distinction: sub-agents run within a parent context with shared state (black box). My approach uses independent parallel agents (separate terminals, separate context windows) that report back to an orchestrator. Large tasks get split into smaller dispatches upfront — each scoped to fit a single context window. The orchestrator can dispatch research to 3 agents in parallel, collect their outputs, then dispatch a synthesis task to a single agent that merges the findings.
So it's not "one context window for everything" — it's right-sized tasks with full observability per agent, and a governance layer managing the sequence and merging results.
How do they report back to the orchestrator? Tmux?
T0 (orchestrator) | T1 (Track A) T2 (Track B) | T3 (Track C)
When a worker finishes, it writes a structured report to a shared unified_reports/ directory. A file watcher (receipt processor) detects it, parses the report into a structured NDJSON receipt (status, files changed, open items, git ref), and delivers it to T0's pane.
T0 then reviews the receipt, runs a quality advisory (automated pass/warn/hold verdict), and decides: close open items, complete the PR, or redispatch. Everything is filesystem-based — no API, no database, no shared memory between agents. Each terminal has its own context window, its own Claude Code (or Codex/Gemini) session, and the only communication channel is structured files on disk.
The receipt ledger is append-only NDJSON, so you can always trace: which agent did what, when, on which dispatch, with which git commit.
I open-sourced the setup recently if you want to dig into the details.
YMMV, I’m still figuring this stuff out