README: update with cascade, orchestrator/worker, plan-first discipline

- Add model cascading for 90% cost reduction
- Add orchestrator/worker system
- Add plan-first discipline
- Reflect recent advances in core logic and agent loop

README.md

@@ -9,6 +9,8 @@ Ollie starts from a simple, slightly silly question: *What might an AI agent loo
 - **Parallel fan-out** — `s/bfg -parallel 4` spawns N agents on the same prompt and collects results
 - **Multi-agent workflows in plain shell** — create named sessions, pass prompts between them via files, coordinate with `statewait`; no framework or SDK
 - **Subagent delegation** — agents can fork ephemeral subagents that run independently and return results
+- **Model cascading for 90% cost reduction** — `cascade` delegates mechanical work to cheaper models, keeping expensive reasoning for high-level planning
+- **Orchestrator/worker system** — specialized agents for task decomposition (`orchestrator`) and mechanical execution (`worker`)
 - **Any model, any backend** — Ollama (local), OpenAI, Anthropic, OpenRouter, GitHub Copilot, Kiro; switch per-session with one write
 - **Sandboxed execution** — every tool call runs inside a Landlock sandbox with configurable filesystem access
 - **Extensible via plain scripts** — drop executables into a directory and the agent picks them up; built-in tools cover file I/O, LSP (go-to-definition, references, rename, diagnostics), persistent memory, web search, browser screenshots, and task tracking
@@ -19,6 +21,7 @@ Ollie starts from a simple, slightly silly question: *What might an AI agent loo
 - **Store federation** — point tools or transcripts at a remote 9P mount to share across machines
 - **Prompt optimization** — `u/optimize` generates N candidate prompts in parallel, then judges them to return the best one
 - **Automatic context compaction** — long conversations are summarized transparently when approaching the model's context limit
+- **Plan‑first discipline** — agents write checklists before acting, reducing wasted steps and improving reliability
 
 ## Getting Started