From cac31746c45bf0f3af593a38e3784e8dc5af2051 Mon Sep 17 00:00:00 2001 From: Ollie Agent Date: Sun, 17 May 2026 18:18:58 +0200 Subject: [PATCH] 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 | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 75f1538..156b31f 100644 --- a/README.md +++ b/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