Agent Tooling & Context: Hooks, Terminal Agents, Editable Memory, Drive I/O

React for Agents: Astro Creator Brings Hooks to Flue adds React-style agent hooks so agents can manage state, attach tools, and compose subagents at runtime. This supplies primitives for building composable, runtime-orchestrated agent architectures — a practical step toward agentic coordination and operationalizing orchestrations (Principle 09).

Mole — Deep research agent for your terminal enforces per-run model budgets, verifies quoted sources, and analyzes local data in a privacy-preserving terminal agent. That pattern matters because it shows how to build auditable, budgeted agents that preserve provenance and local-data privacy for production workflows (Principles 02 & 07).

ThoughtDAG — An editable context graph for LLM conversations represents conversation history as an editable directed graph so context is visible and mutable rather than hidden. Outcome engineers can use this to prevent context pollution, make memory legible, and create deterministic, auditable context pipelines (Principles 06 & 11).

Auto-research with Codex: How I achieved a 232x Faster Kernel demonstrates an agent-driven loop that used Codex and GPU Mode tooling to achieve a 232× speedup on a batched kernel. It’s a concrete example of agents-as-engineering labor: designing tool chains, measuring outcome improvements, and iterating agent policies to deliver measurable system wins (Principles 03 & 07).

ChatGPT subscribers can now open and edit Google Drive files from inside the chat lets the model read and modify Drive documents directly in-chat. That integration changes how you supply and persist context to agents and forces you to think through permissions, provenance, and gating for write operations in agentic systems (Principles 06, 11 & 15).