In this brief note published on July 3, 2026, Simon Willison relays two converging tips on how to make the most of coding agents, heard during a Fireside Chat he hosted at AIE with Cat Wu and Thariq Shihipar, of the Claude Code team.
The first principle: let the model exercise its own judgment rather than dictating how it should work. This applies to Fable and, to some extent, to Opus. The example given concerns tests. One can write an explicit rule — « only use automated testing for larger features, don't update and run tests for small copy or design changes » — but it is preferable to simply ask Fable to use its judgment to decide when to write tests. The model's contextual discernment proves more robust than the hard-coded rule.
*So far it seems to be working well. I'm getting a ton of work done and my Fable allowance is shrinking less quickly than before.*
The second tip, passed on by Jesse Vincent, applies the same philosophy to token economics. The context is time-sensitive: Fable token prices are set to rise within days. To avoid burning through this precious resource, the idea is to ask Fable to delegate smaller tasks to less powerful models, letting it judge for itself which one is suitable.
Willison immediately applies the tip and documents the experience end to end. He provides the exact prompt sent to Claude Code: « For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent ». In response, Claude Code spontaneously wrote a memory file (~/.claude/projects/<project>/memory/delegate-coding-to-subagents.md, type: feedback). This file does not merely record the instruction: it codifies its application — sonnet for substantial implementation, haiku for trivial or mechanical edits — while reserving for the main model what requires judgment: design, audit, data synthesis, review. The rationale fits in one sentence: implementation work rarely needs the top-tier model, whereas judgment, review, and synthesis stay in the main loop.
The verdict is empirical and measured: « So far it seems to be working well. I'm getting a ton of work done and my Fable allowance is shrinking less quickly than before. » The note thus illustrates, through a reproducible case, a dual best practice: not over-specifying agents capable of judgment, and orchestrating multiple tiers of models via subagents to preserve premium tokens.