@trq212 I hit 28% weekly Fable usage maxing session limit an hour and a half before reset with 20% weekly usage in that tiny reset window. My harness delegates to sub-agents: seems high IMO. New repo so no evals and Fable likely took on more but orchestrator role is usually an 8% job.
@waldekm Is a custom harness for Claude/Copilot/Codex. Can specify model using frontmatter in your md's. I find what works then encapsulate in a custom slash command (you call skills) to build repeatable procedures within each repo. The harness is an agentic wrapper to enforce repo rules.
@waldekm With this being said, IMO our future is finding the most token efficient least-most capable model for the task. Thats an output judgement. Economics will rule and model choice is important. I am OK using Haiku to update my wiki but (today) wouldn't use it to improve performance.
@waldekm My harness still delegates to the least-most capable model for the task to max token efficiency.
Opus 4.6->Opus 4.7 and Opus 4.8->Fable where orchestrator behaviour changes were big had the biggest impact on my outputs.
@waldekm Opus 4.8->Fable requires less specificity in agentic instructions and TBH my harness seems like an impediment. Honesty contracts are less important but enforcing guardrails = same (now 5 sub agent layers supported). Orchestrator should DIY more. Some sub agents totally replaced.
@waldekm Opus 4.6->4.7 required greater specificity in agentic instructions+wiki, honesty contracts between spec & code, and hooks to deterministically keep outputs between guardrails (only 2 sub agent layers supported). All orchestrator responsibility. Sub-agents keep using model B.
@waldekm Tests running against all 4** codes is another rule, and each must fail gracefully. Setup is more complex using canonical datasets, realistic API workflows / human journeys. DevProxy|middleware is the way I keep mocks out of code. Backoff is easy, being comprehensive is not.
@waldekm I've found Claude to be comprehensively honest ~15% of the time, ~10% of my implementation loop is actually building, the other ~90% is strict guardrails (time measurement). I am well over 4k deterministic tests on a tool with ABAC ~20 roles/100s of perms (jest & playwright).
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