Why the coding agent ignores your rules.
It isn't just memory loss, and it isn't a bad prompt. Your most important rule is losing a competition for the model's attention, on Control Surfaces you can change and measure.
Olinave turns that into a measurable problem. The framework is a named taxonomy grounded in controlled experiments and Anthropic's own published guidance.
From a diagnostic scan of your repo to a full day on your Claude Code setup.
Find out why your coding agent ignores you, learn how to fix it, or we fix it with you on your own repo.
The Claude Code Diagnostic
We scan your current Claude Code setup and hand you a scored map of your Control Surfaces, with every gap quoted straight from your own code.
By request → 02 / WorkshopHalf-day Claude Code workshop
The full framework in half a day. You'll see how the agent really reads you, where your rules quietly fail, and which rule belongs where. Your team leaves on one shared model.
See the workshop → 03 / IntensiveFull-day Claude Code intensive
the complete optionEverything in the half-day, plus a pre-session Claude Code Diagnostic and a full afternoon strengthening your own repo with us.
See the workshop →How we work.
Suggestion vs instruction
Set suggestions apart from instructions. Most of what you write as a rule is a request; not a guarantee.
Gate it, or win attention
Every failure collapses to one choice: gate the rule, or make it win the attention competition.
Measure, don't trust
Output passing review is not evidence a rule was followed. We measure whether it held instead of trusting that it did.
Practitioners, not theorists.
Olinave is Philip Forshaw, ex-Apple AI Strategy & Operations lead, a technical leader with decades of training and workshop delivery behind him. The framework is patent-pending.
The method is a named taxonomy of the recurring failure modes, grounded in controlled experiments and Anthropic's own published guidance. Separately, we applied it to 300+ real Claude Code setups to measure how common each failure is.
Agent Harness Engineering — a named taxonomy grounded in controlled experiments and Anthropic's published guidance. Framework patent-pending.
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