Olinave
Agent Harness Engineering

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.

Method

How we work.

Observe

Suggestion vs instruction

Set suggestions apart from instructions. Most of what you write as a rule is a request; not a guarantee.

Decide

Gate it, or win attention

Every failure collapses to one choice: gate the rule, or make it win the attention competition.

Verify

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.

Who we are

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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