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Useful patterns for AI systems that have to work outside the demo.

Technical guides, field notes, and practical explanations from the team building Trampoline.

Bound Context and Writes in Enterprise AI Agents

Enforce permissions before material enters model context, then turn consequential writes into reviewable plans that require explicit human approval.

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Two complete model–harness assemblies balance on a scale above equal stacks of budget tokens, emphasizing fair matched-expenditure comparison.

September 9, 2026

How to Evaluate a Long-Horizon Agent Harness

A decision-grade evaluation compares complete model–harness configurations fairly, identifies where extra inference yields marginal returns, and verifies that success occurred in a valid environment.