Harness leakage

Spec

dataadvisor.io. Authors: Alex Savage + the AI inside his working sessions.

harness leakage: the workflow around a model (what runs after it, what consumes its output, what the harness does with the result) written into that model's prompt. Usual author of the leak: a model writing a prompt for a model. It holds the whole machine in context, so it writes the whole machine down.

• Trigger: a prompt or a tool description is being written for a model that runs as one agent inside a larger system. A model drafting it = the common case. The incident: early CC3 (Alex's third agent system), a harness-aware dispatcher, whose job is to decide the 1 next agent, spun up build + review in one turn. Queuing review after build was the next dispatcher's job. It knew the workflow. That was the leak.
• Learning: an agent gets what it needs to do its one piece of work. What happens after ∉ its prompt. Exception: the task requires the agent to act on it. Leaked machinery does three things, none of them "just noise". Steers: the researcher told its output is for a blog post searches and writes in blog terms. Unsettles: a future state the agent cannot see or control, described in advance. Alex: "it's not just noise; it's anxiety about the loss of a thing or some sort of modified state." Invites: an agent that knows the next stage and can act there reaches into it.
• Nearest wrong: (1) more context is safer: the planner is told a dispatcher will assign its work packages; the voice agent is told the call ends when the caller is disqualified (a tool ends it; the agent only records the disqualifier); the prompt warns that earlier tool results get compacted past a limit; the tool description says it loads the artifact "without metadata". (2) the leak fixed with a rule: "do not describe downstream processing" added to the prompt. Prohibition attacks the symptom; the reader follows positive direction better than scolding; the rule is one more line every agent eats.
• Boundary: purpose ≠ machinery. The why of the work (north star) goes verbatim to every agent that works on the assignment; the plumbing that consumes the output does not. Downstream facts the output must satisfy stay: the shape of the result, the path to report. Relevance is a property of (fact, task), not of the fact: the same fact is signal for one reader's task and noise for another's. Where purpose ends and machinery begins has no ontology yet: examples, and taste. ¬ a claim about how models are trained.
• Application: by omission. The application is what the prompts do not say. Dispatcher prompt is modular: one module per preceding agent type, enough to decide the 1 next agent (.agents/tools/workflow/templates/pm.md, pm-modules/, 2026-02→). Crew templates (plan, build, review, uat) name the dispatcher only where it shapes their output. Reflect template is reflector-POV, ∅ about aggregation or normalisation (templates/reflect.md). This site's spec route never sees the narrative. Every model-drafted prompt is hand-refined before it ships; refinement is a jam between Alex and the AI, never an autonomous loop. No hand-made artifact names the principle; a section for principles.md was drafted 2026-06 and never landed. The principle exists as what the prompts leave out.
• Benefit: the dispatcher stopped jumping turns once each dispatcher saw one decision's worth. Prompts got shorter. Unmeasured beyond that.