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Make Evidence Pursuit Goal-Complete
Plan evidence needs, acquire and validate sources, track coverage, resolve disagreements, and stop when the answer is supported rather than when activity merely ends.
Building thoughtful software with AI.Notes from the systems behind the work.
Plan evidence needs, acquire and validate sources, track coverage, resolve disagreements, and stop when the answer is supported rather than when activity merely ends.
Use a user-authoritative goal frame, relevant context, evidence binding, publication checks, and multi-turn evaluation to keep every turn aligned with the user’s current objective.
Give planning, editing, review, summarization, browser, and shell work explicit contracts, bounded authority, deterministic fallbacks, and evidence-based promotion.
Combine immutable run evidence, distinct revisions, blinded gold cases, inter-evaluator agreement, adjudication, and replication artifacts so automated improvement advances on trustworthy measurements.
Create lightweight experimental loops that turn curiosity into evidence, preserve responsible boundaries, and help teams learn before committing to larger bets.
Connect runtime experiments, training gates, behavioral evaluation, performance suites, retained evidence, and documentation into one cumulative learning loop.
Turn reusable guidance into a learning product with time-aware attribution, evidence-linked evaluation, versioned amendments, and preserved lineage.
Layer normalization, deterministic observations, structured interpretation, semantic versions, and drilldowns to create behavioral metrics people can understand and use.
A question-led domain map, connected evidence, progressive disclosure, reusable data mechanics, first-class actions, and measured outcomes turn fragmented tools into one improvement journey.
Evidence-backed triggers, bounded procedures, portable sources, scenario tests, progressive discovery, and active curation turn workflow lessons into durable shared capability.
Privacy-aware traces, structured facets, complementary evaluation, human review, and measured improvements turn completed AI-assisted work into shared capability.
Capability maps, useful challenge, layered assistance, explanatory feedback, reflection, honest progress, transfer, and increasing autonomy help products turn activity into durable human growth.
Scoped contracts, least-privilege evidence, deterministic validation, human review, sandbox evaluation, and maintained releases turn repository judgment into reusable agent capability.
Semantic checkpoints, distinct progress dimensions, evidence-led adaptation, and direct outcome verification make long-work evaluation useful and humane.
Immutable run records, versioned evaluators, bounded evidence, append-only annotations, and compatible comparisons turn evaluation into compounding product knowledge.
Decision-shaped questions, pinned environments, governed inputs, durable run manifests, validated outputs, compatible comparisons, and bounded findings make experimentation compound.
A benchmark plan should remain disabled until its inputs, prerequisites, approvals, isolated run boundary, activation steps, and scoring handoff are explicit and locally validated.
Multi-step background work should reserve a complete bounded outcome before it begins, charge only unfinished dependencies, reconcile estimates with provider observations, and release unused capacity deliberately.
A practical framework for deriving useful conclusions without disguising missing evidence, especially when targets decompose, causes remain partly unknown, or sources conflict.
A disciplined evidence strategy acquires the full candidate population and leaves selection, comparison, and recommendation to the controller.
A rigorous evidence architecture lets models bridge lexical gaps while making exact, independently verified passages the final authority on claim coverage.
A robust AI controller can use rejection feedback to improve an answer while preserving the user's original authority, completion criteria, and evidence obligations.
Reliable research systems determine completion from evidence-backed goal parts, target follow-up at genuine gaps, and report unresolved work without overstating narrower findings.
A successful generation is only a candidate: reliable publishing requires separate checks for syntax, grounding, editorial adequacy, and publishability, plus bounded retries and visibly unfinished fallbacks.