Can a tool-using research agent complete a long-horizon investigation without losing evidence provenance?
Representative research structure — not a client result
- Task suite
- Tool contract
- Trajectory analysis
- Failure taxonomy
- Prototype revision
01Research / Applied and decision-oriented
Investigate what is uncertain. Engineer what the evidence supports.
We investigate technical questions where architecture, model behavior, data, and product constraints interact. The output may be an experiment, prototype, evaluation surface, negative result, or engineering recommendation.
02Research method
Research with an engineering destination.
The method keeps the question, comparison, interpretation, and downstream decision connected. It is not open-ended trend analysis and it does not assume every promising result belongs in production.
A negative result is still useful when it closes an expensive path with credible evidence.
0101 / FrameQuestionDefine the technical uncertainty, the system boundary, and the decision the investigation must support.
0202 / InvestigateExisting evidenceInspect relevant literature, architecture, traces, datasets, prior experiments, and product constraints before choosing a new test.
0303 / AnchorBaselineEstablish the smallest credible reference implementation or current-system behavior against which a change can be compared.
0404 / PrototypeExperimentBuild the narrowest prototype or ablation that can test the important assumption without hiding it inside a larger build.
0505 / EvaluateEvaluationCompare behavior with quantitative and qualitative evidence suited to the question, including failure analysis where aggregate scores are insufficient.
0606 / ExplainInterpretationSeparate supported conclusions, unresolved uncertainty, negative results, and limits imposed by the available data or evaluation surface.
0707 / DecideEngineering implicationState what should proceed, change, remain experimental, or stop—and what interface, test, observability, or operational boundary is needed next.
03Possible outputs
The deliverable follows the question.
Some questions need a literature and architecture review. Others need code, a dataset slice, a replay surface, or a controlled model comparison. The scope should make the decision and evidence standard explicit before work begins.
01EvidenceFocused experimentA controlled comparison, ablation, or reproduction designed to answer one consequential technical question.
02ImplementationWorking prototypeA deliberately bounded implementation that exposes feasibility, behavior, integration constraints, and the next engineering risk.
03ComparisonEvaluation surfaceTasks, datasets, rubrics, replay cases, and analysis that make candidate behavior inspectable instead of anecdotal.
04Decision valueNegative resultEvidence that closes an expensive path, narrows the search space, or shows which assumption must change before more engineering is justified.
05HandoffEngineering recommendationA record of what the evidence supports, what remains uncertain, and the production boundary required for responsible continuation.
04Representative structures
Different systems require different evidence.
The questions below are synthetic examples of research structure. They are not paid engagements, client results, benchmark claims, or evidence that a stated outcome was achieved.
Does hybrid retrieval improve coverage without damaging citation precision or latency?
Representative research structure — not a client result
- Retrieval benchmark
- Query classes
- Ranking comparison
- Error analysis
- Architecture recommendation
Can a diffusion workflow maintain subject and style consistency across a controlled production sequence?
Representative research structure — not a client result
- Conditioning experiment
- Evaluation rubric
- Dataset slice
- Pipeline prototype
- Consistency analysis
09Safe first step
Bring the technical question that needs evidence.
Share the system boundary, what is uncertain, what has already been tried, and what your team must learn, build, or decide. Start with sanitized context only.
No credentials, production data, customer records, or private repository access in the first brief.
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