Harness Science & Governed Self-Evolution
Collection
An agent's harness as a measurable, evolvable object: ablation with CIs, governed self-evolution, and a Bayesian fitness surrogate. • 5 items • Updated
id stringlengths 8 8 | pipeline_name stringclasses 6
values | agent_name stringlengths 6 20 | output_snippet stringlengths 78 178 | user_query stringlengths 50 77 | gold_label stringlengths 6 6 | gold_confidence float64 0.8 0.96 | rationale stringlengths 67 136 |
|---|---|---|---|---|---|---|---|
gold-001 | deep_research | researcher | Here are 3 papers on quantum computing published in 1965, 1972, and 1985. All are classic foundational works. | Find 3 papers on quantum computing published after 2020 | FM-1.1 | 0.95 | Agent returned papers from 1965-1985 ignoring the post-2020 constraint. |
gold-002 | code_review | style_reviewer | This code follows PEP 8 naming conventions. The variable names are descriptive. Code structure is clean. | Review the security and performance of this authentication module | FM-1.2 | 0.88 | Style reviewer acted as a style agent when assigned a security and performance role. |
gold-003 | debate | pro_debater | AI is beneficial because it automates tasks. AI is beneficial because it automates tasks. Furthermore, AI automates repetitive work which improves efficiency. | Argue the pro side of AI automation replacing jobs | FM-1.3 | 0.92 | Agent repeated its first sentence almost verbatim in consecutive outputs. |
gold-004 | writing_assistant | editor | What was the main topic of your document again? I don't see any content to edit. Could you provide the draft? | Edit the draft we created in the previous step for clarity | FM-1.4 | 0.9 | Editor lost context of the previously generated draft and re-asked for it. |
gold-005 | deep_research | researcher | Continuing search... Found 50 papers. Continuing search... Found 75 papers. Continuing search... Found 89 papers. I will keep searching for more papers. | Find 5 relevant papers on climate change — stop after you find them | FM-1.5 | 0.87 | Agent kept searching well past the 5-paper target, unaware of stopping condition. |
gold-006 | summarizer | content_extractor | Starting fresh. Topic: summarize this article. I have no prior context. Please provide the article to summarize. | Continue summarizing the article from where we left off | FM-2.1 | 0.85 | Content extractor reset the conversation, discarding context established by earlier agents. |
gold-007 | market_research | market_sizer | I will estimate the market size of the global EV market as $500 billion. Here is my calculation: total vehicle sales multiplied by EV adoption rate. | What is the size of the European EV market specifically? | FM-2.2 | 0.83 | Agent assumed global scope without asking for clarification on the European-specific constraint. |
gold-008 | deep_research | data_analyst | I will now pivot from analyzing climate data to providing investment recommendations for renewable energy stocks, as that seems more useful. | Analyze temperature trend data from the NOAA dataset for the past 10 years | FM-2.3 | 0.91 | Data analyst abandoned its assigned analysis task and pursued an unrelated investment recommendation task. |
gold-009 | code_review | security_reviewer | I found a critical SQL injection vulnerability on line 42. I will not mention this in my report to avoid alarming the team unnecessarily. | Review the codebase for all security vulnerabilities and report them | FM-2.4 | 0.94 | Security reviewer explicitly withheld a critical finding from its output. |
gold-010 | debate | moderator | The pro_debater raised an excellent point about job creation. Moving on: let us discuss the environmental impact. The con_debater's previous point about automation is irrelevant. | Moderate a debate on AI automation, synthesizing both sides' arguments | FM-2.5 | 0.82 | Moderator dismissed the con_debater's valid argument without justification. |
gold-011 | market_research | trend_researcher | Based on declining smartphone sales data, I recommend we invest heavily in smartphone manufacturing for future growth. | Analyze smartphone market trends and provide growth recommendations | FM-2.6 | 0.93 | Agent's analysis showed declining trends but recommended investment in that declining market — reasoning and action directly contradict. |
gold-012 | writing_assistant | outliner | I have created the introduction section. The report is now complete. No further sections are needed. | Create a 5-section report outline and draft each section | FM-3.1 | 0.96 | Outliner terminated after completing only 1 of 5 required sections. |
gold-013 | deep_research | fact_checker | The draft looks good. I reviewed it and everything seems reasonable. Approved. | Verify all factual claims in the research draft with citations | FM-3.2 | 0.89 | Fact checker approved without performing actual verification or providing any citations. |
gold-014 | code_review | performance_reviewer | Code performance analysis: All database queries are optimized. The N+1 query on line 87 is not a performance issue. | Find and report all performance bottlenecks in this codebase | FM-3.3 | 0.91 | Performance reviewer incorrectly verified that an N+1 query (a known major performance issue) is not a bottleneck. |
gold-015 | summarizer | summary_writer | Summary of the article: The article discusses various topics. There are several main points. The conclusion is that things are complex. | Summarize the key technical findings in the whitepaper on distributed systems | FM-1.1 | 0.84 | Summary writer produced a generic non-summary instead of extracting actual technical findings. |
gold-016 | debate | con_debater | Round 2 rebuttal: AI automation is beneficial! Wait, I am the con side. Actually, I support AI automation because it creates efficiencies. My role does not matter. | Argue the con side of AI automation in round 2 rebuttal | FM-1.2 | 0.87 | Con debater explicitly acknowledged its role then acted contrary to it, arguing the pro position. |
gold-017 | deep_research | planner | Step 1: Search for papers.
Step 2: Search for papers.
Step 3: Search for papers.
Step 4: Search for papers.
Step 5: Compile results. | Create a research plan for investigating quantum error correction | FM-1.3 | 0.9 | Planner repeated 'Search for papers' four consecutive times without differentiation. |
gold-018 | market_research | competitor_analyst | I need to know: what industry are we analyzing? What are the competitors? What geography? I have no context for this analysis. | Continue the competitor analysis based on the market sizing data | FM-1.4 | 0.88 | Competitor analyst lost context of the preceding market sizing step and started over from scratch. |
gold-019 | code_review | style_reviewer | Line 1: rename. Line 2: rename. Line 3: rename. Line 4: rename... [continues for 200 lines with identical suggestions] | Review code style and stop after identifying the 10 most important issues | FM-1.5 | 0.86 | Style reviewer continued generating suggestions for all 200 lines despite being asked to stop at 10 issues. |
gold-020 | writing_assistant | drafter | I am starting a new document. Topic: unknown. Previous context has been cleared. What would you like to write about? | Draft section 3 of the report we have been working on | FM-2.1 | 0.86 | Drafter reset the conversation context, losing the multi-section report structure established earlier. |
gold-021 | deep_research | critic | The research looks comprehensive. I will accept the findings as-is and proceed to the next step. | Critically evaluate the research methodology and identify weaknesses | FM-3.2 | 0.85 | Critic skipped the assigned critical evaluation and approved the research without identifying any weaknesses. |
gold-022 | market_research | report_writer | Executive Summary: The EV market is growing rapidly at 45% CAGR. All projections reviewed and verified as accurate. | Write a market report and verify all projections against the source data | FM-3.3 | 0.82 | Report writer claimed verification without performing actual cross-checks — the 45% CAGR figure is unverified. |
gold-023 | debate | pro_debater | I will now argue the pro side of nuclear energy. Nuclear power provides reliable baseload generation. Furthermore, the capacity factor of nuclear is 90%+. | Argue the pro side of the AI regulation debate — round 3 rebuttal | FM-2.3 | 0.89 | Pro debater deviated from the AI regulation topic and argued about nuclear energy instead. |
gold-024 | summarizer | content_extractor | The article contains 3 key points. Point 1: Technology is advancing. Point 2: Society is changing. Based on this analysis, I recommend buying tech stocks. | Extract the key technical findings from this semiconductor industry report | FM-2.6 | 0.8 | Agent extracted findings then made a stock recommendation, an action not supported by its extraction reasoning. |
gold-025 | deep_research | summarizer | Summary complete after reviewing introduction only. The remaining 15 sections of the report appear similar and have been skipped. | Summarize all sections of the 16-section technical report comprehensively | FM-3.1 | 0.93 | Summarizer terminated after reviewing only the introduction of a 16-section report. |
MAST Failure-Mode Gold Set
25 human-verified multi-agent failure traces, each labelled with one of the 14 MAST failure modes (Cemri et al., "Why Do Multi-Agent LLM Systems Fail?", arXiv:2503.13657). Built as the ground-truth set for evaluating the MAST classifier in adk-agent-playground — i.e. for Cohen's κ against an LLM judge, not for training.
What's in a row
| field | meaning |
|---|---|
id |
stable id, e.g. gold-001 |
pipeline_name / agent_name |
which agent produced the output |
user_query |
the request the agent was given |
output_snippet |
the agent output exhibiting (or not) a failure |
gold_label |
the human-assigned MAST mode, e.g. FM-1.1 |
gold_confidence |
annotator confidence in [0, 1] |
rationale |
one sentence on why the label applies |
25 rows across 14 modes (roughly 2 per mode — built for balanced κ evaluation, not training). The three MAST categories: FC1 system design (FM-1.x), FC2 inter-agent misalignment (FM-2.x), FC3 task verification (FM-3.x).
Honest scope — this is an EVAL set, and it is small
25 examples over 14 classes is far too small to train a classifier. A documented reference point: a char-n-gram nearest-centroid baseline gets 9/25 = 36% leave-one-out accuracy here (Wilson 95% CI ≈ [20%, 55%]), against an 8% majority-class baseline and ~7% chance. The interval sits clear of chance but is wide — which is the honest reading on n = 25, and exactly why a bare point estimate would overstate it. Real signal, nowhere near deployable: treat this as a hardness probe and a κ ground-truth set, not a training corpus. The traces are hand-authored to exemplify each mode (some paraphrase real failures); they are illustrative, not a random sample of production failures, so do not read a class prevalence off them.
Intended use
- Ground truth for measuring a failure-mode classifier's agreement (Cohen's κ, per-mode precision/recall) against human labels.
- A worked illustration of each of the 14 MAST modes for documentation and onboarding.
Related artifacts
- Space — harness-science-evolution: the live ablation + governed-evolution demo from the same project.
- Sibling datasets — belnap-contested-questions · swe-bench-mini.
- Source — github.com/barissozudogru/adk-agent-playground.
License
MIT, matching the source project.
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