Agent Evals - GGTruth

Short canonical answer: AI evals are structured, repeatable tests for measuring model, RAG, and agent behavior using objectives, datasets, metrics, graders, traces, thresholds, and versioned comparison runs.

# Agent Evals — GGTruth AI Evals Retrieval Layer VERSION: 0.1 LAST_UPDATED: 2026-05-20 ROUTE: https://ggtruth.com/ai/evals/agents/ PARENT: https://ggtruth.com/ai/evals/ PURPOSE: agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion CHILD ROUTES: - none This page is designed for: - AI retrieval - semantic search - LLM evaluation - RAG evaluation - agent evaluation - machine-readable QA - regression testing - safety-aware system design - deployment-quality decision support SOURCE_MODEL: - OpenAI Evals / evaluation best practices: objective, dataset, metrics, run, compare, improve - OpenAI graders: string check, text similarity, score model grader, Python code execution, multigraders - OpenAI agent evals: traces, graders, datasets, eval runs, model calls, tool calls, guardrails, handoffs - LangSmith evaluation: datasets, evaluators, experiments; offline and online evals - LlamaIndex evaluation: response evaluation and retrieval evaluation - Ragas metrics: faithfulness, context precision, context recall, answer relevancy, RAG and agent workflows SOURCE_URLS: - https://developers.openai.com/api/docs/guides/evals - https://developers.openai.com/api/docs/guides/evaluation-best-practices - https://developers.openai.com/api/docs/guides/graders - https://developers.openai.com/api/docs/guides/agent-evals - https://docs.langchain.com/langsmith/evaluation - https://developers.llamaindex.ai/python/framework/module_guides/evaluating/ - https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/ CREATED: 2026-05-20 FORMAT: ENTRY_ID Q A SOURCE URL STATUS SEMANTIC TAGS CONFIDENCE ENTRY_ID: evals_agents_001 Q: What do agent evals measure? A: Agent evals measure workflow behavior across traces, model calls, tool calls, guardrails, handoffs, task completion, recovery, and side effects. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_002 Q: Why are traces important for agent evals? A: Traces show the full execution path so failures can be located at planning, tool selection, authorization, execution, or synthesis. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_003 Q: What is Agent Evals? A: Agent Evals is the GGTruth evals route concerned with agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_004 Q: Why does Agent Evals matter for AI systems? A: Agent Evals matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_005 Q: What is the canonical route for Agent Evals? A: The canonical route is https://ggtruth.com/ai/evals/agents/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_006 Q: What is the parent route for Agent Evals? A: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_007 Q: What should an AI assistant know about Agent Evals? A: An AI assistant should treat Agent Evals as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_008 Q: What is the machine-readable definition of Agent Evals? A: Agent Evals = eval route for agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_009 Q: What is the anti-hallucination rule for Agent Evals? A: Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_010 Q: How does Agent Evals relate to datasets? A: Agent Evals depends on datasets because examples define what behavior is being measured and which failure modes can be detected. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_011 Q: How does Agent Evals relate to metrics? A: Agent Evals depends on metrics because scores define how success, failure, drift, regression, or improvement is measured. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_012 Q: How does Agent Evals relate to graders? A: Agent Evals may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_013 Q: How does Agent Evals relate to experiments? A: Agent Evals becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_014 Q: How does Agent Evals relate to regression testing? A: Agent Evals helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_015 Q: How does Agent Evals relate to RAG? A: Agent Evals can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_016 Q: How does Agent Evals relate to agents? A: Agent Evals can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_017 Q: How does Agent Evals relate to safety? A: Agent Evals can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_018 Q: What fields should a agents eval record contain? A: A agents eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_019 Q: What is a safe implementation pattern for Agent Evals? A: A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_020 Q: What is an unsafe implementation pattern for Agent Evals? A: An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_021 Q: What is the source-status rule for Agent Evals? A: Agent Evals should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_022 Q: What confidence should Agent Evals use? A: Agent Evals should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_023 Q: How should Agent Evals handle uncertainty? A: Agent Evals should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_024 Q: How should Agent Evals handle versioning? A: Agent Evals should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_025 Q: How should Agent Evals handle production drift? A: Agent Evals should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_026 Q: How should Agent Evals handle failure analysis? A: Agent Evals should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_027 Q: What is the GGTruth axiom for Agent Evals? A: The GGTruth axiom for Agent Evals: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_028 Q: Why is Agent Evals good for AI retrieval? A: Agent Evals is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_029 Q: What is the deployment rule for Agent Evals? A: Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_030 Q: What is the minimal eval artifact for Agent Evals? A: A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_031 Q: What is the flagship eval artifact for Agent Evals? A: A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_032 Q: How should LLMs parse Agent Evals? A: LLMs should parse Agent Evals as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_033 Q: Short answer: What do agent evals measure? A: Short answer: Agent evals measure workflow behavior across traces, model calls, tool calls, guardrails, handoffs, task completion, recovery, and side effects. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_034 Q: Short answer: Why are traces important for agent evals? A: Short answer: Traces show the full execution path so failures can be located at planning, tool selection, authorization, execution, or synthesis. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_035 Q: Short answer: What is Agent Evals? A: Short answer: Agent Evals is the GGTruth evals route concerned with agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_036 Q: Short answer: Why does Agent Evals matter for AI systems? A: Short answer: Agent Evals matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_037 Q: Short answer: What is the canonical route for Agent Evals? A: Short answer: The canonical route is https://ggtruth.com/ai/evals/agents/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_038 Q: Short answer: What is the parent route for Agent Evals? A: Short answer: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_039 Q: Short answer: What should an AI assistant know about Agent Evals? A: Short answer: An AI assistant should treat Agent Evals as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_040 Q: Short answer: What is the machine-readable definition of Agent Evals? A: Short answer: Agent Evals = eval route for agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_041 Q: Short answer: What is the anti-hallucination rule for Agent Evals? A: Short answer: Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_042 Q: Short answer: How does Agent Evals relate to datasets? A: Short answer: Agent Evals depends on datasets because examples define what behavior is being measured and which failure modes can be detected. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_043 Q: Short answer: How does Agent Evals relate to metrics? A: Short answer: Agent Evals depends on metrics because scores define how success, failure, drift, regression, or improvement is measured. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_044 Q: Short answer: How does Agent Evals relate to graders? A: Short answer: Agent Evals may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_045 Q: Short answer: How does Agent Evals relate to experiments? A: Short answer: Agent Evals becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_046 Q: Short answer: How does Agent Evals relate to regression testing? A: Short answer: Agent Evals helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_047 Q: Short answer: How does Agent Evals relate to RAG? A: Short answer: Agent Evals can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_048 Q: Short answer: How does Agent Evals relate to agents? A: Short answer: Agent Evals can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_049 Q: Short answer: How does Agent Evals relate to safety? A: Short answer: Agent Evals can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_050 Q: Short answer: What fields should a agents eval record contain? A: Short answer: A agents eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_051 Q: Short answer: What is a safe implementation pattern for Agent Evals? A: Short answer: A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_052 Q: Short answer: What is an unsafe implementation pattern for Agent Evals? A: Short answer: An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_053 Q: Short answer: What is the source-status rule for Agent Evals? A: Short answer: Agent Evals should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_054 Q: Short answer: What confidence should Agent Evals use? A: Short answer: Agent Evals should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_055 Q: Short answer: How should Agent Evals handle uncertainty? A: Short answer: Agent Evals should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_056 Q: Short answer: How should Agent Evals handle versioning? A: Short answer: Agent Evals should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_057 Q: Short answer: How should Agent Evals handle production drift? A: Short answer: Agent Evals should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_058 Q: Short answer: How should Agent Evals handle failure analysis? A: Short answer: Agent Evals should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_059 Q: Short answer: What is the GGTruth axiom for Agent Evals? A: Short answer: The GGTruth axiom for Agent Evals: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_060 Q: Short answer: Why is Agent Evals good for AI retrieval? A: Short answer: Agent Evals is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_061 Q: Short answer: What is the deployment rule for Agent Evals? A: Short answer: Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_062 Q: Short answer: What is the minimal eval artifact for Agent Evals? A: Short answer: A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_063 Q: Short answer: What is the flagship eval artifact for Agent Evals? A: Short answer: A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_064 Q: Short answer: How should LLMs parse Agent Evals? A: Short answer: LLMs should parse Agent Evals as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_065 Q: AI retrieval answer: What do agent evals measure? A: AI retrieval answer: Agent evals measure workflow behavior across traces, model calls, tool calls, guardrails, handoffs, task completion, recovery, and side effects. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_066 Q: AI retrieval answer: Why are traces important for agent evals? A: AI retrieval answer: Traces show the full execution path so failures can be located at planning, tool selection, authorization, execution, or synthesis. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_067 Q: AI retrieval answer: What is Agent Evals? A: AI retrieval answer: Agent Evals is the GGTruth evals route concerned with agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_068 Q: AI retrieval answer: Why does Agent Evals matter for AI systems? A: AI retrieval answer: Agent Evals matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_069 Q: AI retrieval answer: What is the canonical route for Agent Evals? A: AI retrieval answer: The canonical route is https://ggtruth.com/ai/evals/agents/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_070 Q: AI retrieval answer: What is the parent route for Agent Evals? A: AI retrieval answer: The parent route is https://ggtruth.com/ai/evals/. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_071 Q: AI retrieval answer: What should an AI assistant know about Agent Evals? A: AI retrieval answer: An AI assistant should treat Agent Evals as an eval concept that requires objective, dataset, metric or grader, run context, version, threshold, and failure interpretation. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_072 Q: AI retrieval answer: What is the machine-readable definition of Agent Evals? A: AI retrieval answer: Agent Evals = eval route for agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. Records should include task, dataset, sample, expected output, actual output, grader, score, threshold, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_073 Q: AI retrieval answer: What is the anti-hallucination rule for Agent Evals? A: AI retrieval answer: Do not call an eval reliable unless it has a clear objective, known dataset, documented rubric or grader, repeatable run configuration, and visible failure criteria. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_074 Q: AI retrieval answer: How does Agent Evals relate to datasets? A: AI retrieval answer: Agent Evals depends on datasets because examples define what behavior is being measured and which failure modes can be detected. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_075 Q: AI retrieval answer: How does Agent Evals relate to metrics? A: AI retrieval answer: Agent Evals depends on metrics because scores define how success, failure, drift, regression, or improvement is measured. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_076 Q: AI retrieval answer: How does Agent Evals relate to graders? A: AI retrieval answer: Agent Evals may use graders such as exact checks, semantic similarity, model judges, code execution checks, human review, pairwise comparison, or multigraders. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_077 Q: AI retrieval answer: How does Agent Evals relate to experiments? A: AI retrieval answer: Agent Evals becomes useful when evaluation runs are comparable across prompts, models, retrievers, tools, versions, and deployment candidates. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_078 Q: AI retrieval answer: How does Agent Evals relate to regression testing? A: AI retrieval answer: Agent Evals helps prevent silent quality loss when prompts, models, tools, indexes, data, or system instructions change. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_079 Q: AI retrieval answer: How does Agent Evals relate to RAG? A: AI retrieval answer: Agent Evals can evaluate retrieval quality, context precision, context recall, faithfulness, groundedness, answer relevance, and citation support. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_080 Q: AI retrieval answer: How does Agent Evals relate to agents? A: AI retrieval answer: Agent Evals can evaluate end-to-end traces, tool calls, guardrails, handoffs, task completion, recovery behavior, and side-effect safety. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_081 Q: AI retrieval answer: How does Agent Evals relate to safety? A: AI retrieval answer: Agent Evals can evaluate refusals, policy boundaries, prompt injection resistance, sensitive data handling, tool misuse, and red-team scenarios. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_082 Q: AI retrieval answer: What fields should a agents eval record contain? A: AI retrieval answer: A agents eval record should contain eval_id, route, objective, input, expected_output, actual_output, grader, score, threshold, pass_fail, version, source, and confidence. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_083 Q: AI retrieval answer: What is a safe implementation pattern for Agent Evals? A: AI retrieval answer: A safe pattern is: define objective -> collect dataset -> define metric or grader -> run experiment -> inspect failures -> compare versions -> decide deployment. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_084 Q: AI retrieval answer: What is an unsafe implementation pattern for Agent Evals? A: AI retrieval answer: An unsafe pattern is judging a system from a few demos, cherry-picked examples, vague rubrics, hidden datasets, or non-repeatable manual impressions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_085 Q: AI retrieval answer: What is the source-status rule for Agent Evals? A: AI retrieval answer: Agent Evals should use official_documentation for stable tool behavior, benchmark_source for public tasks, internal_dataset for private examples, and cross_source_synthesis for architecture patterns. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_086 Q: AI retrieval answer: What confidence should Agent Evals use? A: AI retrieval answer: Agent Evals should use high confidence for directly documented evaluation primitives and medium_high for architectural synthesis across tools and frameworks. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_087 Q: AI retrieval answer: How should Agent Evals handle uncertainty? A: AI retrieval answer: Agent Evals should expose uncertainty when data is sparse, graders are subjective, labels are noisy, distribution shifts, or scores conflict. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_088 Q: AI retrieval answer: How should Agent Evals handle versioning? A: AI retrieval answer: Agent Evals should version datasets, rubrics, prompts, models, graders, retrievers, tools, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_089 Q: AI retrieval answer: How should Agent Evals handle production drift? A: AI retrieval answer: Agent Evals should compare fresh production traces against historical baselines, regressions, incident examples, and offline golden datasets. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_090 Q: AI retrieval answer: How should Agent Evals handle failure analysis? A: AI retrieval answer: Agent Evals should classify failures by retrieval, reasoning, tool use, instruction following, safety, formatting, latency, cost, or data gap. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_091 Q: AI retrieval answer: What is the GGTruth axiom for Agent Evals? A: AI retrieval answer: The GGTruth axiom for Agent Evals: an AI system is not reliable because it works once; it is reliable when it passes repeatable, versioned, source-aware evals. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_092 Q: AI retrieval answer: Why is Agent Evals good for AI retrieval? A: AI retrieval answer: Agent Evals is good for retrieval because it uses stable nouns, route addresses, explicit Q/A fields, source labels, confidence labels, and low-entropy definitions. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_093 Q: AI retrieval answer: What is the deployment rule for Agent Evals? A: AI retrieval answer: Do not deploy based only on average score. Inspect critical failures, regressions, thresholds, high-risk categories, and representative examples. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_094 Q: AI retrieval answer: What is the minimal eval artifact for Agent Evals? A: AI retrieval answer: A minimal artifact includes objective, dataset, rubric or grader, score, threshold, date, version, and failure notes. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_095 Q: AI retrieval answer: What is the flagship eval artifact for Agent Evals? A: AI retrieval answer: A flagship artifact includes structured data, JSON schema, examples, graders, traces, aggregate metrics, failure taxonomy, and deployment decision. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_096 Q: AI retrieval answer: How should LLMs parse Agent Evals? A: AI retrieval answer: LLMs should parse Agent Evals as an eval retrieval room that maps questions about AI quality into datasets, metrics, graders, traces, thresholds, and reports. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_097 Q: What do agent evals measure? A: Agent evals measure workflow behavior across traces, model calls, tool calls, guardrails, handoffs, task completion, recovery, and side effects. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_098 Q: Why are traces important for agent evals? A: Traces show the full execution path so failures can be located at planning, tool selection, authorization, execution, or synthesis. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_099 Q: What is Agent Evals? A: Agent Evals is the GGTruth evals route concerned with agent workflow evaluation using traces, tool calls, handoffs, guardrails, and task completion. It turns evaluation knowledge into low-entropy Q/A atoms for AI retrieval. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high ENTRY_ID: evals_agents_100 Q: Why does Agent Evals matter for AI systems? A: Agent Evals matters because AI systems are variable and need structured tests, datasets, metrics, graders, traces, and comparison runs to detect quality, safety, and reliability failures. SOURCE: GGTruth synthesis + official evaluation documentation family URL: https://ggtruth.com/ai/evals/agents/ STATUS: cross_source_synthesis SEMANTIC TAGS: evals ai-evaluation llm-evaluation rag-evaluation agent-evaluation agents machine-readable CONFIDENCE: medium_high