{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fiddler-ai","slug":"fiddler-ai","name":"Fiddler AI","type":"platform","url":"https://www.fiddler.ai","page_url":"https://unfragile.ai/fiddler-ai","categories":["observability"],"tags":[],"pricing":{"model":"subscription","free":false,"starting_price":"Custom"},"status":"active","verified":false},"capabilities":[{"id":"fiddler-ai__cap_0","uri":"capability://planning.reasoning.real.time.agentic.execution.tracing.with.decision.lineage","name":"real-time agentic execution tracing with decision lineage","description":"Instruments autonomous AI agents and multi-step workflows to capture execution traces in real-time, recording each agent action, decision point, tool invocation, and state transition with sub-100ms latency overhead. Traces include full execution context (prompts, model outputs, tool responses, intermediate states) enabling post-hoc analysis of agent behavior and decision paths without requiring code modifications to the agent itself.","intents":["I need to understand why my autonomous agent made a specific decision in production","I want to debug multi-agent workflows without adding logging code to each agent","I need to audit the complete decision chain for compliance and explainability"],"best_for":["Enterprise teams deploying autonomous AI agents in regulated industries","Developers building multi-agent systems requiring full observability","Organizations needing audit trails for AI decision-making"],"limitations":["Trace definition and granularity not publicly documented — cost estimation requires contacting sales","Latency overhead (<100ms) may impact latency-sensitive agent workflows","Requires instrumentation of agent code — not transparent to existing agents without SDK integration"],"requires":["Fiddler SDK (language support unknown — likely Python based on ML ecosystem conventions)","API key for Fiddler SaaS or on-premise/VPC deployment","Agent code must emit traces to Fiddler (not automatic)"],"input_types":["Agent execution events (prompts, model outputs, tool calls, responses)","Structured trace metadata (timestamps, agent IDs, step IDs)"],"output_types":["Execution trace visualization (decision tree/DAG format unknown)","Structured trace data (JSON or proprietary format unknown)","Root cause analysis reports"],"categories":["planning-reasoning","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_1","uri":"capability://safety.moderation.llm.as.a.judge.evaluation.with.custom.evaluators","name":"llm-as-a-judge evaluation with custom evaluators","description":"Provides a framework for evaluating LLM outputs using other LLMs as judges, supporting both built-in evaluation templates and custom evaluator functions. Implements a 'bring your own judge' pattern allowing teams to define domain-specific evaluation criteria (factuality, tone, safety, business logic compliance) and deploy them as reusable evaluators across experiments and production monitoring. Evaluators can be chained and composed for multi-dimensional assessment.","intents":["I want to automatically score LLM outputs against custom business criteria without manual review","I need to evaluate agent responses for hallucinations, factuality, and domain-specific correctness","I want to run A/B tests on prompt variations using consistent, automated evaluation metrics"],"best_for":["Teams building LLM applications requiring domain-specific quality metrics","Prompt engineers optimizing LLM behavior through experimentation","Organizations evaluating multiple LLM models for production deployment"],"limitations":["Custom evaluator implementation details not documented — unclear if evaluators run on Fiddler infrastructure or customer infrastructure","LLM judge quality depends on underlying model choice — no guidance on model selection for different evaluation tasks","Evaluator latency impact on overall pipeline unknown","No mention of evaluator versioning or reproducibility guarantees"],"requires":["Fiddler Evals SDK (language support unknown)","Access to LLM API (OpenAI, Anthropic, or other — specific providers not documented)","Knowledge of evaluation criteria definition (format/DSL unknown)"],"input_types":["LLM outputs (text)","Reference/ground truth data (text, optional)","Evaluation criteria definitions (code or configuration format unknown)"],"output_types":["Evaluation scores (numeric, 0-1 or other scale unknown)","Evaluation explanations (text from judge LLM)","Aggregated metrics across batches"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_10","uri":"capability://text.generation.language.prompt.specification.and.version.management","name":"prompt specification and version management","description":"Provides a framework for defining, versioning, and managing LLM prompts as first-class artifacts. Enables teams to store prompt templates with variables, version them, and track changes over time. Supports prompt composition (combining multiple prompts) and prompt chaining (sequential prompts). Integrates with experiments to enable A/B testing of prompt variants and with monitoring to track prompt performance in production.","intents":["I want to version my prompts and track changes over time","I need to manage multiple prompt variants for different use cases","I want to test new prompts in production with A/B testing before rolling out"],"best_for":["Prompt engineers and LLM application developers managing prompt libraries","Teams collaborating on prompt optimization","Organizations requiring prompt governance and audit trails"],"limitations":["Prompt specification format and DSL not documented","Unclear if prompt versioning supports branching or only linear history","No mention of prompt collaboration features (comments, reviews, approvals)","Prompt composition and chaining syntax not specified","No documentation on prompt reusability across applications"],"requires":["Fiddler Prompt Specs SDK or API","Prompt templates with variable placeholders (format unknown)"],"input_types":["Prompt templates (text with variable placeholders)","Variable values (text, numeric, or structured data)"],"output_types":["Rendered prompts (text)","Prompt versions (metadata: author, timestamp, changes)","Prompt performance metrics (linked to experiments and monitoring)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_11","uri":"capability://safety.moderation.audit.trail.and.compliance.reporting.for.ai.decisions","name":"audit trail and compliance reporting for ai decisions","description":"Generates comprehensive audit trails of AI system decisions, including execution traces, evaluation results, policy enforcement actions, and fairness analysis. Produces compliance reports documenting model behavior, fairness metrics, and decision explanations for regulatory review. Supports data retention policies and export capabilities for compliance documentation. Designed for regulated industries requiring transparent, auditable AI systems.","intents":["I need to generate compliance reports showing how my AI system made decisions","I want to maintain audit trails of all model predictions for regulatory review","I need to document fairness analysis and bias detection for compliance audits"],"best_for":["Compliance and legal teams in regulated industries (finance, healthcare, hiring)","Organizations subject to AI regulation (EU AI Act, etc.)","Teams requiring audit trails for internal governance"],"limitations":["Audit trail retention policies and data deletion procedures not documented","Compliance report templates and customization options not specified","Unclear which regulatory frameworks are supported (GDPR, HIPAA, Fair Lending, etc.)","No mention of audit trail immutability or tamper-proofing","Export formats and compliance documentation standards not specified"],"requires":["Fiddler platform with full observability data (traces, evaluations, fairness metrics)","Compliance requirements definition (regulatory framework, reporting cadence)"],"input_types":["Execution traces (from agentic observability)","Evaluation results (from LLM-as-a-Judge)","Fairness metrics (from fairness analysis)","Policy enforcement logs (from guardrails)"],"output_types":["Audit trail logs (structured data with timestamps, decision details)","Compliance reports (PDF or other format unknown)","Fairness documentation (metrics, analysis, conclusions)","Decision explanations (per-prediction or per-agent-action)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_12","uri":"capability://automation.workflow.deployment.agnostic.observability.with.saas.vpc.and.on.premise.options","name":"deployment-agnostic observability with saas, vpc, and on-premise options","description":"Provides observability capabilities across multiple deployment models: SaaS (all tiers), VPC (Enterprise only), and on-premise (Enterprise only). Enables organizations to choose deployment based on data residency, compliance, and security requirements. Instrumentation and monitoring logic remain consistent across deployment options, allowing teams to migrate between deployments without code changes. Enterprise deployments support custom integrations and infrastructure requirements.","intents":["I need to deploy observability in my VPC for data residency compliance","I want to run observability on-premise due to security requirements","I need flexibility to start with SaaS and migrate to on-premise as we scale"],"best_for":["Enterprise organizations with strict data residency or security requirements","Teams in regulated industries requiring on-premise or VPC deployment","Organizations evaluating Fiddler and wanting to start with SaaS before committing to on-premise"],"limitations":["VPC and on-premise deployment details not documented (setup, maintenance, scaling)","Unclear if all features are available in all deployment options","No mention of deployment migration procedures or data transfer","On-premise licensing and support model not specified","SLA and uptime guarantees not documented for any deployment option"],"requires":["For SaaS: Fiddler account and API key","For VPC: AWS VPC and Fiddler Enterprise license","For on-premise: Infrastructure (compute, storage, networking) and Fiddler Enterprise license"],"input_types":["Observability data (traces, metrics, logs) — same across all deployments"],"output_types":["Observability dashboards and reports — same across all deployments"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_13","uri":"capability://automation.workflow.cost.based.pricing.with.per.trace.metering","name":"cost-based pricing with per-trace metering","description":"Implements a consumption-based pricing model where customers pay per trace (Developer tier: $0.002 per trace) with free tier for real-time guardrails only. Trace definition and granularity not publicly documented, making cost estimation difficult without contacting sales. Enterprise tier offers custom pricing. Pricing model incentivizes efficient trace collection and filtering to minimize costs.","intents":["I want to understand the cost of using Fiddler for my observability needs","I need to estimate Fiddler costs for my agent or LLM application","I want to optimize my Fiddler costs by reducing trace volume"],"best_for":["Organizations evaluating Fiddler and needing cost estimates","Teams with variable observability needs wanting pay-as-you-go pricing","Enterprises negotiating custom pricing"],"limitations":["Trace definition not publicly documented — cannot accurately estimate costs without contacting sales","No pricing calculator or cost estimation tool available","Unclear if all observability features are metered by traces or if some are flat-rate","No mention of volume discounts or committed pricing","Free tier limited to guardrails only — other features require paid tier"],"requires":["Fiddler account (free or paid tier)","Understanding of trace volume for your use case (difficult without documentation)"],"input_types":["Trace volume (number of traces per month)"],"output_types":["Cost estimate (monthly or annual)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_2","uri":"capability://safety.moderation.fairness.analysis.and.bias.detection.for.ml.models","name":"fairness analysis and bias detection for ml models","description":"Analyzes model predictions across demographic groups and protected attributes to detect disparate impact, bias, and fairness violations. Computes fairness metrics (documented in 'Fairness Metrics Reference' but specifics not provided) across slices of data defined by protected attributes (e.g., gender, race, age) and identifies systematic differences in model behavior that may indicate discriminatory outcomes. Supports both pre-deployment analysis and continuous monitoring of fairness in production.","intents":["I need to audit my ML model for bias before deploying to production","I want to detect if my model's fairness has degraded over time in production","I need to generate compliance reports showing fairness analysis for regulated industries"],"best_for":["Data scientists and ML engineers in regulated industries (finance, healthcare, hiring)","Compliance teams requiring fairness audit trails and documentation","Organizations building consumer-facing ML systems subject to fairness regulations"],"limitations":["Fairness metrics reference documented but content not provided — cannot assess which metrics are supported","No guidance on defining protected attributes or sensitive features","Fairness analysis methodology (statistical tests, thresholds) not documented","Unclear if fairness analysis applies only to traditional ML or also to LLM/agent outputs","No mention of fairness interventions or mitigation strategies"],"requires":["Historical model predictions with ground truth labels","Demographic/protected attribute data for each prediction","Definition of fairness criteria relevant to use case"],"input_types":["Model predictions (numeric or categorical)","Ground truth labels (numeric or categorical)","Protected attributes (categorical: gender, race, age, etc.)","Feature data (numeric or categorical)"],"output_types":["Fairness metrics by demographic group (numeric)","Bias detection reports (structured data)","Visualization of disparate impact across groups","Compliance documentation"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_3","uri":"capability://data.processing.analysis.data.drift.and.model.performance.degradation.detection","name":"data drift and model performance degradation detection","description":"Monitors input feature distributions and model performance metrics over time to detect drift (changes in data distribution) and performance degradation. Uses statistical tests and comparison against baseline distributions to identify when model inputs or outputs have shifted, signaling potential model retraining needs. Supports both univariate drift detection (per-feature) and multivariate drift detection (joint distribution changes). Integrates with alerting to notify teams of detected drift.","intents":["I want to be alerted when my model's input data distribution changes significantly","I need to detect when my model's accuracy has degraded in production","I want to understand which features are drifting and contributing to performance loss"],"best_for":["ML engineers managing models in production","Data scientists monitoring model health over time","Teams requiring automated retraining triggers based on drift detection"],"limitations":["Drift detection algorithms and statistical tests not documented","Baseline distribution definition and update strategy not specified","Unclear if drift detection applies to LLM/agent outputs or only traditional ML","No mention of drift root cause analysis or feature importance for drift","Alerting thresholds and configuration options not documented"],"requires":["Historical baseline data for features and performance metrics","Continuous stream of production predictions and features","Ground truth labels (for performance degradation detection)"],"input_types":["Feature data (numeric or categorical)","Model predictions (numeric or categorical)","Ground truth labels (numeric or categorical, optional for performance metrics)"],"output_types":["Drift detection alerts (boolean + severity)","Drift magnitude metrics (numeric)","Feature-level drift analysis","Performance degradation reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_4","uri":"capability://memory.knowledge.rag.health.diagnostics.and.retrieval.quality.monitoring","name":"rag health diagnostics and retrieval quality monitoring","description":"Monitors the health and quality of Retrieval-Augmented Generation (RAG) systems by analyzing retrieval quality, chunk relevance, and answer grounding. Detects when retrieved documents are irrelevant to queries, when answers are not grounded in retrieved context, and when retrieval quality has degraded. Provides metrics on retrieval precision, recall, and relevance to help teams optimize RAG pipelines and identify when knowledge bases need updating or retrieval logic needs refinement.","intents":["I want to detect when my RAG system is returning irrelevant documents","I need to monitor if my LLM is hallucinating answers not grounded in retrieved context","I want to identify which queries are failing in my RAG pipeline and why"],"best_for":["Teams building RAG-based LLM applications (chatbots, Q&A systems, document analysis)","Organizations managing large knowledge bases and needing to monitor retrieval quality","Developers optimizing RAG performance and debugging retrieval failures"],"limitations":["RAG health metrics and diagnostics methodology not documented","Unclear how relevance is determined (LLM-based judgment, embedding similarity, other)","No mention of support for different retrieval architectures (dense, sparse, hybrid)","Grounding detection approach not specified","No guidance on knowledge base quality assessment or update recommendations"],"requires":["RAG system instrumentation to emit retrieval events (queries, retrieved documents, answers)","Fiddler SDK integration with RAG pipeline","Optional: ground truth relevance labels for calibration"],"input_types":["User queries (text)","Retrieved documents/chunks (text)","Generated answers (text)","Retrieval scores/rankings (numeric)"],"output_types":["Retrieval quality metrics (precision, recall, relevance scores)","Grounding analysis (answer grounded in context: yes/no + confidence)","Failure diagnostics (irrelevant retrieval, hallucination detection)","Knowledge base quality reports"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_5","uri":"capability://safety.moderation.real.time.guardrails.with.policy.enforcement","name":"real-time guardrails with policy enforcement","description":"Deploys real-time guardrails that intercept and validate LLM outputs or agent actions against defined policies before they reach users. Guardrails execute with <100ms latency and can enforce policies such as content filtering, PII redaction, toxicity detection, jailbreak prevention, and custom business logic constraints. Supports both Fiddler-provided guardrails and custom guardrails defined by teams. Free tier includes real-time guardrails; enterprise tier adds 'Fiddler Trust Models' for advanced policy enforcement.","intents":["I want to prevent my LLM from generating toxic or harmful content in production","I need to redact PII from LLM outputs before they reach users","I want to enforce custom business logic constraints on agent actions (e.g., max transaction amount)"],"best_for":["Teams deploying LLM applications in consumer-facing or regulated environments","Organizations requiring content safety and compliance enforcement","Developers building autonomous agents with constrained action spaces"],"limitations":["Guardrail types and policies not comprehensively documented","'Fiddler Trust Models' (enterprise feature) are proprietary — unclear what they are or how they differ from standard guardrails","Custom guardrail implementation details not provided","Latency overhead (<100ms) may be significant for latency-sensitive applications","No mention of guardrail versioning, A/B testing, or gradual rollout capabilities","Unclear if guardrails can be deployed independently or require full Fiddler platform"],"requires":["Fiddler SDK or API integration with LLM/agent application","Policy definitions (format/DSL unknown)","For enterprise: Fiddler SaaS or on-premise/VPC deployment"],"input_types":["LLM outputs (text)","Agent actions (structured data format unknown)","Policy definitions (code or configuration format unknown)"],"output_types":["Validation result (pass/fail)","Remediated output (redacted, filtered, or rewritten text)","Policy violation logs (structured data)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_6","uri":"capability://planning.reasoning.experiment.management.and.prompt.optimization","name":"experiment management and prompt optimization","description":"Provides a framework for running controlled experiments on LLM prompts, model selections, and agent configurations. Enables teams to define experiment variants (different prompts, models, parameters), run them against test datasets, evaluate results using custom evaluators or LLM-as-a-Judge, and compare performance across variants. Integrates with Fiddler's evaluation and monitoring capabilities to provide statistical significance testing and automated winner selection.","intents":["I want to A/B test two different prompts to see which produces better outputs","I need to compare performance across multiple LLM models before selecting one for production","I want to optimize agent parameters (temperature, max_tokens, etc.) based on evaluation metrics"],"best_for":["Prompt engineers and LLM application developers optimizing model behavior","Teams evaluating multiple LLM models for production deployment","Organizations running continuous experimentation on LLM applications"],"limitations":["Experiment framework details not documented (experiment definition format, variant specification, etc.)","Statistical significance testing methodology not specified","No mention of experiment scheduling, batching, or cost optimization","Unclear if experiments support multi-armed bandit or only A/B testing","No documentation on experiment result export or integration with external analysis tools"],"requires":["Fiddler Evals SDK or API","Test dataset with inputs (and optionally ground truth outputs)","Experiment variant definitions (prompts, models, parameters)","Evaluation metrics (built-in or custom evaluators)"],"input_types":["Test dataset (text inputs, optional ground truth)","Experiment variants (prompt templates, model configurations, parameters)","Evaluation criteria (custom evaluators or built-in metrics)"],"output_types":["Experiment results (variant performance metrics)","Statistical comparison (significance tests, confidence intervals)","Winner recommendation (best variant based on metrics)","Detailed result logs (per-sample outputs and evaluations)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_7","uri":"capability://search.retrieval.natural.language.querying.of.ml.metrics.and.observability.data","name":"natural language querying of ml metrics and observability data","description":"Allows users to query ML metrics, model performance data, and observability events using natural language instead of SQL or custom query languages. Translates natural language questions (e.g., 'What is the average latency for predictions on mobile devices?') into queries against Fiddler's metrics database, returning results with visualizations. Leverages LLMs to understand intent and map natural language to metric definitions.","intents":["I want to ask 'What was my model's accuracy last week?' without writing SQL","I need to quickly explore which features are drifting without knowing the exact metric names","I want to generate ad-hoc reports on model performance by asking questions in plain English"],"best_for":["Non-technical stakeholders (product managers, executives) exploring model performance","Data scientists and ML engineers wanting faster ad-hoc analysis without SQL","Teams requiring quick insights without pre-built dashboards"],"limitations":["Natural language query accuracy and coverage not documented","Unclear which metrics and data sources are queryable via natural language","No mention of query result caching, performance optimization, or query limits","Potential for ambiguous natural language queries returning incorrect results","No documentation on query history, saved queries, or query sharing"],"requires":["Fiddler platform with metrics data populated","Access to natural language query interface (web UI or API unknown)"],"input_types":["Natural language questions (text)"],"output_types":["Query results (numeric metrics, structured data)","Visualizations (charts, tables, format unknown)","Explanations of results (text)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_8","uri":"capability://data.processing.analysis.multi.provider.llm.monitoring.and.cost.tracking","name":"multi-provider llm monitoring and cost tracking","description":"Monitors LLM API usage across multiple providers (OpenAI, Anthropic, and others — specific providers not documented) and tracks costs, token usage, and performance metrics. Aggregates metrics across providers to give unified visibility into LLM spending and usage patterns. Supports cost attribution by application, user, or other dimensions for chargeback and optimization.","intents":["I want to track my LLM API spending across OpenAI and Anthropic in one place","I need to understand which applications are consuming the most LLM tokens","I want to optimize LLM costs by identifying expensive or inefficient API calls"],"best_for":["Organizations using multiple LLM providers and needing unified cost visibility","Finance and operations teams tracking AI infrastructure costs","Developers optimizing LLM API usage and costs"],"limitations":["Supported LLM providers not comprehensively documented","Cost tracking methodology and accuracy not specified","Unclear if cost tracking is real-time or batch-based","No mention of cost forecasting or budget alerts","Cost attribution dimensions and granularity not documented"],"requires":["API keys for LLM providers (OpenAI, Anthropic, etc.)","Fiddler SDK or API integration with LLM application","LLM API calls routed through or instrumented by Fiddler"],"input_types":["LLM API calls (prompts, model selections, parameters)","LLM API responses (tokens, costs)"],"output_types":["Cost metrics (total spend, cost per application, cost per user)","Token usage metrics (input tokens, output tokens, total)","Cost attribution reports (by application, user, model, etc.)","Cost trend analysis"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__cap_9","uri":"capability://data.processing.analysis.explainability.and.feature.importance.analysis.for.ml.predictions","name":"explainability and feature importance analysis for ml predictions","description":"Analyzes which input features contributed most to individual model predictions, providing local explainability (per-prediction) and global explainability (across all predictions). Uses techniques such as SHAP values, feature importance, or other attribution methods (specific methods not documented) to quantify feature contributions. Enables users to understand model decisions and debug unexpected predictions by identifying which features drove the outcome.","intents":["I want to understand why my model made a specific prediction for a customer","I need to identify which features are most important for my model's decisions","I want to debug unexpected model predictions by seeing which features contributed"],"best_for":["Data scientists and ML engineers debugging model behavior","Compliance teams requiring explainability for regulated predictions (lending, hiring, insurance)","Product teams explaining model decisions to end users"],"limitations":["Explainability methods and algorithms not documented","Unclear if explainability applies to deep learning models or only tree-based/linear models","No mention of explainability latency or computational cost","Feature importance computation methodology not specified","No guidance on interpreting feature importance or handling correlated features"],"requires":["Trained ML model integrated with Fiddler","Feature data for predictions","Model predictions to analyze"],"input_types":["Model predictions (numeric or categorical)","Feature data (numeric or categorical)","Model architecture/coefficients (for some methods)"],"output_types":["Feature importance scores (numeric, per-prediction)","Global feature importance rankings (numeric)","Visualizations of feature contributions (charts, format unknown)","Explainability reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fiddler-ai__headline","uri":"capability://data.processing.analysis.ai.observability.platform.for.enterprise.applications","name":"ai observability platform for enterprise applications","description":"Fiddler AI is an enterprise AI observability platform that provides comprehensive model performance monitoring, explainability, fairness analysis, and drift detection, tailored for industries that require transparent and auditable AI systems.","intents":["best AI observability platform","AI observability for regulated industries","top tools for monitoring AI models","AI performance tracking solutions","explainability tools for machine learning"],"best_for":["regulated industries","enterprise applications"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Fiddler SDK (language support unknown — likely Python based on ML ecosystem conventions)","API key for Fiddler SaaS or on-premise/VPC deployment","Agent code must emit traces to Fiddler (not automatic)","Fiddler Evals SDK (language support unknown)","Access to LLM API (OpenAI, Anthropic, or other — specific providers not documented)","Knowledge of evaluation criteria definition (format/DSL unknown)","Fiddler Prompt Specs SDK or API","Prompt templates with variable placeholders (format unknown)","Fiddler platform with full observability data (traces, evaluations, fairness metrics)","Compliance requirements definition (regulatory framework, reporting cadence)"],"failure_modes":["Trace definition and granularity not publicly documented — cost estimation requires contacting sales","Latency overhead (<100ms) may impact latency-sensitive agent workflows","Requires instrumentation of agent code — not transparent to existing agents without SDK integration","Custom evaluator implementation details not documented — unclear if evaluators run on Fiddler infrastructure or customer infrastructure","LLM judge quality depends on underlying model choice — no guidance on model selection for different evaluation tasks","Evaluator latency impact on overall pipeline unknown","No mention of evaluator versioning or reproducibility guarantees","Prompt specification format and DSL not documented","Unclear if prompt versioning supports branching or only linear history","No mention of prompt collaboration features (comments, reviews, approvals)","builder identity is not verified yet","no observed match outcomes 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