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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAI OBSERVABILITY DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAI observability is the practice of instrumenting, tracing and evaluating AI systems so teams can understand how inputs, data, prompts, models, tools and application logic shape each output in production.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-3af76f3437","text":"\u003Cp\u003EWhen \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003Eartificial intelligence\u003C/a\u003E influences customer support, financial analysis, compliance workflows or executive decisions, a bad output is a business risk. 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For organizations running AI on business-critical data, it’s the foundation on which trustworthy enterprise AI gets built.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-observability":{"id":"title-v2-2ac859dd9a","additionalClasses":"anchor-title anchor-title--what-is-ai-observability","type":"heading2","lines":["What is AI observability?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-observability_0":{"id":"text-d31ba2b45c","text":"\u003Cp\u003EAI observability is the practice of instrumenting, evaluating and tracing AI systems so teams can understand their behavior across the full path from input to output. For a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003Egenerative AI\u003C/a\u003E application, that path might include the user prompt, retrieved documents, model response, tool calls, intermediate reasoning steps, latency, token usage, cost and evaluation scores. 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Visibility sheds light on where a problem is originating and what must be addressed to fix it.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EMeasurability:\u003C/b\u003E Organizations need a consistent way to evaluate whether AI outputs are accurate, relevant, grounded, safe, timely and cost effective for the workflow they support. Measurability gives teams a way to compare prompts, models, retrieval strategies and configurations with evidence rather than guesswork.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EGovernability:\u003C/b\u003E AI systems need to operate within enterprise controls. This means honoring access policies, using approved data sources, recording evaluations and keeping enough trace evidence to investigate failures. As AI is integrated into regulated workflows and touches sensitive data, observability must become part of \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E rather than a separate engineering concern.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExplainability:\u003C/b\u003E Business users, auditors, developers and executives need to understand how an output was produced before they can trust it. Explainability doesn’t require exposing every internal detail of a model, but it does require showing the inputs, context, decision path and evaluation evidence that allow a team to investigate, remediate and improve.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-is-ai-observability_0":{"id":"card-v2-aeafcf1259","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"title":{"id":"title","type":"heading4","lines":["Customer story: TS Imagine"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/ts-imagine/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Read the full case study"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3b812ed0-848d-4714-96e8-ce82ea5f8ebb/ts-imagine%25403x.png?preferwebp=true&quality=85","alt":"TSImagine Logo","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ETS Imagine uses Snowflake and Snowflake Cortex AI to unify data, teams and technologies across more than 500 financial-services clients while scaling generative AI use cases across the business. With RAG-based workflows and Streamlit in Snowflake, TS Imagine automated manual email monitoring, accelerated customer-support triage and knowledge discovery, reduced AI costs by 30% compared with external LLM APIs, and saved 4,000 hours of manual effort per year.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal",":type":"snowflake-site/components/card-v2"},"title_ai-observability-vs-ml-monitoring-vs-model-monitoring":{"id":"title-v2-ef2e5a189a","additionalClasses":"anchor-title anchor-title--ai-observability-vs-ml-monitoring-vs-model-monitoring","type":"heading2","lines":["AI observability vs. ML monitoring vs. model monitoring"],":type":"snowflake-site/components/title-v2"},"text_ai-observability-vs-ml-monitoring-vs-model-monitoring_0":{"id":"text-21bf55124b","text":"\u003Cp\u003EAI observability overlaps with model monitoring and ML monitoring, but the scope is broader.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EModel monitoring focuses on a deployed model:\u003C/strong\u003E Teams track whether the model’s predictions remain accurate, whether input data has drifted, whether latency is changing and whether the model still performs against expected service-level objectives. This is the core of monitoring for many supervised \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) systems, especially those with clear ground truth such as fraud detection, churn prediction or demand forecasting.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EML monitoring extends that view across the pipeline:\u003C/strong\u003E A model can degrade because features arrive late, labels are delayed, schema changes break a transformation or a batch job introduces missing values. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/observability-vs-monitoring/\"\u003EML monitoring\u003C/a\u003E tracks the operational path around the model, including data quality, feature freshness, pipeline health and prediction behavior.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAI observability covers the full AI system:\u003C/strong\u003E In a \u003Ca href=\"https://www.snowflake.com/en/fundamentals/large-language-model/\"\u003Elarge language model\u003C/a\u003E (LLM) application, the model is only one component in a larger chain. A \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG\u003C/a\u003E app might retrieve five documents, pass three into the prompt, call an LLM, apply a guardrail and write the response into a workflow. An agent might plan a task, call a search tool, query a table, summarize results, call another tool and then produce a recommendation. Observability has to capture the trace, not just the final answer.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cem\u003ERead our \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/mlops/\"\u003Eguide on MLOps\u003C/a\u003E to learn how teams operationalize the machine learning lifecycle, from model development and deployment to monitoring and management in production.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-three-pillars-of-observability":{"id":"title-v2-43d0e05d56","additionalClasses":"anchor-title anchor-title--the-three-pillars-of-observability","type":"heading2","lines":["The three pillars of observability"],":type":"snowflake-site/components/title-v2"},"text_the-three-pillars-of-observability_0":{"id":"text-5e2b1e6862","text":"\u003Cp\u003EThe classic pillars of observability are logs, metrics and traces. They still apply to AI systems, but the objects being observed are different. Instead of looking only at service errors, CPU usage or API latency, teams also need to inspect prompts, retrieved documents, model responses, embedding behavior, token usage, tool calls, groundedness scores and human feedback.\u003C/p\u003E\r\n\u003Ch3\u003ELogs\u003C/h3\u003E\r\n\u003Cp\u003EAt the event level, logs can capture the prompt template, user query, model name, response metadata, retrieval query, selected documents, tool calls, policy decisions, error messages and feedback. In an enterprise setting, those records need governance of their own. Prompts and responses may contain proprietary data, regulated information or internal business context, so retention, redaction and role-based access should be part of the design.\u003C/p\u003E\r\n\u003Cp\u003EA useful log record gives the team enough detail to investigate without turning observability into another source of unmanaged sensitive data. For some workflows, that may mean storing metadata by default and allowing limited access to full prompt and response content during approved investigations.\u003C/p\u003E\r\n\u003Ch3\u003EMetrics\u003C/h3\u003E\r\n\u003Cp\u003EOver time, metrics show whether the system is holding steady. Latency, throughput, error rate, request volume, token usage and cost per request capture the operational side. Groundedness, answer relevance, context relevance, factual correctness, refusal rate, toxicity, completeness and consistency capture more of the AI-quality side.\u003C/p\u003E\r\n\u003Cp\u003EIn ML workflows, data drift, prediction drift and concept drift help teams understand whether the model is still operating under familiar conditions. Population stability index (PSI), feature attribution and SHAP values can support that analysis when a model depends on structured features and known outcomes. For generative AI, where outputs are language-based and often judged against context rather than a single label, evaluation metrics may need to score how well an answer uses retrieved information or satisfies a task-specific rubric.\u003C/p\u003E\r\n\u003Ch3\u003ETraces\u003C/h3\u003E\r\n\u003Cp\u003EWithin a single request, a trace follows the system from one step to the next. In a basic chatbot, that may mean the prompt, model call and response. In a RAG application, the trace can include the original question, retrieval query, ranked chunks, prompt assembly, inference call, evaluation results and final answer. In an agentic workflow, spans may cover planning, tool selection, tool responses, retries and intermediate outputs.\u003C/p\u003E\r\n\u003Cp\u003EThe value of the trace is that it preserves sequence. If an answer cites the wrong policy, the team can see whether the wrong document was retrieved, whether the right document was retrieved but ignored, or whether the model generated unsupported text despite receiving the right context. If an agent takes 45 seconds to answer a simple question, the trace can show whether the time went to model inference, a slow tool, repeated retrieval or a loop in the agent’s plan.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EWatch Snowflake’s Chief Data & Analytics Officer, Anahita Tafvizi, unpack the architecture behind trustworthy gen AI:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_the-three-pillars-of-observability_0":{"id":"embed-3074eada97","youtubeVideoId":"gA-R_MgZQAk","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_identifying-ai-drift":{"id":"title-v2-48bb90ec7b","additionalClasses":"anchor-title anchor-title--identifying-ai-drift","type":"heading2","lines":["Identifying AI drift"],":type":"snowflake-site/components/title-v2"},"text_identifying-ai-drift_0":{"id":"text-38fc67bb26","text":"\u003Cp\u003EAs AI systems combine structured data, unstructured documents, embeddings and runtime context, drift can appear in several places.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EData drift:\u003C/strong\u003E Input data changes over time. A customer support classifier trained on last year’s tickets may see new product categories, new terminology or new escalation patterns.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EConcept drift:\u003C/strong\u003E The relationship between inputs and outcomes changes. A fraud model might learn from one pattern of abuse, then face a new tactic that makes old correlations less reliable.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EPrediction drift:\u003C/strong\u003E Output distributions change. For example, a model that used to classify 10% of cases as high risk now classifies 35% that way, even though the business did not expect such a shift.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EEmbedding drift:\u003C/strong\u003E Vector representations change as data, models or embedding strategies change. In a RAG system, this can affect which documents are retrieved and how similar two pieces of content appear to the system.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EData-quality drift:\u003C/strong\u003E Missing values, delayed updates, schema changes and duplicate records affect the data the system uses, even when the model itself has not changed.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003E\u003Cstrong\u003EFairness\u003C/strong\u003E\u003C/a\u003E \u003Cstrong\u003Eand bias drift:\u003C/strong\u003E Model behavior can shift differently across user groups, regions, product lines or language patterns. Observability helps teams detect whether quality, refusal behavior or error rates are unevenly distributed.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_identifying-ai-drift_0":{"id":"text-b1d2350ad9","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMonitoring alone isn’t sufficient. It can show when something changed, but observability provides the logs, metrics, traces and evaluations needed to understand why it changed and how to fix it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_llm-and-agentic-ai-observability":{"id":"title-v2-8dba97d09a","additionalClasses":"anchor-title anchor-title--llm-and-agentic-ai-observability","type":"heading2","lines":["LLM and agentic AI observability"],":type":"snowflake-site/components/title-v2"},"text_llm-and-agentic-ai-observability_0":{"id":"text-f05d938ce0","text":"\u003Cp\u003ELLM observability focuses on the behavior of systems that use large language models, including chatbots, document assistants, summarization workflows, coding assistants, text-to-SQL interfaces and RAG applications. In those systems, quality depends on more than the model response. Teams need to know whether the application retrieved the right context, assembled the right prompt, followed the right controls and produced an answer that was useful, grounded and governed.\u003C/p\u003E\n\u003Ch3\u003ETrace the RAG pipeline\u003C/h3\u003E\n\u003Cp\u003EIn a RAG pipeline, the answer is shaped before the model generates a response. The query may be ambiguous, the retrieval layer may return irrelevant documents, or the prompt may include the right document but leave out an instruction to cite it. The model may generate an answer that sounds reasonable but is not supported by the retrieved context.\u003C/p\u003E\n\u003Cp\u003EA groundedness score can help flag that kind of failure, but the trace shows where it entered the system. By capturing the query, retrieved documents, ranked chunks, prompt assembly, model response and evaluation result, teams can see whether the problem started with retrieval, context selection, prompt design or model behavior.\u003C/p\u003E\n\u003Ch3\u003EMonitor prompts, responses, cost and latency\u003C/h3\u003E\n\u003Cp\u003EPrompt and response tracing gives developers a way to inspect the request path. Spans can capture retrieval, model calls, tool calls and other operations inside a single trace, so teams can examine each step without reconstructing the workflow from separate logs.\u003C/p\u003E\n\u003Cp\u003EToken usage monitoring helps teams understand cost and performance, especially when an application sends long prompts, retrieves too much context or loops through repeated tool calls. Latency metrics show whether slow responses come from retrieval, inference, orchestration or an external service. Together, these signals help teams tune the application without treating quality, speed and cost as separate issues.\u003C/p\u003E\n\u003Ch3\u003ECapture agent steps and tool calls\u003C/h3\u003E\n\u003Cp\u003EAgentic AI observability adds another layer because agents don’t simply answer a prompt. An agent might engage in a whole series of actions: call a search tool, query a structured table, open a document, summarize findings and decide whether to ask a follow-up question.\u003C/p\u003E\n\u003Cp\u003EObservability needs to capture that sequence so teams can see whether the agent selected the right tools, accessed the right data and stopped at the right point. Without that step-by-step record, an agent’s final response may look acceptable even when the workflow behind it was inefficient, unsupported or outside the intended policy boundaries.\u003C/p\u003E\n\u003Ch3\u003EStandardize gen AI telemetry\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://opentelemetry.io/\" target=\"_blank\"\u003EOpenTelemetry\u003C/a\u003E is becoming an important standardization point for LLM and agentic AI observability. Its generative AI semantic conventions define how gen AI operations can be recorded, including model calls, token counts and, when organizations opt in, prompt content, completions, tool calls and tool results.\u003C/p\u003E\n\u003Cp\u003EBecause AI applications often span multiple frameworks, models and infrastructure layers, a common telemetry model is vital for comparing behavior across systems, reusing instrumentation patterns and avoiding a fragmented observability stack.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-implement-ai-observability":{"id":"title-v2-ce59121c8a","additionalClasses":"anchor-title anchor-title--how-to-implement-ai-observability","type":"heading2","lines":["How to implement AI observability"],":type":"snowflake-site/components/title-v2"},"text_how-to-implement-ai-observability_0":{"id":"text-b17ae689f1","text":"\u003Cp\u003EBefore instrumenting an AI application, teams should decide what the system needs to prove in production. For example, a customer support assistant may need to prove that its answer came from current policy content, while a finance assistant may need to show which tables, metric definitions and reporting periods shaped a variance explanation.\u003C/p\u003E\n\u003Cp\u003EThe requirements of the workflow determine what should be captured. In lower-risk workflows, lightweight feedback, sampled traces and basic quality checks may be enough. In regulated or business-critical workflows, the application likely needs deeper tracing, stricter evaluation data sets, access-controlled records and clear promotion criteria before a prompt, model or retrieval change reaches production.\u003C/p\u003E\n\u003Ch3\u003EInstrument the execution path with telemetry\u003C/h3\u003E\n\u003Cp\u003EInstrumentation captures the logs, metrics and traces that make the system observable. For AI systems, that means instrumenting the application path around the model: prompts, retrieval, tool calls, model responses, evaluation scores, latency, token usage and user feedback.\u003C/p\u003E\n\u003Cp\u003EFor a RAG application, that includes the user prompt, retrieval query, retrieved context, prompt construction, model response, evaluation scores, latency and cost. For an agent, it also includes planning, tool calls, intermediate results, retries and stopping conditions.\u003C/p\u003E\n\u003Ch3\u003EEstablish baselines and alerts\u003C/h3\u003E\n\u003Cp\u003EOnce traces and metrics are flowing, baselines give teams a normal range for quality and performance, including latency, cost, groundedness, refusal rate, answer relevance, drift metrics and other signals. These baselines will vary by workflow, so the baseline should reflect the task rather than a generic standard.\u003C/p\u003E\n\u003Cp\u003EAlerts should focus on meaningful changes rather than every deviation. A small increase in token usage after adding richer context may be expected, while a sudden drop in groundedness after a prompt change warrants investigation.\u003C/p\u003E\n\u003Ch3\u003EBuild feedback loops\u003C/h3\u003E\n\u003Cp\u003EAI observability should feed improvement. Evaluation results can guide prompt changes, retrieval tuning, model selection, guardrail adjustments and data-quality remediation. User feedback can help identify gaps in test data. Trace analysis can show where an agent repeats work, chooses the wrong tool or fails to use retrieved context.\u003C/p\u003E\n\u003Cp\u003EThis loop is especially important for generative AI because quality is often comparative. Teams need to know whether a new prompt, model, embedding strategy or inference configuration improved the application for the intended task. Snowflake’s AI Observability supports side-by-side comparison of evaluations across LLMs, prompts and inference configurations, which helps teams assess response quality before promoting a configuration to production.\u003C/p\u003E\n\u003Ch3\u003EStandardize where possible\u003C/h3\u003E\n\u003Cp\u003EAs AI applications spread across teams, observability can fragment quickly. One team logs prompts in an application database, another tracks cost in a dashboard and another keeps evaluation results in notebooks. Standardizing telemetry, evaluation data sets, metrics and trace formats helps the organization compare systems and reuse operating patterns.\u003C/p\u003E\n\u003Cp\u003EOpenTelemetry can help with the telemetry layer, while governed data platforms can help with evaluation data, access controls, monitoring records and trace storage. The goal is not to force every AI application into the same design. It’s to make sure every production AI system leaves enough evidence for teams to evaluate and improve it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-implement-ai-observability_0":{"id":"text-840927980b","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EInstrument the full execution path early — prompts, retrieval, tool calls, model responses, evaluation scores, latency, token usage and feedback — so teams have the evidence needed to troubleshoot, compare configurations and govern AI in production.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_why-ai-observability-on-snowflake":{"id":"title-v2-bc3750acc5","additionalClasses":"anchor-title anchor-title--why-ai-observability-on-snowflake","type":"heading2","lines":["Why AI observability on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_why-ai-observability-on-snowflake_0":{"id":"text-3de407434f","text":"\u003Cp\u003EFor enterprise AI, the data used to generate an answer and the evidence needed to evaluate that answer should stay close together. When source data, prompts, traces, evaluations and access policies live across disconnected systems, every investigation starts with reconstruction: which version ran, which context was retrieved, which policy applied and which configuration changed.\u003C/p\u003E\n\u003Cp\u003EWithin the Snowflake AI Data Cloud, \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ESnowflake Cortex AI\u003C/a\u003E gives teams access to generative AI capabilities on governed enterprise data, while Snowflake AI Observability supports systematic evaluation, comparison and tracing for generative AI applications and agents.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_building-trust-through-observable-ai":{"id":"title-v2-779f10f9c8","additionalClasses":"anchor-title anchor-title--building-trust-through-observable-ai","type":"heading2","lines":["Building trust through observable AI"],":type":"snowflake-site/components/title-v2"},"text_building-trust-through-observable-ai_0":{"id":"text-44e1618e10","text":"\u003Cp\u003EAs AI systems move further into business processes, trust will depend on the organization’s ability to inspect the path behind each output. A trace, an evaluation score, a retrieved document and a model configuration are not after-the-fact engineering details. They’re the records that let teams improve AI systems without losing control of the data, policies and workflows those systems rely on.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_building-trust-through-observable-ai_0":{"id":"text-51bacae9e0","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAI observability gives teams the visibility they need to trust production AI by tracing how prompts, data, retrieved context, models, tool calls and application logic shape each output.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-ai-observability","text_what-is-ai-observability_0","card_v2_what-is-ai-observability_0","title_ai-observability-vs-ml-monitoring-vs-model-monitoring","text_ai-observability-vs-ml-monitoring-vs-model-monitoring_0","title_the-three-pillars-of-observability","text_the-three-pillars-of-observability_0","yt_the-three-pillars-of-observability_0","title_identifying-ai-drift","text_identifying-ai-drift_0","callout_identifying-ai-drift_0","title_llm-and-agentic-ai-observability","text_llm-and-agentic-ai-observability_0","title_how-to-implement-ai-observability","text_how-to-implement-ai-observability_0","callout_how-to-implement-ai-observability_0","title_why-ai-observability-on-snowflake","text_why-ai-observability-on-snowflake_0","title_building-trust-through-observable-ai","text_building-trust-through-observable-ai_0","callout_building-trust-through-observable-ai_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sticky-sidebar","columnClassNames":{"text_943981956_copy_":"aem-GridColumn 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