Observe by Snowflake adds Agent Observability to monitor, debug and optimize AI agents

# What this is Snowflake is adding Agent Observability to Observe, its observability product. The feature is entering private preview and is aimed at teams that run multi-step AI agents and need a way to trace, debug and evaluate agent interactions while keeping an eye on cost and quality.

# Why teams need it Agent workflows are often multi-step: prompts, model calls, retrievals, tool invocations, retries and final completions. A request can succeed technically while still returning wrong or low-quality results. Those multi-step interactions generate far more telemetry than a typical API call, which makes it harder and more expensive to retain the detail needed to investigate failures, measure quality, or explain token consumption.

# What Agent Observability does Agent Observability provides end-to-end visibility into agent interactions and ties operational signals to business data in Snowflake. Key capabilities listed by Snowflake include:

• Instrumentation with an optional OpenTelemetry (OTel)-compliant SDK that captures prompts, completions, retrievals, tool calls, token usage and session IDs. The SDK includes tested support for LangChain, the Anthropic Agents SDK and the OpenAI Agents SDK. Teams can also use their own instrumentation and send traces via an OTLP endpoint.

• Agent Explorer for searching traces, sessions and conversations to inspect behavior, tool usage and the point where quality or failures occurred.

• Online LLM-as-judge evaluations that run on production traffic to surface hallucinations, guardrail failures and poor-quality responses and link results back to the underlying spans and traces.

# How it handles data volume and cost Snowflake positions Observe to handle the scale and structure of agent telemetry by:

• Storing spans as events so long-running traces and late-arriving spans remain part of a complete interaction, searchable by trace ID or conversation ID.

• Using Snowflake's native support for semi-structured event data so prompts, model responses, retrieved context and tool inputs/outputs can be stored without stripping context.

# How teams will use it in practice

# Availability and next steps Agent Observability is announced for private preview. Snowflake is hosting a launch event on Oct. 22 where executives will discuss the feature and its roadmap.

# Bottom line This addition to Observe targets a real operational gap introduced by agentic LLM apps: the need to retain and examine complex, long-running traces, evaluate response quality in production, and connect agent behavior to cost and business outcomes. Snowflake's approach emphasizes keeping complete interaction traces and lowering storage cost through a separation of storage and compute.