sf-ai-agentforce-observability

Extract and analyze Agentforce session tracing data from Salesforce Data Cloud.

6|Updated Dec 20, 2023

Search Tags:#extraction #parquet #polars #agentforce #stmd #data-360

Ready to run

npx skills add https://github.com/ronitnuguru/Package-Visualizer --skill sf-ai-agentforce-observability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sf-ai-agentforce-observability
Source: https://github.com/ronitnuguru/Package-Visualizer/tree/main/.cursor/skills/sf-ai-agentforce-observability
Command: npx skills add https://github.com/ronitnuguru/Package-Visualizer --skill sf-ai-agentforce-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, pyarrow, pyjwt, cryptography, polars, rich, click, pydantic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Agentforce session tracing data is large and complex; this Skill enables scalable extraction and analysis from Salesforce Data Cloud to support debugging and performance insights.

Core Features & Use Cases

Extract STDM data (sessions, interactions, steps, messages) to Parquet for efficient storage and analysis.
Analyze traces with Polars to detect patterns, bottlenecks, and escalation paths across agents.
Use cases include debugging sessions, performance optimization, and compliance auditing for data-trace workflows.

Quick Start

Run the stdm-extract CLI to pull recent sessions and then analyze results with the STDMAnalyzer in Python.

Frequently Asked Questions about sf-ai-agentforce-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract Agentforce session tracing data from Salesforce Data Cloud?▼

You can extract Agentforce session tracing data from Salesforce Data Cloud by running the stdm-extract CLI, which automates pulling sessions, interactions, steps, and messages into Parquet files for efficient storage and analysis.

How does Polars analysis work with Agentforce STDM data?▼

Polars analysis processes Agentforce STDM data by querying extracted Parquet files to detect performance patterns, bottlenecks, and escalation paths across agents using the STDMAnalyzer Python API.

Can I use this for high-volume Agentforce tracing workloads?▼

Yes, this supports high-volume Agentforce tracing workloads by enforcing STDM schemas and enabling both incremental and full extractions to Parquet, ensuring scalable data processing across large session datasets.

What is the best way to debug Agentforce sessions in Data Cloud?▼

The best way to debug Agentforce sessions is to extract STDM tracing data to Parquet using the CLI, then analyze session interactions and steps with the Polars-driven STDMAnalyzer to identify execution bottlenecks.

Does the STDM extraction enforce schema validation for Agentforce data?▼

Yes, STDM extraction enforces schema validation for Agentforce data by applying STDM schemas across sessions, interactions, steps, and messages to ensure consistent data structure during Parquet storage and analysis.

What are the limitations of Parquet-based storage for Agentforce trace analysis?▼

Parquet-based storage for Agentforce trace analysis requires Python dependencies like pyarrow and polars, and while it enables efficient columnar analysis, it does not support real-time streaming of session tracing data from Data Cloud.