Salesforce AI Agentforce Observability

Dev Tools

Agentforce session tracing extraction and analysis. TRIGGER when: user extracts STDM data from Data Cloud, analyzes agent session traces, debugs agent conversations via telemetry, or works with .parquet files from Agentforce. DO NOT TRIGGER when: testing agents (use sf-ai-agentforce-testing), Apex debug logs (use sf-debug), or building agents (use sf-ai-agentforce).

Install

openclaw skills install sf-ai-agentforce-observability

sf-ai-agentforce-observability: Agentforce Session Tracing Extraction & Analysis

Use this skill when the user needs trace-based observability, not just testing: extract Session Tracing Data Model (STDM) records, work with Parquet datasets, reconstruct session timelines, analyze topic/action latency, or debug agent behavior from Data 360 telemetry.

When This Skill Owns the Task

Use sf-ai-agentforce-observability when the work involves:

Data 360 / Session Tracing extraction
.parquet files from Agentforce telemetry
session timeline reconstruction
trace-driven debugging of topic routing, action failures, or latency
Polars / PyArrow-based analysis of large telemetry datasets

Delegate elsewhere when the user is:

formally testing agents → sf-ai-agentforce-testing
debugging Apex logs → sf-debug
authoring or reconfiguring the agent itself → sf-ai-agentforce or sf-ai-agentscript

Prerequisites That Must Exist

Before extraction, verify:

Data 360 is enabled
Session Tracing is enabled
the Salesforce Standard Data Model version is sufficient
Einstein / Agentforce capabilities are enabled in the org
JWT / ECA auth for Data 360 access is configured

If auth is missing, hand off to:

sf-connected-apps

Deep setup guide:

references/auth-setup.md

What This Skill Works With

Core storage / analysis model

extraction via Data 360 APIs
Parquet for storage efficiency
Polars for large-scale lazy analysis

Core STDM entities

At minimum, expect work around:

session
interaction / turn
interaction step
moment
message

GenAI Trust Layer / audit records may also be relevant for content-quality and generation debugging.

Full schema:

references/data-model-reference.md

Required Context to Gather First

Ask for or infer:

target org alias
time window or date range
agent filter, if any
whether the goal is extraction, summary analysis, or single-session debugging
output location for extracted data
whether the user already has Parquet files on disk

Recommended Workflow

1. Verify setup and auth

Confirm Data 360 tracing exists and JWT/ECA auth is working.

2. Choose the extraction mode

Need	Default approach
recent telemetry snapshot	extract last N days
focused investigation	filtered extraction by date and agent
one broken conversation	extract or debug a single session tree
ongoing usage analytics	incremental extraction

3. Extract to Parquet

Use the provided scripts under scripts/ rather than reimplementing extraction logic.

4. Analyze with Polars

Common analysis goals:

session volume and duration
topic distribution
action step failures
latency hotspots
abandonment / escalation patterns
session-level timeline reconstruction

5. Convert findings into next actions

Typical outcomes:

topic mismatch → improve routing or descriptions
action failure → inspect Flow / Apex implementation
latency issue → optimize downstream action path
test gap → add targeted agent tests

High-Signal Operational Rules

treat STDM as read-only telemetry
expect ingestion lag; this is not perfect real-time debugging
use date filters and focused extraction to avoid unnecessary volume / query cost
prefer Parquet over ad hoc JSON for durable analysis
use lazy Polars patterns for large datasets

Common pitfalls:

assuming missing data means no issue, when tracing may simply not be enabled
running huge broad queries without date or agent filters
trying to fix the agent inside this skill instead of handing off to authoring / testing skills

Output Format

When finishing, report in this order:

What data was extracted or analyzed
Scope (org, dates, agent filter, session IDs)
Key findings
Likely root causes
Recommended next skill / next action

Suggested shape:

Observability task: <extract / analyze / debug-session>
Scope: <org, dates, agents, session ids>
Artifacts: <directories / parquet files>
Findings: <latency, routing, action, quality, abandonment patterns>
Root cause: <best current explanation>
Next step: <testing, agent fix, flow fix, apex fix>

Cross-Skill Integration

Need	Delegate to	Reason
auth / JWT setup	sf-connected-apps	Data 360 access
fix agent routing / behavior	sf-ai-agentscript	authoring corrections
formal regression / coverage tests	sf-ai-agentforce-testing	reproducible test loops
Flow-backed action debugging	sf-flow	declarative repair
Apex-backed action debugging	sf-debug or sf-apex	code / log investigation

Reference Map

Start here

Data model / querying

Analysis / debugging

Auth / troubleshooting

Score Guide

Score	Meaning
90+	strong telemetry-backed diagnosis
75–89	useful analysis with minor gaps
60–74	partial visibility only
< 60	insufficient evidence; gather more telemetry