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 d

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](../sf ai agentforce testing/SKILL.md) debugging Apex logs → [sf debug](../sf debug/SKILL.md) authoring or reconfiguring the agent itself → [sf ai agentforce](../sf ai agentforce/SKILL.md) or [sf ai agentscript](../sf ai agentscript/SKILL.md) 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](../sf connected apps/SKILL.md) Deep setup guide: [references/auth setup.md](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](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: 1. What data was extracted or analyzed 2. Scope (org, dates, agent filter, session IDs) 3. Key findings 4. Likely root causes 5. Recommended next skill / next action Suggested shape: Cross Skill Integration Need Delegate to Reason auth / JWT setup [sf connected apps](../sf connected apps/SKILL.md) Data 360 access fix agent routing / behavior [sf ai agentscript](../sf ai agentscript/SKILL.md) authoring corrections formal regression / coverage tests [sf ai agentforce testing](../sf ai agentforce testing/SKILL.md) reproducible test loops Flow backed action debugging [sf flow](../sf flow/SKILL.md) declarative repair Apex backed action debugging [sf debug](../sf debug/SKILL.md) or [sf apex](../sf apex/SKILL.md) code / log investigation Reference Map Start here [README.md](README.md) [references/basic extraction.md](references/basic extraction.md) [references/filtered extraction.md](references/filtered extraction.md) [references/cli reference.md](references/cli reference.md) Data model / querying [references/data model reference.md](references/data model reference.md) [references/query patterns.md](references/query patterns.md) [references/client demo queries.md](references/client demo queries.md) Analysis / debugging [references/analysis cookbook.md](references/analysis cookbook.md) [references/analysis examples.md](references/analysis examples.md) [references/debugging sessions.md](references/debugging sessions.md) [references/polars cheatsheet.md](references/polars cheatsheet.md) [references/agent execution lifecycle.md](references/agent execution lifecycle.md) Auth / troubleshooting [references/auth setup.md](references/auth setup.md) [references/troubleshooting.md](references/troubleshooting.md) [references/billing and troubleshooting.md](references/billing and troubleshooting.md) [references/builder trace api.md](references/builder trace api.md) [scripts/](scripts/) 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

sf-ai-agentforce-observability