# Sf AI Agentforce Observability

> Extract and analyze Agentforce session tracing data from Salesforce Data 360. Supports high-volume extraction (1-10M records/day), Polars-based analysis, and debugging workflows for agent sessions.

- Skill: `tools-only/sf-ai-agentforce-observability-3` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds add tools-only/sf-ai-agentforce-observability-3`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/sf-ai-agentforce-observability-3/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/tools-only/sf-ai-agentforce-observability-3

---


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

Expert in extracting and analyzing Agentforce session tracing data from Salesforce Data 360. Supports high-volume data extraction (1-10M records/day), Parquet storage, and Polars-based analysis for debugging agent behavior.

## Core Responsibilities

1. **Session Extraction**: Extract STDM (Session Tracing Data Model) data via Data 360 Query API
2. **Data Storage**: Write to Parquet format with PyArrow for efficient storage
3. **Analysis**: Polars-based lazy evaluation for memory-efficient analysis
4. **Debugging**: Session timeline reconstruction for troubleshooting agent issues
5. **Cross-Skill Integration**: Works with sf-connected-apps for auth, sf-ai-agentscript for fixes

## Document Map

| Need | Document | Description |
|------|----------|-------------|
| **Quick start** | [README.md](README.md) | Installation & basic usage |
| **Data model** | [resources/data-model-reference.md](resources/data-model-reference.md) | Full STDM schema documentation |
| **Query patterns** | [resources/query-patterns.md](resources/query-patterns.md) | Data Cloud SQL examples |
| **Analysis recipes** | [resources/analysis-cookbook.md](resources/analysis-cookbook.md) | Common Polars patterns |
| **CLI reference** | [docs/cli-reference.md](docs/cli-reference.md) | Complete command documentation |
| **Auth setup** | [docs/auth-setup.md](docs/auth-setup.md) | JWT Bearer configuration |
| **Troubleshooting** | [resources/troubleshooting.md](resources/troubleshooting.md) | Common issues & fixes |
| **Analysis examples** | [references/analysis-examples.md](references/analysis-examples.md) | Session summary & debug timeline output |
| **Billing & issues** | [references/billing-and-troubleshooting.md](references/billing-and-troubleshooting.md) | Credit consumption & common errors |

---

## CRITICAL: Prerequisites Checklist

Before extracting session data, verify:

| Check | How to Verify | Why |
|-------|---------------|-----|
| **Data 360 enabled** | Setup → Data 360 | Required for Query API |
| **Salesforce Standard Data Model v1.124+** | Setup → Apps → Packaging → Installed Packages | Required for session tracing DMOs |
| **Einstein Generative AI enabled** | Setup → Einstein Generative AI | Enables agent capabilities |
| **Session Tracing enabled** | Setup → Einstein Audit, Analytics, and Monitoring | Must toggle ON to collect data |
| **JWT Auth configured** | Use `sf-connected-apps` | Required for Data 360 API |

> **Official Setup Guide**: [Set Up Agentforce Session Tracing](https://help.salesforce.com/s/articleView?id=ai.generative_ai_session_trace_setup.htm)

### Auth Setup (via sf-connected-apps)

```bash
# 1. Create key directory
mkdir -p ~/.sf/jwt

# 2. Generate certificate (naming convention: {org}-agentforce-observability)
openssl req -x509 -sha256 -nodes -days 365 -newkey rsa:2048 \
  -keyout ~/.sf/jwt/myorg-agentforce-observability.key \
  -out ~/.sf/jwt/myorg-agentforce-observability.crt \
  -subj "/CN=AgentforceObservability/O=MyOrg"

# 3. Secure the private key
chmod 600 ~/.sf/jwt/myorg-agentforce-observability.key

# 4. Create External Client App in Salesforce (see docs/auth-setup.md)
# Required scopes: cdp_query_api, refresh_token/offline_access
```

**Key Path Resolution Order:**
1. Explicit `--key-path` argument
2. App-specific: `~/.sf/jwt/{org}-agentforce-observability.key`
3. Generic fallback: `~/.sf/jwt/{org}.key`

See [docs/auth-setup.md](docs/auth-setup.md) for detailed instructions.

---

## T6 Live API Discovery Summary

**Validated: January 30, 2026** | **24 DMOs Found** | **260+ Test Points**

| Category | DMOs | Status |
|----------|------|--------|
| **Session Tracing** | 5 | All Found (Session, Interaction, Step, Message, Participant) |
| **Agent Optimizer** | 6 | All Found (Moment, Tag system) |
| **GenAI Audit** | 13 | All Found (Generation, Quality, Feedback, Gateway) |
| **RAG Quality** | 3 | Not Found (GenAIRetriever* DMOs don't exist) |

**Key Discoveries:**
- Field naming: API uses `AiAgent` (lowercase 'i'), not `AIAgent`
- Agent name location: Stored on `Moment`, not `Session`
- Channel types: `E & O`, `Builder`, `SCRT2 - EmbeddedMessaging`, `Voice`, `NGC`
- Participant roles: `USER`, `AGENT` (not Owner/Observer)

---

## Session Tracing Data Model (STDM)

> See [resources/data-model-reference.md](resources/data-model-reference.md) for the complete field-level schema of all 5 core DMOs + 13 GenAI Audit DMOs.

**5 Core DMOs** — all field names use `AiAgent` prefix (lowercase 'i'):

| DMO | Key | Relationship | Primary Fields |
|-----|-----|-------------|----------------|
| `AIAgentSession__dlm` | `Id__c` | Root | StartTimestamp, EndTimestamp, ChannelType, EndType |
| `AIAgentInteraction__dlm` | `Id__c` | Session → N Turns | TopicApiName, InteractionType, TraceId |
| `AIAgentInteractionStep__dlm` | `Id__c` | Turn → N Steps | StepType (LLM/ACTION), InputValue, OutputValue, Error |
| `AIAgentMoment__dlm` | `Id__c` | Session (NOT Turn) | **AgentApiName lives here**, RequestSummary, ResponseSummary |
| `AIAgentMessage__dlm` | `Id__c` | Turn → Messages | Content, Role, Timestamp |

**13 GenAI Trust Layer DMOs** — detectors for toxicity, PII, prompt defense, instruction adherence:

| DMO | Purpose | Key Fields |
|-----|---------|------------|
| `GenAIGatewayRequest__dlm` | LLM request details | model, provider, tokens, safety flags |
| `GenAIGeneration__dlm` | LLM output | responseText, links to Steps via GenerationId |
| `GenAIContentQuality__dlm` | Trust Layer assessment | isToxicityDetected |
| `GenAIContentCategory__dlm` | Detector results | detectorType, category, confidence value |
| `GenAIFeedback__dlm` | User feedback | GOOD/BAD + detail comments |

---

## Workflow (5-Phase Pattern)

### Phase 1: Requirements Gathering

Use **AskUserQuestion** to gather:

| # | Question | Options |
|---|----------|---------|
| 1 | Target org | Org alias from `sf org list` |
| 2 | Time range | Last N days / Date range |
| 3 | Agent filter | All agents / Specific API names |
| 4 | Output format | Parquet (default) / CSV |
| 5 | Analysis type | Summary / Debug session / Full extraction |

### Phase 2: Auth Configuration

Verify JWT auth is configured:

```python
from scripts.auth import Data360Auth

auth = Data360Auth(
    org_alias="myorg",
    consumer_key="YOUR_CONSUMER_KEY"
)

# Test authentication
token = auth.get_token()
print(f"Auth successful: {token[:20]}...")
```

If auth fails, invoke:
```
Skill(skill="sf-connected-apps", args="Setup JWT Bearer for Data 360")
```

### Phase 3: Extraction

**Basic Extraction (last 7 days):**
```bash
python3 scripts/cli.py extract \
  --org prod \
  --days 7 \
  --output ./stdm_data
```

**Filtered Extraction:**
```bash
python3 scripts/cli.py extract \
  --org prod \
  --since 2026-01-01 \
  --until 2026-01-28 \
  --agent Customer_Support_Agent \
  --output ./stdm_data
```

**Session Tree (specific session):**
```bash
python3 scripts/cli.py extract-tree \
  --org prod \
  --session-id "a0x..." \
  --output ./debug_session
```

### Phase 4: Analysis

**Session Summary:**
```python
from scripts.analyzer import STDMAnalyzer
from pathlib import Path

analyzer = STDMAnalyzer(Path("./stdm_data"))

# High-level summary
summary = analyzer.session_summary()
print(summary)

# Step distribution by agent
steps = analyzer.step_distribution(agent_name="Customer_Support_Agent")
print(steps)

# Topic routing analysis
topics = analyzer.topic_analysis()
print(topics)
```

**Debug Specific Session:**
```bash
python3 scripts/cli.py debug-session \
  --data-dir ./stdm_data \
  --session-id "a0x..."
```

### Phase 5: Integration & Next Steps

Based on analysis findings:

| Finding | Next Step | Skill |
|---------|-----------|-------|
| Topic mismatch | Improve topic descriptions | `sf-ai-agentscript` |
| Action failures | Debug Flow/Apex | `sf-flow`, `sf-debug` |
| Slow responses | Optimize actions | `sf-apex` |
| Missing coverage | Add test cases | `sf-ai-agentforce-testing` |

---

## CLI Quick Reference

### Extraction Commands

| Command | Purpose | Example |
|---------|---------|---------|
| `extract` | Extract session data | `extract --org prod --days 7` |
| `extract-tree` | Extract full session tree | `extract-tree --org prod --session-id "a0x..."` |
| `extract-incremental` | Resume from last run | `extract-incremental --org prod` |

### Analysis Commands

| Command | Purpose | Example |
|---------|---------|---------|
| `analyze` | Generate summary stats | `analyze --data-dir ./stdm_data` |
| `debug-session` | Timeline view | `debug-session --session-id "a0x..."` |
| `topics` | Topic analysis | `topics --data-dir ./stdm_data` |

### Common Flags

| Flag | Description | Default |
|------|-------------|---------|
| `--org` | Target org alias | Required |
| `--consumer-key` | ECA consumer key | `$SF_CONSUMER_KEY` env var |
| `--key-path` | JWT private key path | `~/.sf/jwt/{org}-agentforce-observability.key` |
| `--days` | Last N days | 7 |
| `--since` / `--until` | Date range (YYYY-MM-DD) | - / Today |
| `--agent` | Filter by agent API name | All |
| `--output` | Output directory | `./stdm_data` |
| `--verbose` | Detailed logging | False |

See [docs/cli-reference.md](docs/cli-reference.md) for complete documentation.

---

## Analysis Examples

> See [references/analysis-examples.md](references/analysis-examples.md) for full session summary and debug timeline output examples.

**Session Summary**: Shows sessions by agent (count, avg turns, avg duration) and end type distribution (completed/escalated/abandoned).

**Debug Timeline**: Reconstructs a session step-by-step — input → topic routing → LLM steps → action steps → output — with timestamps and I/O payloads.

---

## Cross-Skill Integration

| Skill | When | How to Invoke |
|-------|------|---------------|
| `sf-connected-apps` | Auth setup | `Skill(skill="sf-connected-apps", args="JWT Bearer for Data Cloud")` |
| `sf-ai-agentscript` | Fix topic routing issues | `Skill(skill="sf-ai-agentscript", args="Fix topic: [issue]")` |
| `sf-flow` / `sf-debug` | Debug action failures | `Skill(skill="sf-debug", args="Analyze agent action failure")` |
| `sf-ai-agentforce-testing` | Create test cases from patterns | `Skill(skill="sf-ai-agentforce-testing", args="Add test cases")` |

---

## Key Insights

| Insight | Description | Action |
|---------|-------------|--------|
| **STDM is read-only** | Data 360 stores traces; cannot modify | Use for analysis only |
| **Session lag** | Data may lag 5-15 minutes | Don't expect real-time |
| **Volume limits** | Query API: 10M records/day | Use incremental extraction |
| **Parquet efficiency** | 10x smaller than JSON | Always use Parquet for storage |
| **Lazy evaluation** | Polars scans without loading | Handles 100M+ rows |
| **~24 records per LLM call** | Each round-trip generates ~24 records | Factor into volume estimates |

---

## Billing & Common Issues

> See [references/billing-and-troubleshooting.md](references/billing-and-troubleshooting.md) for credit consumption details and error resolution.

**Quick reference**: Session Tracing consumes Data 360 credits. ~24 records per LLM round-trip. 1,000 sessions/day × 4 turns × 24 = ~96K records/day. Use [Digital Wallet](https://help.salesforce.com/s/articleView?id=sf.digital_wallet.htm) for consumption tracking.

| Error | Quick Fix |
|-------|-----------|
| `401 Unauthorized` | Refresh token or reconfigure ECA |
| `No session data` | Enable Session Tracing in Agent Settings |
| `Query timeout` | Add date filters, use incremental |
| `Memory error` | Use Polars lazy frames |

---

## Output Directory Structure

```
stdm_data/
├── sessions/          # date=YYYY-MM-DD/part-0000.parquet
├── interactions/      # date=YYYY-MM-DD/part-0000.parquet
├── steps/             # date=YYYY-MM-DD/part-0000.parquet
├── messages/          # date=YYYY-MM-DD/part-0000.parquet
└── metadata/          # extraction.json + watermark.json (incremental)
```

---

## Dependencies

**Python 3.10+**: `polars>=1.0`, `pyarrow>=15.0`, `pyjwt>=2.8`, `cryptography>=42.0`, `httpx>=0.27`, `rich>=13.0`, `click>=8.1`, `pydantic>=2.6`

Install: `pip install -r requirements.txt`