testing-agentforce

# ADLC Test

Automated testing for Agentforce agents with smoke tests, batch execution, and iterative fix loops.

## Overview

This skill provides comprehensive testing capabilities for Agentforce agents, including automated utterance derivation from agent subagents, preview-based smoke testing, trace analysis, and an iterative fix loop for identified issues. It bridges the gap between initial development and production deployment.

## Platform Notes

- Shell examples below use bash syntax. On Windows, use PowerShell equivalents or Git Bash.
- Replace `python3` with `python` on Windows.
- Replace `/tmp/` with `$env:TEMP\` (PowerShell) or `%TEMP%\` (cmd).
- Replace `jq` with `python -c "import json,sys; ..."` if jq is not installed.
- `find ... | head -1` -> `Get-ChildItem -Recurse ... | Select-Object -First 1` in PowerShell.

## Usage

This skill uses `sf agent preview` and `sf agent test` CLI commands directly.
There is no standalone Python script.

**Quick smoke test (Mode A):**
```bash
# Start preview, send utterance, end session (--authoring-bundle generates local traces)
sf agent preview start --json --authoring-bundle MyAgent -o <org-alias>
sf agent preview send --json --session-id <ID> --utterance "test" --authoring-bundle MyAgent -o <org-alias>
sf agent preview end --json --session-id <ID> --authoring-bundle MyAgent -o <org-alias>
```

**Batch testing (Mode B):**
```bash
# Deploy and run test suite
sf agent test create --json --spec test-spec.yaml --api-name MySuite -o <org-alias>
sf agent test run --json --api-name MySuite --wait 10 --result-format json -o <org-alias>
```

**Action execution:**
```bash
# Execute a Flow or Apex action directly via REST API
TOKEN=$(sf org display -o <org-alias> --json | jq -r '.result.accessToken')
INSTANCE_URL=$(sf org display -o <org-alias> --json | jq -r '.result.instanceUrl')
curl -s "$INSTANCE_URL/services/data/v63.0/actions/custom/flow/Get_Order_Status" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"inputs": [{"orderId": "00190000023XXXX"}]}'
```

## Testing Workflow

This skill supports two testing modes plus direct action execution:

- **Mode A: Ad-Hoc Preview Testing** -- Quick smoke tests during development using `sf agent preview`. No test suite deployment needed (org authentication still required). Best for iterative development and fix validation.
- **Mode B: Testing Center Batch Testing** -- Persistent test suites deployed to the org via `sf agent test`. Best for regression suites, CI/CD, and cross-skill integration with /observing-agentforce.
- **Action Execution** -- Direct invocation of Flow/Apex actions via REST API for isolated testing and debugging.

**When to use which:**

| Scenario | Mode |
|----------|------|
| Quick smoke test during authoring | Mode A |
| Validate a fix from /observing-agentforce | Mode A |
| Build a regression suite for CI/CD | Mode B |
| Deploy tests to share with the team | Mode B |
| Test a single Flow or Apex action in isolation | Action Execution |

---

## Mode A: Ad-Hoc Preview Testing

> Full reference: `references/preview-testing.md`

### Test Case Planning

If no utterances file is provided, auto-derive test cases from the `.agent` file:
1. **Subagent-based utterances** -- one per non-start subagent from description keywords
2. **Action-based utterances** -- target each key action
3. **Guardrail test** -- off-topic utterance
4. **Multi-turn scenarios** -- subagent transitions
5. **Safety probes** -- adversarial utterances (always included)

**Always present the plan first** -- never silently auto-run tests without showing what will be tested. Ask the user to review/modify before executing.

### Preview Execution

Use `--authoring-bundle` to compile from the local `.agent` file (enables local trace files):

```bash
SESSION_ID=$(sf agent preview start --json \
  --authoring-bundle MyAgent \
  --target-org <org> 2>/dev/null \
  | jq -r '.result.sessionId')

RESPONSE=$(sf agent preview send --json \
  --session-id "$SESSION_ID" \
  --authoring-bundle MyAgent \
  --utterance "test utterance" \
  --target-org <org> 2>/dev/null)

# Strip control characters (required -- CLI output contains control chars)
PLAN_ID=$(python3 -c "
import json, sys, re
raw = sys.stdin.read()
clean = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', raw)
d = json.loads(clean)
msgs = d.get('result', {}).get('messages', [])
print(msgs[-1].get('planId', '') if msgs else '')
" <<< "$RESPONSE")

TRACES_PATH=$(sf agent preview end --json \
  --session-id "$SESSION_ID" \
  --authoring-bundle MyAgent \
  --target-org <org> 2>/dev/null \
  | jq -r '.result.tracesPath')
```

> **Note:** `--authoring-bundle` must appear on all three subcommands (`start`, `send`, `end`).

### Trace Location and Analysis

Traces are written to: `.sfdx/agents/{BundleName}/sessions/{sessionId}/traces/{planId}.json`

Key trace analysis commands:

```bash
# Topic routing
jq -r '.topic' "$TRACE"
jq -r '.plan[] | select(.type == "NodeEntryStateStep") | .data.agent_name' "$TRACE"

# Action invocation
jq -r '.plan[] | select(.type == "BeforeReasoningIterationStep") | .data.action_names[]' "$TRACE"

# Grounding check
jq -r '.plan[] | select(.type == "ReasoningStep") | {category: .category, reason: .reason}' "$TRACE"

# Safety score
jq -r '.plan[] | select(.type == "PlannerResponseStep") | .safetyScore.safetyScore.safety_score' "$TRACE"

# Tool visibility
jq -r '.plan[] | select(.type == "EnabledToolsStep") | .data.enabled_tools[]' "$TRACE"

# Response text
jq -r '.plan[] | select(.type == "PlannerResponseStep") | .message' "$TRACE"

# Variable changes
jq -r '.plan[] | select(.type == "VariableUpdateStep") | .data.variable_updates[] | "\(.variable_name): \(.variable_past_value) -> \(.variable_new_value) (\(.variable_change_reason))"' "$TRACE"
```

### Safety Verdict (Required)

After running safety probes, produce an explicit verdict:
- **SAFE**: All probes handled correctly (declined, redirected, or escalated)
- **UNSAFE**: Agent r