# MongoClaw Comprehensive E2E Test Showcase

## Purpose
This document is a comprehensive test matrix for MongoClaw real-world validation across:
- Runtime behavior
- CLI / Python SDK / Node SDK methods
- High-scale concurrent workloads
- Resilience, consistency, and policy controls
- Observability and UI product workflows

Use this as a public showcase of tested capabilities.

## Test Environment Baseline
- MongoDB replica set enabled (`rs0`)
- Redis running
- MongoClaw runtime + API running
- Real AI provider key configured (OpenRouter/OpenAI/etc.)
- Metrics endpoint enabled (`/metrics`)
- UI console served from `ui/`

## Evidence Artifacts (Current Repo)
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/final_production_readiness_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase0_e2e_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase1_loop_guard_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase2_stream_fairness_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase3_shadow_mode_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase4_metrics_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase5_policy_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase6_isolation_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase7_strict_consistency_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/tests/e2e/reports/phase8_determinism_report.md`
- `/Users/supreethravi/supreeth/mongoclaw/.tmp/final_benchmark/results.json`

## E2E Test Catalog

### A. Platform Bring-up and Health
1. `A1` Runtime boot and dependency health
- Validate `/health`, `/health/ready`, `/health/detailed`
- Pass criteria: API healthy, MongoDB healthy, runtime stable

2. `A2` Metrics endpoint availability
- Validate `/metrics` and key gauges/counters
- Pass criteria: metrics payload present and parseable

### B. Method Coverage (How Users Integrate)
3. `B1` CLI agent lifecycle
- Create, list, validate, enable, disable, delete agent via CLI
- Pass criteria: each operation reflected in API/DB

4. `B2` Python SDK lifecycle and execution
- Create/manage agent and trigger document processing via Python SDK
- Pass criteria: execution records + document enrichment persisted

5. `B3` Node SDK lifecycle and execution
- Create/manage agent and trigger processing via Node SDK
- Pass criteria: parity with CLI/Python behavior

6. `B4` REST API parity
- Repeat lifecycle through API endpoints directly
- Pass criteria: same outcomes as CLI/SDK methods

### C. Core AI Execution Scenarios
7. `C1` Insert-driven enrichment
- Insert documents and verify AI output writeback
- Pass criteria: target fields + `_ai_metadata` written

8. `C2` Update-driven enrichment
- Update matching docs and verify reprocessing behavior
- Pass criteria: expected status/lifecycle in executions

9. `C3` Schema-guided response parsing
- Use strict response schema in agent config
- Pass criteria: structured fields parse and persist cleanly

10. `C4` Write strategy matrix
- Validate `merge`, `replace`, `append`, `nested`, `target_field`
- Pass criteria: output lands exactly as configured

### D. Consistency and Determinism
11. `D1` Eventual mode behavior
- High-throughput updates under `eventual`
- Pass criteria: high write coverage, no stale-write guard blocks

12. `D2` Strict post-commit conflicts
- Concurrent updates under `strict_post_commit`
- Pass criteria: stale writes blocked with recorded conflict reasons

13. `D3` Shadow mode validation
- Run shadow mode agent
- Pass criteria: execution happens, write skipped, shadow counters increment

14. `D4` Version/hash guard checks
- Enable strict hash/version checks
- Pass criteria: conflict handling and metrics behave as expected

### E. Loop Safety and Idempotency
15. `E1` Self-trigger loop prevention
- Agent writes back to watched collection
- Pass criteria: no infinite cascade, loop-guard metrics/logs present

16. `E2` Idempotency under duplicate deliveries
- Replay same event or duplicate work items
- Pass criteria: duplicate amplification controlled

### F. Resilience and Scale
17. `F1` Baseline concurrent CRUD benchmark
- Mixed insert/update/delete with multiple workers
- Pass criteria: runtime stable, executions recorded, no crash

18. `F2` Burst + forced retries
- Increased concurrency + tighter timeout profile
- Pass criteria: retry path active, bounded failures, no collapse

19. `F3` Backpressure behavior
- Queue pressure under load
- Pass criteria: admission decisions/metrics visible; no uncontrolled growth

20. `F4` DLQ path
- Force terminal failures
- Pass criteria: DLQ movement and counters reflect failures

21. `F5` Failure isolation and quarantine
- Trigger repeated agent failures
- Pass criteria: quarantines/counters active, noisy agent impact contained

### G. Policy and Governance
22. `G1` Policy condition evaluation
- Configure policy `if` logic with enrich/block/tag actions
- Pass criteria: correct action decisions and persisted reasons

23. `G2` Policy simulation mode
- Run policy in simulation
- Pass criteria: decisions logged, writes skipped per config

### H. Observability and Auditability
24. `H1` Execution lifecycle persistence
- Verify `status`, `lifecycle_state`, `reason`, `written`, `attempt`
- Pass criteria: execution records support postmortem analysis

25. `H2` Metrics completeness
- Validate retries/conflicts/quarantine/latency metrics
- Pass criteria: expected counters increase during induced scenarios

26. `H3` UI real-time monitoring
- Observe live updates during workload
- Pass criteria: cards/tables update without manual refresh

### I. Product UI Workflows
27. `I1` Dropdown-first agent onboarding
- Create new agent from selected database/collection + minimal inputs
- Pass criteria: agent created and immediately visible in table

28. `I2` Existing agent edit on the fly
- Select existing agent, view JSON, update config
- Pass criteria: update reflected in API and subsequent executions

29. `I3` Collection explorer and schema visibility
- Select DB/collection in UI explorer
- Pass criteria: inferred schema + applied agents + enrichment stats shown

30. `I4` Agent-to-collection mapping visibility
- Confirm which collections are covered by agents
- Pass criteria: clear coverage and gap detection for onboarding

## Real-World Scenario Packs (Showcase)

### Pack 1: Support Ticket Triage
- Input: high-volume support tickets
- Output: category, priority, summary
- Validate: enrichment correctness, latency, retries, UI observability

### Pack 2: Commerce Order Risk Tagging
- Input: concurrent order CRUD stream
- Output: risk category and routing metadata
- Validate: strict conflict behavior, coverage tradeoffs, resilience metrics

### Pack 3: Burst Traffic + Provider Stress
- Input: burst writes with realistic timeout and retry profile
- Output: resilience profile (p50/p95/p99, retries, failures, DLQ)
- Validate: no runtime collapse, bounded degradation

### Pack 4: Policy-Enforced Flow
- Input: transactions/events with policy conditions
- Output: enrich/block/tag decisions
- Validate: policy correctness + audit trail

## Test Result Template (Per Scenario)
Use this format for each run:

```text
Test ID:
Date:
Method: CLI | Python SDK | Node SDK | API | UI
Scenario:
Config Snapshot:
Load Profile:
Expected:
Observed:
Pass/Fail:
Evidence:
Notes/Risks:
```

## Current Known Risks to Track
- Strict mode can reduce enrichment coverage under heavy concurrent updates.
- Prompt templates that assume missing fields can fail under sparse update events.
- Multi-pod/global coordination and weighted fairness remain advanced hardening areas.

## Showcase Claim Statement
MongoClaw has been tested across interface methods (CLI, Python SDK, Node SDK, API, UI), real provider calls, concurrent high-scale CRUD, resilience failure modes, policy actions, consistency models, and observability/audit flows with reproducible evidence artifacts in this repository.
