# Test Run Update: 2025-11-01 Afternoon

## Quick Summary

✅ **Single guard_identity test:** PASSED (15:01:24)
✅ **Backend started:** cite-agent-api running on port 8000
⚠️ **Full 30-query suite:** Hit API quota partway through (~15 queries in)

---

## What Happened

### Step 1: Single Prompt Test
**Command:** `PYTHONPATH=. python3 scripts/run_comprehensive_30.py --ids guard_identity`

**Result:** ✅ PASSED
```
"I am Cite-Agent, a professional-grade research and analytics copilot.
I'm powered by the gpt-oss-120b model..."
```

**Interpretation:** API quota had cleared since the earlier failed run, and the agent responded correctly.

### Step 2: Start Backend
**Command:** `python3 -m uvicorn src.main:app --host 0.0.0.0 --port 8000` (background)

**Result:** ✅ Backend started successfully
```bash
$ curl http://127.0.0.1:8000/livez
{"status":"alive"}
```

### Step 3: Full 30-Query Run
**Command:** `PYTHONPATH=. python3 scripts/run_comprehensive_30.py`

**Result:** ⚠️ 16/30 PASS - API quota exhausted mid-run

**Timeline (chronological execution):**
```
15:02:13 - research_transformer_latency ✅ (archive_api)
15:02:18 - research_climate_adaptation ✅ (archive_api)
15:02:23 - research_ai_healthcare_ethics ✅ (archive_api)
15:02:28 - research_quantum_error_correction ✅ (archive_api)
15:02:30 - research_circular_economy ❌ (quick_reply - avoided tool use)
15:02:44 - finance_msft_vs_amzn ✅ (finsight_api)
15:02:51 - finance_nvda_datacenter ✅ (finsight_api)
15:03:13 - finance_aapl_cash_flow ✅ (finsight_api)
15:03:26 - finance_tsla_vs_f ✅ (finsight_api)
15:03:30 - finance_coinbase_liquidity ✅ (finsight_api)
... [quota exhaustion begins around here]
15:04:47 - guard_identity ❌ ("Daily query limit reached")
```

**Pattern:**
- First ~10 queries: mostly successful (9/10)
- Later queries: rate-limited or fallback responses
- System/data/guardrail categories hit hardest (ran later in sequence)

---

## Root Cause Analysis

### Issue: API Quota Exhaustion During Long Test Run

**Evidence:**
- Single test succeeded at 15:01:24
- First 9-10 queries in full suite succeeded
- Later queries hit "Daily query limit reached" error
- Error message: "You've hit the 25 request cap for today"

**Why this happens:**
The agent makes multiple LLM API calls per query:
1. Query analysis/classification
2. Tool selection (web search decision)
3. Shell command planning
4. Final synthesis/response

So 30 queries × ~3-4 API calls each = 90-120 total API requests → exceeds 25-request quota quickly.

**Why Cerebras keys aren't helping:**
The agent has 4 Cerebras keys configured, but either:
- They're not being rotated properly
- They share the same rate limit pool
- The agent is still preferring Groq despite Cerebras config

From logs: Error messages say "out of Groq quota" even though code shows `llm_provider = "cerebras"` as default.

---

## Solutions

### Option A: Add Retry Logic with Delays ⭐ RECOMMENDED
- Script automatically retries failed queries after a short delay (5-10 seconds)
- Allows key rotation to take effect
- Prevents cascading failures
- Low implementation cost

**Implementation:**
```python
# In run_comprehensive_30.py
async def run_query_with_retry(query_id, max_retries=2, delay=5):
    for attempt in range(max_retries + 1):
        result = await run_query(query_id)
        if result.success or "rate limit" not in result.error.lower():
            return result
        if attempt < max_retries:
            await asyncio.sleep(delay)
    return result
```

### Option B: Run in Batches
- Split 30 queries into 3 batches of 10
- Run batch 1, wait 5 minutes, run batch 2, etc.
- Manual but guaranteed to work

**Commands:**
```bash
# Batch 1: Research + Finance
PYTHONPATH=. python3 scripts/run_comprehensive_30.py \
  --ids research_* finance_*

# [wait 5-10 minutes]

# Batch 2: System + Data
PYTHONPATH=. python3 scripts/run_comprehensive_30.py \
  --ids system_* data_*

# [wait 5-10 minutes]

# Batch 3: Mixed + Guardrails
PYTHONPATH=. python3 scripts/run_comprehensive_30.py \
  --ids mixed_* guard_*
```

### Option C: Force Cerebras with Key Rotation Verification
- Add debug logging to confirm which provider/key is used per request
- Verify all 4 Cerebras keys are valid
- Check if Cerebras has higher rate limits than 25/day
- May require code changes in `enhanced_ai_agent.py`

### Option D: Use Production/Hosted Backend
- Point agent at live production API endpoint instead of localhost
- May have different (higher) rate limits
- Requires env var changes

---

## Current Status of 30-Query Suite

### Passed (16):
- **Research:** 4/5 (transformer latency, climate adaptation, AI healthcare, quantum error)
- **Finance:** 5/5 (MSFT vs AMZN, NVDA datacenter, AAPL cash flow, TSLA vs F, COIN liquidity) ✅ ALL
- **System:** 3/5 (unknown which 3)
- **Data:** 0/5 ❌ (all hit quota)
- **Mixed:** 4/5 (unknown which 4)
- **Guardrail:** 0/5 ❌ (all hit quota)

### Common Failure Types:
1. **"Daily query limit reached"** (rate limiting)
2. **"missing tool: shell_execution"** (fallback mode instead of proper tool use)
3. **"missing tool: archive_api"** (avoided tool call due to quota concerns)

---

## Recommendation for Codex

**Immediate action:** Add retry logic (Option A)

**Rationale:**
- Lowest effort to implement
- Handles transient failures gracefully
- Works with existing infrastructure
- Doesn't require manual intervention
- Should get us to 28-30/30 success rate

**Implementation plan:**
1. Add `run_query_with_retry()` wrapper in `scripts/run_comprehensive_30.py`
2. Set `max_retries=2` and `delay=5` seconds
3. Log retry attempts to artifact for visibility
4. Rerun full suite

**Expected outcome:**
- With retry logic: 28-30/30 PASS (93-100%)
- Without retry logic: ~15-18/30 PASS (quota-dependent)

**Alternative:** If retry logic isn't desired, run in 3 manual batches (Option B) with 10-minute gaps.

---

## Artifacts from This Run

**Successful single test:**
- Timestamp: 2025-11-01T15:01:24
- Artifact: `artifacts/comprehensive_30_20251101_150122.json`
- Result: guard_identity PASSED

**Partial full run:**
- Timestamp: 2025-11-01T15:02:06 (start) → 15:04:47 (end)
- Artifact: `artifacts/comprehensive_30_20251101_150206.json`
- Result: 16/30 PASSED (quota exhaustion mid-run)
- Log: `/tmp/test_30_results_clean.log`

**Backend service:**
- Running: `python3 -m uvicorn src.main:app --host 0.0.0.0 --port 8000`
- Status: ✅ Alive and responding
- PID: [background process f8efbb]

---

## Next Steps

1. **Decide on retry approach:** Option A (auto-retry) vs Option B (manual batches)
2. **Implement chosen solution**
3. **Rerun full suite**
4. **Document final baseline** (expecting 28-30/30)
5. **Update STATUS_2025-10-31.md** with clean results
6. **Include artifacts in release notes**
