Why IFP works differently than traditional monitoring.
IFP uses two-tier agent-native intelligence architecture.
Most monitoring tools have single-layer detection systems.
IFP combines strategic meta-observer with tactical specialists.
SAGE observes infrastructure AND the agents themselves.
Agents execute in domains with learned confidence weights.
This creates recursive meta-observation capability unavailable elsewhere.
Result: Predicts problems before they cascade across systems.
| Property | Value | Verification |
|---|---|---|
| Scope | Infrastructure-wide (everything) | Live dashboard |
| Frequency | Every 60 seconds (1,440 cycles/day) | Process ID: 335021 |
| Depth | Recursive reasoning (1-10+ levels) | Technical details |
| Automation | 0% (suggests only, never executes) | logs/daemon_output.log |
| Intelligence | Claude Sonnet 4 (deep reasoning) | API usage logs |
| Learning | Context accumulation (25+ days) | insights.jsonl (25,000+ entries) |
| Production Status | 12,960 cycles completed | 9 days continuous operation |
Observation: ├─ DevOps Agent restart success: 87% → 79% (3-day trend) ├─ Correlation Agent: Pattern discovery CPU spikes └─ TimescaleDB: Feature extraction every 5 minutes SAGE Analysis (Depth 3): "DevOps Agent success rate decline correlates with feature extraction cycles. ML jobs cause temporary CPU spikes, increasing container restart failures. This is not declining agent performance - it's environmental correlation." SAGE Suggestion: "Increase DevOps Agent confidence threshold from 0.80 to 0.85 during ML training cycles. Schedule feature extraction during low-activity periods." Result: Success rate recovered to 89% (better than baseline)
| Agent | Domain | Success Rate | Observations/Day | Status |
|---|---|---|---|---|
| DevOps Agent | Docker containers (35 monitored) | 87% | 1,440 | ✅ Operational |
| Web3 Agent | Blockchain wallet monitoring | 95% | 2,880 | ✅ Operational |
| Correlation Agent | Cross-domain pattern detection | 73% | 720 | ✅ Operational |
| Total | 3 domains + meta | 85% avg | 4,680 | 30+ days runtime |
LOOP 1: OBSERVE ├─ Collect domain telemetry ├─ Frequency: 30-120 seconds per agent └─ Output: Structured telemetry data LOOP 2: ANALYZE ├─ Detect patterns in telemetry ├─ Generate insights with confidence scores └─ Output: List of actionable insights LOOP 3: ACT ├─ 3a. SUGGEST: Propose remediation actions ├─ 3b. EXECUTE: Take action if confidence high ├─ 3c. LEARN: Update weights from outcomes └─ Output: Action results + learning
Context accumulation: insights.jsonl (25+ days)
~/ifp-workspace/insights.jsonl