How depth 1-10+ reasoning predicts cascading failures.
SAGE performs recursive meta-observation at multiple depths.
Most monitoring observes infrastructure only at single level.
SAGE observes infrastructure AND the agents observing infrastructure.
Then SAGE reflects on its own past reflections.
Depth increases from one to ten-plus levels progressively.
Each level discovers patterns invisible at shallower depths.
This enables prediction before problems cascade across systems.
"Container ifp-grafana CPU: 78% (high but not critical)"
"DevOps Agent marked this as 'monitor' (no action).
Agent confidence for 'scale' action: 0.89 (medium).
Question: Why didn't agent suggest scaling?"
"DevOps Agent learned: Grafana CPU spikes correlate
with dashboard refreshes (every 30s). Spikes are
transient, not sustained load. Scaling would waste
resources. Agent's non-action was CORRECT decision."
"This pattern (transient spike + no action) appears
in 12 other services: Prometheus, TimescaleDB, Redis.
All have periodic query patterns. Agent has learned
to distinguish transient vs sustained load."
"DevOps Agent's learning system is working well.
Confidence weights accurately reflect service
behavior. No adjustment needed. This validates
the weight adaptation algorithm."
"Transient spike pattern correlates with feature
extraction cycles (every 5 min). All database
services show synchronized spikes. This is
expected behavior, not infrastructure problem."
"Next feature extraction: 14:35:00 UTC (+2 min).
Expected CPU spike: 78% → 85% (transient).
Expected duration: 8-12 seconds.
Action required: None (within normal parameters)."
| Depth Range | Insight Type | Equivalent Expertise |
|---|---|---|
| 1-2 | Reactive (problem detection) | What monitoring tools do |
| 3-4 | Analytical (root cause) | What good engineers do |
| 5-7 | Predictive (pattern recognition) | What senior engineers do |
| 8-10 | Optimizing (system-wide strategy) | What architects do |
| 10+ | Meta-learning (framework refinement) | What IFP does uniquely |
Cost per cycle: $0.012 (Claude API)
Daily cost: $0.012 × 1,440 cycles = $17.28/day
Monthly cost: $518.40/month
Limitation: Depth 3 is maximum economically viable.
Cost per cycle: $0.002 (local Llama 3.3 70B)
Daily cost: $0.002 × 1,440 cycles = $2.88/day
Monthly cost: $86.40/month
Savings: $432/month (83% reduction)
Benefit: Depth 10-100 becomes economically viable.
Result: Deeper reasoning → Better predictions → Fewer incidents.
View recursive reasoning chains in insights.jsonl
~/ifp-workspace/insights.jsonl
Depth: 3 levels (pre-DGX)
Target: 10-100 levels (post-DGX)