v1.0 - Lightweight Release

IFP Edge v1.0
Lightweight AI-Powered Monitoring

5 services. 5 minutes. Zero configuration.

Intelligent infrastructure monitoring that runs anywhere Docker runs. No cloud required.

📦 5 containers ⚡ 300MB footprint 🧠 AI-powered 🚀 5-min setup
01

Two-Tier Architecture

Agent-native intelligence with strategic meta-observation layer

02

Recursive Reasoning

Predicts cascading failures before they happen

Depth 1-10+
03

SAGE Coordination

Strategic meta-observer managing infrastructure + agents

04

Learned Confidence

Agent layer executes with adaptive confidence weights

05

Production-Proven

87-95% success rates across all agents

Live Now
06

Context Memory

25+ days of continuous learning and adaptation

69%
Autonomous
31%
Human-Approved
12,960
Cycles Completed
30+
Days Runtime
🧠

SAGE: Strategic Meta-Observer

  • Observes agents AND infrastructure
  • Recursive reasoning (depth 1-10+)
  • Context accumulation (25+ days)
  • Hybrid escalation: 69% auto, 31% human
12,960 cycles 1,440/day 60s interval
↕

Coordinates

🤖

Agent Layer: Domain Specialists

  • DevOps Agent: 87% success (Docker)
  • Web3 Agent: 95% success (Blockchain)
  • Correlation Agent: 73% success (Meta)
  • Learning: Confidence weight adaptation
4,680 obs/day 30-120s cycles 3-loop reasoning 
git clone https://github.com/Nordvei/ifp-edge.git
cd ifp-edge
./quick-start.sh
⭐ Get IFP Edge v1.0 Live SAGE Dashboard How It Works

What's Included in IFP Edge v1.0

Lightweight monitoring stack - everything you need in 5 Docker containers.

5
Services Included

Complete monitoring stack

300MB
Total Footprint

Lightweight and efficient

2GB
RAM Required

4GB recommended

60s
SAGE Cycle Interval

AI analysis every minute

Why IFP Works Differently

Traditional monitoring reacts. IFP predicts.

Traditional Monitoring

Single-Tier Detection
  • ❌
    Reactive Only

    Alerts fire after problems occur.

  • ❌
    Manual Correlation

    Humans connect dots between alerts.

  • ❌
    No Learning

    Same problems repeat indefinitely.

  • ❌
    Alert Fatigue

    Too many false positives to trust.

IFP Two-Tier Intelligence

SAGE + Agent Layer
  • ✅
    Predictive Meta-Observation

    SAGE predicts cascading failures before they happen.

  • ✅
    Automatic Correlation

    Correlation Agent finds cross-domain patterns.

  • ✅
    Continuous Learning

    Confidence weights adapt from execution outcomes.

  • ✅
    High-Confidence Actions

    69% autonomous, 31% human-in-the-loop.

🧠 The SAGE Advantage

SAGE doesn't just observe your infrastructure.

SAGE observes the agents observing your infrastructure.

Then SAGE reflects on its own past reflections.

This recursive meta-observation enables prediction at depth.

Traditional monitoring cannot do this.

How Recursive Reasoning Works →

SAGE + Agent Layer Capabilities

Not monitoring dashboards. Real autonomous intelligence that learns.

🧠

SAGE: Recursive Meta-Observer

Strategic intelligence coordinating everything.

  • Observes agents AND infrastructure
  • Recursive reasoning (depth 1-10+)
  • Context accumulation (25+ days)
  • Predicts cascading failures
Current: 12,960 cycles completed
Frequency: Every 60 seconds
Depth: 3 levels (expanding to 10+ with DGX)
🤖

Agent Layer: Domain Specialists

Tactical executors with learned confidence.

  • DevOps Agent: 87% success (1,440 obs/day)
  • Web3 Agent: 95% success (2,880 obs/day)
  • Correlation Agent: 73% success (720 obs/day)
  • Three-loop: Observe → Analyze → Act
Total: 4,680 observations/day
Learning: Confidence weight adaptation
Production: 30+ days operational
⚖️

Hybrid Escalation Policy

Balance autonomy with human oversight.

  • Tier 1: Always escalate (31% - critical systems)
  • Tier 2: Conditional auto-execute (50% - monitoring)
  • Tier 3: Encouraged auto-execute (19% - edge services)
  • You stay in control, always
Result: 69% autonomous, 31% human-approved
Safety: Critical systems require approval
Transparency: All decisions logged
🔍

Pattern Discovery (HDBSCAN)

Statistical clustering without hallucinations.

  • HDBSCAN clustering (deterministic math)
  • 1,195 features extracted per cycle
  • Requires 3+ occurrences (no guessing)
  • Shows WHY patterns were detected
Performance: 0.41s per extraction cycle
Frequency: Every 5 minutes
False positives: Near-zero (mathematical proof)
🔒

Self-Hosted / Air-Gapped

Zero cloud dependencies for compliance.

  • Works completely offline (with DGX)
  • Perfect for banks, healthcare, defense
  • GDPR compliant by design
  • All data stays on your hardware
Current: 35/35 containers self-hosted
Option: Cloud APIs for enhanced reasoning
Your choice: Local or hybrid deployment
💰

DGX Cost Optimization

90% cost reduction with local inference.

  • Current: $28.80/day (Claude API)
  • With DGX: $2.88/day (local Llama 3.3 70B)
  • Savings: $777/month after hardware ROI
  • Hardware: $4,000 one-time (5-month payback)
Benefit: Deeper reasoning (10-100x depth)
Speed: 1-2s vs 3-5s (cloud API)
Privacy: No external data transmission

IFP vs Cloud Monitoring

We're not better at everything. We're better at transparency and autonomous intelligence.

Feature IFP Edge Datadog New Relic
Two-Tier Intelligence ✓ SAGE + Agent Layer ✗ Single-tier detection ✗ Single-tier detection
Recursive Meta-Observation ✓ Depth 1-10+ levels ✗ Not available ✗ Not available
Agent Learning System ✓ Confidence weight adaptation Partial (proprietary) Partial (proprietary)
Self-Hosted ✓ Yes ✗ Cloud only ✗ Cloud only
Air-Gap Capable ✓ Yes (with DGX) ✗ No ✗ No
Success Rate Transparency ✓ Published (87-95%) ✗ Not disclosed ✗ Not disclosed
Open Source ✓ Yes (MIT) ✗ Proprietary ✗ Proprietary
Context Accumulation ✓ 25+ days (growing) Unknown Unknown
Monthly Cost (100 hosts) $86-864 $1,500-2,300 $1,800-2,500

Frequently Asked Questions

Honest answers about SAGE, agents, and two-tier intelligence.

What is SAGE? ▼

SAGE (Self-Aware General Executor) is IFP's strategic meta-observer.

SAGE doesn't just monitor infrastructure. SAGE observes the agents monitoring infrastructure, then reflects on its own past reflections.

This recursive reasoning enables prediction before cascading failures occur.

Key capabilities:

  • Recursive reasoning (depth 1-10+ levels)
  • Context accumulation (25+ days of learning)
  • Hybrid escalation policy (69% auto, 31% human)
  • Never executes directly - coordinates specialists
How do agents learn? ▼

Agents use confidence weight adaptation based on execution outcomes.

After each action:

  • Success → Increase confidence by 10% (weight × 1.1)
  • Failure → Decrease confidence by 10% (weight × 0.9)
  • Weights bounded: 0.1 ≤ weight ≤ 2.0

Example: DevOps Agent over 30 days

  • Day 1: Restart confidence = 1.0 (baseline)
  • Day 15: Restart confidence = 1.21 (restarts work well)
  • Day 30: Restart confidence = 1.45 (high trust)

This produces the 87-95% success rates you see in production.

Why two tiers instead of one? ▼

Traditional monitoring has a single tier: observe infrastructure, fire alerts.

IFP's two-tier architecture adds meta-observation:

Tier 1 (SAGE): Strategic meta-observer

  • Observes agents AND infrastructure
  • Reflects recursively (depth 1-10+)
  • Predicts cascading failures
  • Coordinates specialists

Tier 2 (Agents): Tactical domain specialists

  • Execute in specific domains (Docker, Web3, K8s)
  • Fast response (30-120s cycles)
  • Learn from outcomes
  • 69% autonomous execution

This separation enables both speed (agents) and depth (SAGE).

Will IFP make changes to production automatically? ▼

Default: No. IFP uses a hybrid escalation policy.

Three tiers:

  • Tier 1 (31%): Always escalate to human - Control plane, databases, blockchain
  • Tier 2 (50%): Conditional auto-execute (confidence > 0.85) - Monitoring, agents
  • Tier 3 (19%): Encouraged auto-execute (confidence > 0.70) - Edge services, UI

You can adjust thresholds per container. You stay in control.

How quickly can I deploy IFP? ▼

3 commands, 60 seconds to install:

git clone https://github.com/Nordvei/ifp-edge.git
cd ifp-edge
./quick-start.sh

Timeline:

  • Minute 1: 35 containers start
  • Minute 5: Basic monitoring operational
  • Hour 1: Agents begin observations
  • Day 1: Pattern discovery accumulating
  • Week 1: Agent confidence weights tuned
  • Month 1: Full production intelligence
What's the DGX cost optimization about? ▼

SAGE uses LLM inference for deep reasoning. Two options:

Option 1: Cloud APIs (Current)

  • Cost: $28.80/day ($864/month)
  • Depth: Limited to 3 levels (cost constraint)
  • Latency: 3-5 seconds per cycle

Option 2: Local GPU (DGX Spark)

  • Cost: $2.88/day ($86/month)
  • Depth: 10-100 levels (no cost limit)
  • Latency: 1-2 seconds per cycle
  • Hardware: $4,000 one-time (5-month ROI)

Savings: $777/month after hardware payback

Where does my data go? ▼

Nowhere. All data processing happens on your infrastructure.

IFP is self-hosted. Your data never leaves your control.

Optional: You can enable cloud LLM APIs for enhanced SAGE reasoning, but this is opt-in only.

We don't collect analytics, phone home, or have access to your data.

What if SAGE makes a wrong prediction? ▼

SAGE doesn't execute directly - it coordinates agents.

If an agent makes a wrong decision based on SAGE's suggestion:

  • The agent's confidence weight decreases
  • SAGE learns the pattern didn't work
  • Future similar suggestions are less likely
  • You can review decision logs in insights.jsonl

The system improves continuously from mistakes.

Ready to Deploy Two-Tier Intelligence?

Open source. Production-proven. Running for 25+ days.

git clone https://github.com/Nordvei/ifp-edge.git
cd ifp-edge
./quick-start.sh
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