UnderstandingHow AI Does RMM
The OODA loop is a military decision framework: Observe → Orient → Decide → Act. Every agent runs this loop continuously, learning from every outcome.
The Continuous Loop
Click each phase to learn how the brain processes information and takes action. This loop runs every 10 seconds, ensuring rapid response to changing conditions.
Observe Everything
The edge agent streams real-time telemetry from every endpoint—CPU, memory, disk, processes, services, network, security events.
- Lightweight Go agent on every endpoint
- Sub-second telemetry streaming
- osquery for deep system introspection
- No polling—continuous observation
See It In Action
Watch the brain process a real-world scenario: High CPU on a production server.
Signal: High CPU Alert
ACME-DC01 • CPU at 98% for 15 minutes
Confidence Gates Action
Not all actions are equal. The brain assesses risk and requires appropriate approval before executing.
Read Only
Gather information, no changes
Low Risk
Minor changes, easily reversible
Medium Risk
Significant changes, may need rollback
High Risk
Major changes, business impact possible
Critical
Dangerous actions, irreversible
Every Outcome Teaches
The brain doesn't just act—it learns. Every decision and outcome is recorded, building institutional knowledge that improves over time.
Action Executed
Brain takes action based on OODA decision
Outcome Observed
Was the problem fixed? Did anything break?
Result Recorded
Decision + outcome stored in memory
Confidence Updated
Success increases confidence; failure decreases
Pattern Learned
Similar situations get better recommendations
Semantic Search Across All Decisions
Every decision becomes a 1024-dimensional vector. Similar problems cluster together, enabling instant pattern discovery.
Embedding Every Decision
Using OpenAI's text-embedding-3-large model, every decision, outcome, and pattern is converted to a 1024-dimensional vector and stored in pgvector with HNSW indexing.
Semantic Similarity Search
When a new signal arrives, the brain finds similar past situations instantly—not by keyword matching, but by understanding the meaning.
Confidence From History
Past outcomes directly inform current confidence. If similar actions succeeded before, confidence increases. If they failed, the brain adjusts.
Cross-Fleet Pattern Discovery
With tenant consent, anonymized patterns from across the fleet improve everyone's experience. When a fix works for one MSP, similar MSPs benefit from that knowledge—without exposing sensitive data.
Traditional RMM vs. Agentic RMM
See the fundamental difference in approach
Traditional RMM
- ×Alert fires → Human reviews → Human decides → Human acts
- ×Knowledge lives in technician heads
- ×Same problems recur with same manual fixes
- ×Reactive: Wait for problems to happen
- ×Evidence: Scattered across tickets and emails
Agentic RMM
- Signal detected → AI reasons → AI decides → AI acts (with gates)
- Knowledge in evolutionary brain, accessible to all
- Patterns learned, confidence grows, outcomes improve
- Proactive: Predict and prevent before impact
- Evidence: Cryptographic chains for every action
Ready to See It In Action?
Watch the OODA loop process real telemetry, make decisions, and take action—all in a live demo.