How Binary Intelligence works
Binary Intelligence is a closed-loop operations system. It does not stop at alerting or scripted fixes; it verifies recovery before closing the incident.
Closed-loop workflow
- Stage 1, Observe: Windows Fleet Runs. Structured events, metrics, and dependency state across the fleet — not raw log noise. Owner: BI agent. Time to next stage: ≈ ms.
- Stage 2, Diagnose: AI Diagnoses. Cross-endpoint correlation produces confidence-weighted, policy-aware recommendations. Owner: Reasoning engine. Time to next stage: ms – s.
- Stage 3, Remediate: Remediation Executes. Approved remediation runs inside operator-defined safety boundaries, with every action recorded. Owner: Playbooks. Time to next stage: s – min.
- Stage 4, Validate: Recovery Validated. Health is re-checked against the live model before the incident is closed. Owner: Validation engine. Time to next stage: min.
- Stage 5, Adapt: Endpoints Healed. Validated outcomes refine thresholds and playbooks — no agent redeploy required. Owner: Continuous loop. Time to next stage: continuous.
- Policy: all stages run inside operator-defined boundaries. The cycle repeats continuously without redeploy.
- 01 OBSERVE
Windows Fleet Runs
Structured events, metrics, and dependency state across the fleet — not raw log noise.
- 02 DIAGNOSE
AI Diagnoses
Cross-endpoint correlation produces confidence-weighted, policy-aware recommendations.
- 03 REMEDIATE
Remediation Executes
Approved remediation runs inside operator-defined safety boundaries, with every action recorded.
- 04 VALIDATE
Recovery Validated
Health is re-checked against the live model before the incident is closed.
- 05 ADAPT
Endpoints Healed
Validated outcomes refine thresholds and playbooks — no agent redeploy required.
- POLICY
All five stages run inside operator-defined boundaries. The cycle repeats continuously without redeploy.
01 OBSERVE
Model live state
Structured events, metrics, and dependency state across the fleet — not raw log noise.
BI agent · ≈ ms
02 DIAGNOSE
Rank the safest fix
Cross-endpoint correlation produces confidence-weighted, policy-aware recommendations.
ms - s
03 REMEDIATE
Act inside bounds
Approved remediation runs inside operator-defined safety boundaries, with every action recorded.
playbooks · s - min
04 VALIDATE
Prove recovery
Health is re-checked against the live model before the incident is closed.
min
05 ADAPT
Learn for next time
Validated outcomes refine thresholds and playbooks — no agent redeploy required.
continuous
POLICY: All five stages run inside operator-defined boundaries. The cycle repeats continuously without redeploy.
- Bold arc = primary handoff (diagnose to remediate)
- Dashed inner ring = operator-defined policy boundary
- Filled node = autonomous stage inside the policy envelope
Observe · Diagnose · Remediate · Validate — one closed loop, running continuously. Read the framework brief →
Example incident timeline
- 00:00 Signal model detects spooler drift and failed dependency check.
- 00:05 Engine selects baseline restore + restart sequence (confidence 0.93).
- 00:08 Remediation executes within approved policy envelope.
- 00:20 Validation confirms stable service state; incident closed with evidence.
Operational controls
- Bounded autonomy: Autonomous actions run only inside your approved envelope.
- Approval pathways: Low-confidence and sensitive actions route for operator review.
- Auditability: Each action logs reason, confidence, execution details, and validation result.
- Provider choice: Use OpenAI, Anthropic, Ollama, or LocalAI based on your data posture.
Want to map this loop to your environment?
We run a scoped architecture workshop to map integrations, policy boundaries, and pilot outcomes before deployment.