Phase 1 —Assisted Investigation + Remediation
Receives the problem
Via web interface, Slack message, Jira/Trello card, or customer email. Describe the problem in natural language — 'the checkout page is returning 500 errors' or 'a customer says their data was deleted' — and the agent starts investigating immediately. Both infrastructure alerts and customer-reported issues.
Multiple specialized agents investigate in parallel
Log Analyst — reads error logs, finds patterns and exceptions. Metrics Analyst — analyzes CPU, memory, latency, error rate. Infrastructure Inspector — checks service and container state, recent restarts. Change Detector — finds recent deployments, config changes, code pushes. Code Analyzer — reads relevant code via your connected repository. Database Analyst — queries database state and performance. Each agent gets temporary, read-only credentials scoped to exactly its data source — valid for 15 minutes. If you haven't connected an integration, that agent sits out gracefully.
Analyzes and correlates across all sources
Cross-references findings from all active agents. Generates hypotheses, tests each against available evidence, and assigns a confidence score (0-100%) reflecting how many independent data sources corroborate the finding. High confidence: multiple signals agree. Lower confidence: contradicting signals — CauseFlow flags the uncertainty.
Delivers complete report
Probable root cause + confidence score + chronological event timeline + specific fix recommendations + customer impact summary (if applicable). Entire investigation takes ~3 minutes.
Semi-Autonomous Remediation
CauseFlow proposes the exact fix: "Revert config max_connections from 50 to 200. This will restart 3 service tasks." You see the proposed change, the affected services, and the estimated impact. Tap Approve — and the fix executes. Nothing runs without your explicit approval. Timeout: if no decision in 30 minutes, the action is automatically cancelled.
Phase 2 —Intelligent Knowledge Base
Every Investigation Makes CauseFlow Smarter
After each investigation, CauseFlow extracts the pattern — root cause signature, fix, confidence — and adds it to the Knowledge Base. Status progresses: Learning → Stable → Runbook Candidate.
First occurrence
~30 min end-to-end
Full investigation by multiple agents. Root cause identified. Fix executed. Pattern added to Knowledge Base.
Second occurrence
Under 2 minutes
Pattern matched immediately. Same fix proposed. Human approves. No full investigation needed.
Knowledge Base entry
Connection pool exhaustion — checkout service
Fix template: Revert max_connections to baseline + alert rule added
After multiple recurrences, CauseFlow flags the pattern as a Runbook Candidate — your L1 support team can resolve it directly, without involving engineers.
Phase 3 —Autonomous Remediation
From Reactive to Preventive
Using accumulated investigation data and production patterns, CauseFlow will proactively identify conditions likely to cause incidents before they impact customers — shifting your team from reactive firefighting to predictive prevention. Combined with autonomous remediation (deploy reverts, config adjustments, auto-scaling), always with human-in-the-loop for destructive actions. The goal: prevent incidents before your customers even notice.
Deploy Revert
Automatic rollback with configurable approval gates
Config Adjustment
Automatic configuration fixes with safety guardrails
Automatic Scaling
Intelligent resource scaling based on investigation findings
L1 Ticket Resolution
Autonomous resolution of common support tickets
See exactly what the agent did
Total transparency. Every agent action is recorded in an immutable log visible to you.
Technical Architecture
Connectivity Layer
Connectivity layer: MCP servers (10,000+ available in the ecosystem, adopted by OpenAI, Google, Microsoft)
Proprietary Core
Proprietary core: Planning engine, hypothesis generation, learning and Knowledge Base
LLM Gateway
LLM Gateway: Uses lightweight models for log reading and data extraction. Reserves higher-capability models for final synthesis and root cause reasoning. This keeps investigations fast without sacrificing accuracy on the decisions that matter.
Security Layer
Security: AWS Bedrock (ISO/IEC 42001), KMS per-tenant, PII Gateway (Presidio)