Phase 1 —Assisted Investigation + Remediation
Receives the problem
Via web interface, Slack message, Jira/Trello card or customer email. The user describes the problem in natural language and the agent starts the investigation.
Connects to all sources
Slack (messages from #incidents channel), GitHub (commits, PRs, recent releases), Jira (related tickets), CloudWatch (error logs), HubSpot (affected customer data). All in parallel.
Analyzes and correlates
Cross-references data from all sources using multi-model LLM with intelligent routing by complexity. Generates hypotheses, tests against evidence, classifies with confidence score.
Delivers complete report
Probable root cause + confidence score (0-100%) + chronological event timeline + specific fix recommendations + customer impact (if applicable).
Semi-Autonomous Remediation
With user approval, the agent executes the fix plan: generates PRs on GitHub, executes kubectl commands, remediation scripts, deploy revert. Always with human-in-the-loop before any destructive action.
Phase 2 —Intelligent Knowledge Base
The more you use it, the faster it resolves
The system learns from every resolved investigation, building a Knowledge Base that maps service dependencies and blast radius. When a similar problem is detected, CauseFlow suggests the previous solution immediately —recurring problems resolved in seconds, not hours.
Investigate
Every resolved case feeds the knowledge base
Learn
Maps dependencies and blast radius automatically
Resolve Faster
Recurring problems resolved in seconds, not hours
Phase 3 —Autonomous Remediation
Auto-healing with guardrails
Automatic correction with configurable approval: deploy revert, config adjustment, automatic scaling. Integration marketplace and autonomous L1 ticket resolution.
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 (8,620+ 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: Intelligent router that selects the optimal model by task complexity
Security Layer
Security: AWS Bedrock (ISO/IEC 42001), KMS per-tenant, PII Gateway (Presidio)