How It Works - IncidentFox

Architecture Overview

IncidentFox uses a sophisticated dual-runtime architecture with two complementary agent systems:

OpenAI SDK Agent - Multi-agent orchestration for production automation
Claude SDK SRE Agent - Interactive debugging with Kubernetes sandbox isolation

Both systems share access to 300+ tools and the RAPTOR knowledge base.

Dual Agent Runtimes

OpenAI SDK Agent (Production Automation)

The production agent uses multi-agent orchestration where specialized agents collaborate: Key characteristics:

Automated investigation triggered by webhooks
Multi-agent orchestration for parallel data gathering
Optimized for production incident response
Async execution with status updates

Claude SDK SRE Agent (Interactive Debugging)

The interactive agent provides hands-on debugging with enhanced security: Key characteristics:

Kubernetes sandbox isolation with gVisor
Interactive mode with interrupt/resume support
Streaming responses for real-time feedback
Credentials proxy (Envoy) - secrets never touch agent
Ideal for complex, exploratory investigations

The Agents

Planner Agent

The Planner is the orchestrator. When you trigger an investigation, it:

Analyzes the request - Understands what you’re asking
Creates a plan - Determines which agents and tools are needed
Delegates tasks - Assigns work to specialized agents
Synthesizes results - Combines findings into a coherent response

The Planner doesn’t execute tools directly. It coordinates other agents that have the specialized capabilities.

K8s Agent

Specializes in Kubernetes troubleshooting with 9 dedicated tools:

Tool	Description
`get_pod_logs`	Fetch container logs from pods
`describe_pod`	Get pod status, events, and configuration
`list_pods`	List pods in a namespace with status
`get_pod_events`	Get Kubernetes events for pods
`describe_deployment`	Get deployment status and replica info
`get_deployment_history`	View rollout history
`describe_service`	Get service details and endpoints
`get_pod_resource_usage`	CPU/memory usage metrics
`propose_pod_restart`	Suggest pod restart with approval workflow

AWS Agent

Handles AWS infrastructure debugging with 8+ tools:

Tool	Description
`describe_ec2_instance`	EC2 instance details and status
`get_cloudwatch_logs`	Fetch logs from CloudWatch Log Groups
`describe_lambda_function`	Lambda configuration and metrics
`get_rds_instance_status`	RDS database status and metrics
`query_cloudwatch_insights`	Run CloudWatch Insights queries
`get_cloudwatch_metrics`	Query CloudWatch metrics
`list_ecs_tasks`	List ECS Fargate tasks
`describe_codepipeline`	Get CodePipeline execution status

Metrics Agent

Focuses on anomaly detection and correlation with 22+ tools including:

Anomaly Detection - Z-score, Prophet-based seasonal detection
Correlation Analysis - Pearson correlation between metrics
Change Point Detection - Identify when metrics behavior changed
Forecasting - Prophet-based capacity planning
Trend Decomposition - Separate trend, seasonality, and residuals

Coding Agent

Handles code analysis and CI/CD with 15+ tools:

File Operations - Read, search, and analyze code
Git Operations - Diff, blame, log, recent changes analysis
GitHub Integration - PR analysis, code search, commit correlation
Test Execution - Run tests and analyze failures

Investigation Agent

The “jack of all trades” agent with access to all 300+ tools. Used for complex, cross-domain investigations that require multiple types of analysis.

RAPTOR Knowledge Base

IncidentFox uses RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval), a hierarchical knowledge system based on ICLR 2024 research.

Why RAPTOR?

Traditional RAG (Retrieval Augmented Generation) struggles with:

Long documents (100+ page runbooks)
Complex relationships between concepts
Multi-level reasoning

RAPTOR solves this with hierarchical abstraction:

Knowledge Types

Level	Type	Description
L1	Procedural	Step-by-step runbooks
L2	Factual	Service configurations, thresholds
L3	Temporal	Past incidents, timelines
L4	Policy	Escalation rules, SLAs

Knowledge Graph

Beyond tree structure, IncidentFox maintains a knowledge graph for:

Service dependencies
Team ownership
Expertise mapping
Related investigations

Learning from Investigations

IncidentFox improves over time by:

Recording patterns - Stores cause-solution pairs from successful investigations
Finding similar incidents - Matches new issues to past investigations
Importance scoring - Uses 9+ signals to rank knowledge relevance
Contextual boosts - Adjusts relevance based on current investigation context

Investigation Flow

Here’s what happens when you trigger an investigation:

Trigger Received

User mentions @incidentfox in Slack with a request like “investigate high latency in payments service”

Knowledge Retrieval

RAPTOR retrieves relevant runbooks, past incidents, and service documentation

Planner Activates

The Planner agent analyzes the request and determines:

What systems might be involved (payments, database, etc.)
What data sources to query (logs, metrics, recent changes)
Which specialized agents to involve

Data Gathering

Specialized agents execute their tools in parallel:

K8s Agent checks pod status and logs
AWS Agent queries CloudWatch metrics
Metrics Agent runs anomaly detection
Git tools correlate with recent deployments

Correlation

The Investigation Agent correlates findings:

Timeline reconstruction
Root cause identification
Impact assessment (blast radius)

Response

Results are synthesized and posted back to Slack with:

Summary of findings
Root cause with confidence score
Evidence (logs, metrics, events)
Recommended actions

Learning

If the investigation was successful, patterns are recorded for future use

Investigation Results

Investigations return structured results:

{
  "success": true,
  "findings": "Payment service latency spike correlated with database connection exhaustion",
  "root_cause": "RDS connection pool at 100% capacity following deployment",
  "confidence": 92,
  "recommendations": [
    "Increase max_connections on RDS",
    "Review connection pool settings in app config"
  ],
  "phase_results": {
    "data_collection": "completed",
    "anomaly_detection": "completed",
    "root_cause_analysis": "completed",
    "impact_assessment": "completed"
  },
  "duration_seconds": 45.2
}

Finding Types

Type	Description
`metric_anomaly`	Unusual metric behavior detected
`log_error`	Error pattern in logs
`event`	System event correlation
`hypothesis`	Generated theory about root cause
`contradiction`	Conflicting evidence detected

Configuration Inheritance

IncidentFox uses hierarchical configuration that flows from organization to team level: Each level can override settings from the level above. This allows:

Org-wide defaults - Set sensible defaults for all teams
Business unit settings - Configure for platform vs. application teams
Team overrides - Fine-tune for specific team needs

Example Configuration Flow

// Organization level
{
  "mcp_servers": ["grafana", "aws"],
  "agents": {
    "investigation_agent": {
      "prompt": "You are an SRE investigation agent..."
    }
  }
}

// Team level override
{
  "mcp_servers": ["grafana", "aws", "coralogix"],  // Added coralogix
  "agents": {
    "investigation_agent": {
      "enable_extra_tools": ["snowflake"]  // Team-specific
    }
  }
}

The team’s effective config merges both, so they get:

All three MCP servers
The org’s base prompt
The snowflake tool enabled

Data Flow

Triggers send investigation requests to the Orchestrator
Orchestrator routes to appropriate agent runtime
Config Service provides team-specific configuration
Knowledge Base provides relevant context
Tools query external data sources
Results flow back through the agent to the trigger source

Tool Loading

Tools are loaded dynamically based on:

Installation - Is the integration package installed?
Configuration - Are credentials configured?
Team Settings - Is the tool enabled for this team?

# Example: Tool loading logic
if is_integration_available("coralogix"):
    if config.coralogix.api_key:
        if "coralogix" not in config.disabled_tools:
            load_coralogix_tools()

This means teams only see tools relevant to their stack.

MCP Integration

IncidentFox supports the Model Context Protocol (MCP) for extending capabilities with custom tools.

What is MCP?

MCP is an open protocol that allows AI agents to access external tools and data sources in a standardized way. IncidentFox is compatible with 100+ MCP servers.

Using MCP with IncidentFox

Configure MCP servers at org or team level
Tools auto-load without code changes
Agents automatically discover and use MCP tools during investigations

{
  "mcp_servers": [
    {
      "name": "internal-tools",
      "url": "mcps://tools.internal.company.com",
      "auth": "vault://secrets/mcp-token"
    }
  ]
}

MCP Inheritance

MCP servers configured at the organization level are inherited by all teams. Teams can add additional servers specific to their needs.

Security Architecture

Credentials Proxy

Secrets never touch the agent directly:

Agent makes API call through Envoy proxy
Envoy intercepts and injects credentials at request time
Secrets are never stored in agent memory

Claude Sandbox Isolation

The Claude SDK SRE Agent runs in an isolated Kubernetes environment:

gVisor - User-space kernel for container isolation
Network policies - Restricted egress
Resource limits - CPU, memory, and time bounds
Ephemeral - Sandboxes destroyed after investigation

Next Steps

Configuration

Learn how to configure agents and tools

Data Sources

Connect your observability stack

Knowledge Base

Configure RAPTOR and teach custom knowledge

Security

Understand security architecture

Getting Started

Core Concepts

Configuration

Integrations

Data Sources

Tools Catalog

​Architecture Overview

​Dual Agent Runtimes

​OpenAI SDK Agent (Production Automation)

​Claude SDK SRE Agent (Interactive Debugging)

​The Agents

​Planner Agent

​K8s Agent

​AWS Agent

​Metrics Agent

​Coding Agent

​Investigation Agent

​RAPTOR Knowledge Base

​Why RAPTOR?

​Knowledge Types

​Knowledge Graph

​Learning from Investigations

​Investigation Flow

​Investigation Results

​Finding Types

​Configuration Inheritance

​Example Configuration Flow

​Data Flow

​Tool Loading

​MCP Integration

​What is MCP?

​Using MCP with IncidentFox

​MCP Inheritance

​Security Architecture

​Credentials Proxy

​Claude Sandbox Isolation

​Next Steps

Configuration

Data Sources

Knowledge Base

Security

Architecture Overview

Dual Agent Runtimes

OpenAI SDK Agent (Production Automation)

Claude SDK SRE Agent (Interactive Debugging)

The Agents

Planner Agent

K8s Agent

AWS Agent

Metrics Agent

Coding Agent

Investigation Agent

RAPTOR Knowledge Base

Why RAPTOR?

Knowledge Types

Knowledge Graph

Learning from Investigations

Investigation Flow

Investigation Results

Finding Types

Configuration Inheritance

Example Configuration Flow

Data Flow

Tool Loading

MCP Integration

What is MCP?

Using MCP with IncidentFox

MCP Inheritance

Security Architecture

Credentials Proxy

Claude Sandbox Isolation

Next Steps