AI-First Design: MCP and Agentic Workflows

Cyclonetix is designed to be AI-first from the ground up, integrating intelligent agents directly into the workflow orchestration engine. This document outlines the architecture and features for MCP (Model Context Protocol) integration and agentic workflow capabilities.

Core Concepts

Agentic Tasks

Tasks in Cyclonetix can be either traditional (shell commands) or agentic (MCP calls). Agentic tasks leverage AI agents to make intelligent decisions, analyze data, and dynamically modify workflow execution.

Evaluation Points with Intelligence

Evaluation points serve as decision nodes where AI agents can:

  • Analyze workflow state and data
  • Make intelligent routing decisions
  • Determine appropriate next steps based on context
  • Modify execution graphs dynamically

High-Speed + Intelligent Flows

Cyclonetix combines high-speed stream processing (millions of events/second) with intelligent reasoning where it matters:

  • Fast path: Traditional stream processing for routine operations
  • Smart path: Agentic analysis for edge cases, exceptions, and complex decisions
  • Human approval gates: Critical decisions requiring human oversight

MCP Integration Architecture

Agent Runtime

  • MCP server runs as sidecar to orchestrator
  • Agent pool for concurrent task processing
  • Task routing between traditional and agentic execution paths
  • Context preservation across agent interactions

Task Types

Traditional Tasks

id: "process_data"
name: "Process Daily Data"
command: "python process_data.py --input ${INPUT_FILE}"
type: "traditional"

Agentic Tasks

id: "analyze_patterns"
name: "Analyze Data Patterns"
command: "mcp://pattern-analyzer"
type: "agentic"
context:
  - data_history
  - system_conditions
  - business_rules
evaluation_point: true

Agent Evaluation Points

Agents can dynamically modify workflow execution by:

  • Adding new tasks based on analysis results
  • Modifying parameters for downstream tasks
  • Branching workflows based on intelligent decisions
  • Updating context for subsequent operations

Use Case Examples

Intelligent Data Processing

workflow: "data_processing"
description: "AI-powered data processing with dynamic response"
tasks:
  - id: "ingest_data"
    type: "traditional"
    command: "kafka-consumer --topic data-stream"
    
  - id: "analyze_patterns"
    type: "agentic" 
    command: "mcp://pattern-analyzer"
    dependencies: ["ingest_data"]
    evaluation_point: true
    context:
      - data_history
      - system_metrics
      - business_rules
    
  - id: "generate_insights"
    type: "conditional"  # Created dynamically by agent
    command: "python generate_insights.py --severity ${SEVERITY}"

Compliance Automation

workflow: "compliance_monitoring"
description: "Automated compliance monitoring and reporting"
tasks:
  - id: "collect_data"
    type: "traditional"
    command: "extract_data.py --date ${DATE}"
    
  - id: "compliance_check"
    type: "agentic"
    command: "mcp://compliance-analyzer"
    dependencies: ["collect_data"]
    evaluation_point: true
    context:
      - regulation_text
      - historical_cases
      - company_policies
    
  - id: "risk_assessment"
    type: "agentic"
    command: "mcp://risk-evaluator"
    dependencies: ["compliance_check"]
    context:
      - compliance_results
      - entity_profile
      - transaction_history

Dynamic Exception Handling

Agents can intelligently route exceptions based on:

  • Severity analysis: Determine if manual review is required
  • Historical patterns: Learn from previous resolutions
  • Context awareness: Consider system conditions, entity risk profiles
  • Escalation logic: Automatically involve appropriate teams

Technical Implementation

Event-Driven Architecture

#[derive(Clone, Debug)]
pub enum CyclonetixEvent {
    // Traditional events
    TaskCompleted { task_id: String, result: TaskResult },
    
    // Agentic events
    AgentAnalysisRequested { task_id: String, context: AgentContext },
    AgentDecisionMade { task_id: String, decision: AgentDecision },
    WorkflowModified { run_id: String, modifications: Vec<GraphModification> },
}

MCP Integration Layer

pub struct MCPIntegration {
    server_pool: MCPServerPool,
    context_manager: ContextManager,
    decision_engine: DecisionEngine,
}

impl MCPIntegration {
    pub async fn execute_agentic_task(
        &self, 
        task: &AgenticTask,
        context: &WorkflowContext
    ) -> Result<AgentDecision, MCPError> {
        // Execute MCP call with context
        // Parse agent response
        // Return decision with graph modifications
    }
}

Agent Decision Format

{
  "decision": "route_for_review",
  "confidence": 0.85,
  "reasoning": "Unusual pattern detected in data processing pipeline",
  "next_tasks": ["escalate_to_team", "generate_detailed_report"],
  "parameters": {
    "escalate_to_team": {
      "priority": "high",
      "reviewer": "senior_analyst"
    }
  },
  "context_updates": {
    "risk_level": "elevated",
    "requires_review": true
  },
  "metadata": {
    "analysis_time_ms": 250,
    "data_sources": ["historical_data", "system_metrics", "entity_profile"]
  }
}

Governance and Compliance

AI Decision Auditing

  • Full audit trail for all agent decisions
  • Reasoning capture for compliance review
  • Human override capabilities
  • Performance metrics and accuracy tracking

Data Classification and Security

  • Context isolation per workflow run
  • Data classification enforcement (Public, Internal, Restricted)
  • PII handling with automatic redaction
  • Secure agent communication via encrypted channels

Human-in-the-Loop Controls

  • Approval gates for high-risk decisions
  • Confidence thresholds for automatic execution
  • Escalation paths for uncertain outcomes
  • Override mechanisms for exceptional cases

Performance Characteristics

Latency Targets

  • Traditional tasks: Sub-5ms scheduling
  • Agentic tasks: Sub-500ms analysis and decision
  • Graph modification: Sub-100ms workflow updates
  • Human approval: Configurable timeout with fallback

Scalability

  • Concurrent agents: Horizontal scaling of MCP servers
  • Context caching: Efficient reuse of analysis results
  • Batch processing: Group similar decisions for efficiency
  • Resource isolation: Prevent agent workloads from impacting core orchestration

Integration Examples

External System Integration

workflow: "system_validation"
tasks:
  - id: "fetch_data"
    command: "external-api --endpoint data --filter ${FILTER}"
    
  - id: "validate_data"
    type: "agentic"
    command: "mcp://data-validator"
    context:
      - data_schemas
      - validation_rules
      - historical_patterns
    evaluation_point: true
    
  - id: "exception_analysis"
    type: "conditional"  # Created by agent if exceptions found
    command: "mcp://exception-analyzer"

Real-Time Monitoring

workflow: "real_time_monitoring"
trigger: "kafka://system-events"
tasks:
  - id: "metric_calculation"
    type: "traditional"
    command: "calculate_metrics.py --input ${INPUT}"
    
  - id: "threshold_analysis"
    type: "agentic"
    command: "mcp://threshold-analyzer"
    context:
      - system_limits
      - historical_variance
      - current_load
    evaluation_point: true

Future Enhancements

Multi-Agent Coordination

  • Agent collaboration on complex decisions
  • Consensus mechanisms for critical choices
  • Specialized agents for different domains (compliance, operations, analysis)

Learning and Adaptation

  • Decision feedback loops for continuous improvement
  • Pattern recognition from historical outcomes
  • Adaptive thresholds based on performance metrics

Advanced Context Management

  • Cross-workflow context sharing
  • Temporal context for time-series analysis
  • External data integration (feeds, news, regulatory updates)

Success Metrics

Performance Metrics

  • Agent decision latency (target: <500ms)
  • Workflow modification time (target: <100ms)
  • Context retrieval efficiency
  • Resource utilization per agent

Business Metrics

  • False positive reduction in monitoring
  • Exception handling automation rate
  • Process review efficiency
  • Human intervention reduction

Quality Metrics

  • Agent decision accuracy
  • Reasoning quality scores
  • Audit trail completeness
  • Compliance adherence

Design Principles

  • Intelligence where it matters: Use AI for complex decisions, traditional processing for routine operations
  • Audit everything: Full traceability for regulatory compliance
  • Human oversight: Always maintain human control and override capabilities
  • Performance first: AI enhancement should not compromise core orchestration speed
  • Context awareness: Agents should understand business context, not just technical data
  • Fail safely: Graceful degradation when AI services are unavailable

This AI-first design positions Cyclonetix as the next generation of workflow orchestration, combining the speed of traditional processing with the intelligence of modern AI agents.

Next Steps