Layer 6: Orchestration

Overview

The Orchestration Layer (L6) enables coordination of multiple agents working together as teams, managing complex workflows, and achieving goals collaboratively. This layer is required for ARAL-ORCH profile.

users Team Management

Form and manage agent teams

diagram-project Workflow Engine

Execute complex multi-step workflows

scale-balanced Load Balancing

Distribute work across agents

handshake Consensus

Aggregate decisions from multiple agents

Team Architecture

Team Definition

interface Team {
  id: string
  name: string
  members: TeamMember[]
  coordinator: string  // Agent ID of coordinator
  workflowStrategy: WorkflowStrategy
  consensusThreshold: number
}

interface TeamMember {
  agentId: string
  role: string
  capabilities: string[]
  weight: number  // For consensus voting
}

const developmentTeam: Team = {
  id: 'dev-team-001',
  name: 'Software Development Team',
  members: [
    {
      agentId: 'architect-001',
      role: 'System Architect',
      capabilities: ['design', 'architecture', 'review'],
      weight: 1.5
    },
    {
      agentId: 'developer-001',
      role: 'Senior Developer',
      capabilities: ['coding', 'testing', 'refactoring'],
      weight: 1.0
    },
    {
      agentId: 'tester-001',
      role: 'QA Engineer',
      capabilities: ['testing', 'validation', 'reporting'],
      weight: 1.0
    }
  ],
  coordinator: 'architect-001',
  workflowStrategy: 'sequential',
  consensusThreshold: 0.67
}

Orchestrator

class TeamOrchestrator {
  async executeTask(
    task: Task,
    team: Team
  ): Promise<TaskResult> {
    // 1. Analyze task and assign to appropriate agent(s)
    const assignments = await this.assignTask(task, team)

    // 2. Execute based on workflow strategy
    let results: AgentResult[]

    switch (team.workflowStrategy) {
      case 'sequential':
        results = await this.executeSequential(assignments)
        break

      case 'parallel':
        results = await this.executeParallel(assignments)
        break

      case 'consensus':
        results = await this.executeConsensus(assignments)
        break
    }

    // 3. Aggregate results
    const finalResult = await this.aggregateResults(
      results,
      team.consensusThreshold
    )

    return finalResult
  }

  private async executeSequential(
    assignments: Assignment[]
  ): Promise<AgentResult[]> {
    const results: AgentResult[] = []

    for (const assignment of assignments) {
      const agent = await this.getAgent(assignment.agentId)
      const result = await agent.execute(assignment.task)

      results.push(result)

      // Pass result to next agent as context
      if (assignment.next) {
        assignment.next.context = result
      }
    }

    return results
  }

  private async executeParallel(
    assignments: Assignment[]
  ): Promise<AgentResult[]> {
    const promises = assignments.map(async assignment => {
      const agent = await this.getAgent(assignment.agentId)
      return agent.execute(assignment.task)
    })

    return Promise.all(promises)
  }

  private async executeConsensus(
    assignments: Assignment[]
  ): Promise<AgentResult[]> {
    // All agents work on same task
    const results = await this.executeParallel(assignments)

    // Find consensus
    const consensus = await this.findConsensus(results)

    return [consensus]
  }
}

class TeamOrchestrator:
    async def execute_task(
        self,
        task: Task,
        team: Team
    ) -> TaskResult:
        # 1. Analyze and assign
        assignments = await self.assign_task(task, team)

        # 2. Execute based on strategy
        if team.workflow_strategy == 'sequential':
            results = await self.execute_sequential(assignments)
        elif team.workflow_strategy == 'parallel':
            results = await self.execute_parallel(assignments)
        elif team.workflow_strategy == 'consensus':
            results = await self.execute_consensus(assignments)

        # 3. Aggregate results
        final_result = await self.aggregate_results(
            results,
            team.consensus_threshold
        )

        return final_result

    async def execute_sequential(
        self,
        assignments: List[Assignment]
    ) -> List[AgentResult]:
        results = []

        for assignment in assignments:
            agent = await self.get_agent(assignment.agent_id)
            result = await agent.execute(assignment.task)

            results.append(result)

            # Pass result to next agent
            if assignment.next:
                assignment.next.context = result

        return results

Workflow Strategies

arrow-right Sequential Workflow

Agents execute one after another, passing results forward.

// Example: Code Review Pipeline
const codeReviewWorkflow: Workflow = {
  steps: [
    {
      agent: 'architect',
      action: 'review-design',
      output: 'designFeedback'
    },
    {
      agent: 'developer',
      action: 'implement-changes',
      input: 'designFeedback',
      output: 'implementation'
    },
    {
      agent: 'tester',
      action: 'run-tests',
      input: 'implementation',
      output: 'testResults'
    }
  ]
}

code-fork Parallel Workflow

Multiple agents work simultaneously on different parts.

// Example: Parallel Analysis
const analysisWorkflow: Workflow = {
  steps: [
    {
      agent: 'security-agent',
      action: 'security-scan',
      parallel: true
    },
    {
      agent: 'performance-agent',
      action: 'performance-test',
      parallel: true
    },
    {
      agent: 'quality-agent',
      action: 'code-quality-check',
      parallel: true
    }
  ],
  aggregation: 'combine-reports'
}

users Consensus Workflow

Multiple agents vote or agree on decisions.

// Example: Decision Making
const decisionWorkflow: Workflow = {
  mode: 'consensus',
  agents: ['expert-1', 'expert-2', 'expert-3'],
  task: 'evaluate-proposal',
  consensusThreshold: 0.67,
  tieBreaker: 'expert-1'
}

sitemap Hierarchical Workflow

Supervisor agent manages worker agents.

// Example: Project Management
const projectWorkflow: Workflow = {
  mode: 'hierarchical',
  supervisor: 'project-manager',
  workers: [
    { agent: 'developer-1', capacity: 10 },
    { agent: 'developer-2', capacity: 8 },
    { agent: 'designer', capacity: 5 }
  ],
  allocation: 'by-capacity'
}

Consensus Mechanisms

class ConsensusEngine {
  async findConsensus(
    results: AgentResult[],
    threshold: number
  ): Promise<AgentResult> {
    // Weight votes by agent expertise
    const weightedVotes = results.map(r => ({
      result: r,
      weight: this.getAgentWeight(r.agentId)
    }))

    // Find majority agreement
    const grouped = this.groupByOutcome(weightedVotes)
    const totalWeight = weightedVotes.reduce((sum, v) => sum + v.weight, 0)

    for (const [outcome, votes] of grouped) {
      const voteWeight = votes.reduce((sum, v) => sum + v.weight, 0)
      const agreement = voteWeight / totalWeight

      if (agreement >= threshold) {
        return {
          outcome,
          consensus: true,
          agreement,
          votes: votes.length,
          dissent: results.length - votes.length
        }
      }
    }

    // No consensus reached
    return {
      outcome: 'no-consensus',
      consensus: false,
      agreement: 0,
      suggestions: this.getDissenting Opinions(results)
    }
  }
}

Load Balancing

class LoadBalancer {
  async distributeWork(
    tasks: Task[],
    agents: Agent[]
  ): Promise<Map<string, Task[]>> {
    const assignments = new Map<string, Task[]>()

    // Get current load for each agent
    const agentLoads = await Promise.all(
      agents.map(async a => ({
        agent: a,
        load: await a.getCurrentLoad(),
        capacity: a.getCapacity()
      }))
    )

    // Sort agents by available capacity
    agentLoads.sort((a, b) =>
      (a.capacity - a.load) - (b.capacity - b.load)
    )

    // Distribute tasks
    for (const task of tasks) {
      // Find agent with most available capacity
      const bestAgent = agentLoads.find(a =>
        a.load + task.estimatedLoad <= a.capacity
      )

      if (bestAgent) {
        if (!assignments.has(bestAgent.agent.id)) {
          assignments.set(bestAgent.agent.id, [])
        }
        assignments.get(bestAgent.agent.id)!.push(task)
        bestAgent.load += task.estimatedLoad
      } else {
        // Queue for later or scale up
        await this.queueOrScale(task)
      }
    }

    return assignments
  }
}

Configuration Example

{
  "layers": {
    "orchestration": {
      "teams": [
        {
          "id": "dev-team",
          "name": "Development Team",
          "members": [
            {
              "agentId": "architect",
              "role": "lead",
              "weight": 1.5
            },
            {
              "agentId": "developer",
              "role": "implementer",
              "weight": 1.0
            }
          ],
          "workflow": {
            "strategy": "sequential",
            "steps": [
              {
                "agent": "architect",
                "action": "design"
              },
              {
                "agent": "developer",
                "action": "implement"
              }
            ]
          }
        }
      ],
      "loadBalancing": {
        "enabled": true,
        "strategy": "least-loaded",
        "healthCheck": {
          "interval": 10000,
          "timeout": 5000
        }
      },
      "consensus": {
        "defaultThreshold": 0.67,
        "tieBreaker": "coordinator"
      }
    }
  }
}