Definition

Agent Orchestration is the discipline of coordinating, scheduling, and managing multiple autonomous AI agents to collaborate on complex tasks that exceed the capabilities of any single agent. It encompasses the protocols for inter-agent communication, the strategies for task decomposition and distribution, the mechanisms for conflict resolution and consensus building, the policies for resource allocation and load balancing, and the supervision hierarchies that ensure fault tolerance and recovery. In essence, agent orchestration transforms a collection of independent agents into a coherent, goal-directed system.

Overview

The challenge of agent orchestration arises from a fundamental tension: individual agents are designed to be autonomous and specialized, yet real-world tasks frequently require the coordinated effort of multiple agents with different capabilities. A security assessment might require a reconnaissance agent, a vulnerability scanner, a compliance checker, and a report generator -- each autonomous, each specialized, yet all working toward a unified outcome. Orchestration is the layer that resolves this tension.

The field draws from several foundational disciplines. From distributed systems theory, it inherits concepts of consensus, consistency, and fault tolerance. From workflow engines, it takes task decomposition, dependency graphs, and execution scheduling. From multi-agent systems research, it adopts communication protocols (speech act theory, contract nets), coordination mechanisms (blackboard systems, tuple spaces), and social structures (hierarchies, markets, teams). From OTP/Erlang, it inherits supervision trees, process linking, and "let it crash" fault tolerance.

The Prismatic Platform represents one of the most ambitious agent orchestration deployments in production: 530+ AIAD agents across 16 domains, organized in a five-tier hierarchy (L1 Strategic through L5 Autonomous), coordinated through Elixir/OTP supervision trees and managed by the Archer Supreme meta-orchestrator. The system handles everything from simple sequential pipelines to complex multi-team adversarial exercises with real-time synthesis and closure verification.

Orchestration Patterns

Pattern	Description	Complexity	Platform Usage
Sequential Pipeline	Agents execute in fixed order, output of one feeds input of next	Low	Quality gates, build pipelines
Parallel Fan-Out	Multiple agents execute simultaneously on independent sub-tasks	Medium	OSINT multi-source intelligence gathering
Scatter-Gather	Fan-out + aggregation of results from multiple agents	Medium	Security assessment, compliance checks
Hierarchical Delegation	Commander agents delegate to specialist agents	High	Color Team operations, domain-specific workflows
Blackboard	Shared workspace where agents contribute and react to changes	High	Knowledge synthesis, epistemic analysis
Market-Based	Agents bid on tasks based on capability and availability	Very High	Resource-constrained scheduling
Choreography	Agents coordinate through events without central orchestrator	Very High	Emergent behavior, self-organizing systems

Technical Details

Orchestration Architecture

Agent orchestration in the Prismatic Platform is built on Elixir/OTP primitives, providing battle-tested fault tolerance and concurrency:

defmodule PrismaticAgents.Orchestrator do
  @moduledoc """
  Central orchestration engine for coordinating multi-agent workflows.
  Manages task decomposition, agent selection, execution scheduling,
  and result aggregation with full OTP supervision.
  """

  use GenServer

  @type workflow :: %{
    id: String.t(),
    name: String.t(),
    tasks: list(task()),
    dependencies: %{String.t() => list(String.t())},
    status: :pending | :running | :completed | :failed,
    started_at: DateTime.t() | nil,
    completed_at: DateTime.t() | nil
  }

  @type task :: %{
    id: String.t(),
    agent_type: atom(),
    input: map(),
    output: map() | nil,
    status: :pending | :running | :completed | :failed | :skipped,
    assigned_agent: pid() | nil,
    timeout: non_neg_integer()
  }

  @spec orchestrate(String.t(), map()) :: {:ok, map()} | {:error, term()}
  def orchestrate(workflow_name, params) do
    with {:ok, workflow} <- build_workflow(workflow_name, params),
         {:ok, execution_plan} <- resolve_dependencies(workflow),
         {:ok, results} <- execute_plan(execution_plan) do
      {:ok, %{
        workflow: workflow_name,
        results: results,
        execution_time: calculate_execution_time(workflow),
        agents_involved: count_agents(execution_plan)
      }}
    end
  end

  @spec resolve_dependencies(workflow()) :: {:ok, list(list(task()))} | {:error, term()}
  defp resolve_dependencies(%{tasks: tasks, dependencies: deps}) do
    case topological_sort(tasks, deps) do
      {:ok, sorted_layers} -> {:ok, sorted_layers}
      {:error, :cycle_detected} -> {:error, :circular_dependency}
    end
  end

  @spec execute_plan(list(list(task()))) :: {:ok, map()} | {:error, term()}
  defp execute_plan(layers) do
    Enum.reduce_while(layers, {:ok, %{}}, fn layer, {:ok, accumulated} ->
      case execute_layer(layer, accumulated) do
        {:ok, layer_results} ->
          {:cont, {:ok, Map.merge(accumulated, layer_results)}}

        {:error, reason} ->
          {:halt, {:error, %{layer: layer, reason: reason, partial: accumulated}}}
      end
    end)
  end

  @spec execute_layer(list(task()), map()) :: {:ok, map()} | {:error, term()}
  defp execute_layer(tasks, context) do
    tasks
    |> Enum.map(fn task ->
      Task.async(fn -> execute_task(task, context) end)
    end)
    |> Task.await_many(timeout_for_layer(tasks))
    |> Enum.reduce({:ok, %{}}, fn
      {:ok, {task_id, result}}, {:ok, acc} -> {:ok, Map.put(acc, task_id, result)}
      {:error, reason}, _acc -> {:error, reason}
      _result, {:error, _} = error -> error
    end)
  end
end

Agent Selection and Routing

The orchestrator must select the best available agent for each task based on capability, availability, and tier authorization:

defmodule PrismaticAgents.AgentSelector do
  @moduledoc """
  Selects optimal agents for task execution based on capability
  matching, availability, tier authorization, and load balancing.
  """

  @spec select(atom(), keyword()) :: {:ok, pid()} | {:error, term()}
  def select(agent_type, opts \\ []) do
    required_tier = Keyword.get(opts, :min_tier, :l5)
    required_capabilities = Keyword.get(opts, :capabilities, [])

    candidates =
      PrismaticAgents.Registry.lookup(agent_type)
      |> Enum.filter(&meets_tier_requirement?(&1, required_tier))
      |> Enum.filter(&has_capabilities?(&1, required_capabilities))
      |> Enum.filter(&is_available?/1)

    case candidates do
      [] ->
        {:error, :no_available_agents}

      agents ->
        selected =
          agents
          |> Enum.map(&score_candidate/1)
          |> Enum.max_by(& &1.score)

        {:ok, selected.pid}
    end
  end

  @spec score_candidate(map()) :: map()
  defp score_candidate(agent) do
    score =
      agent.capability_match * 0.4 +
      agent.availability_score * 0.3 +
      agent.recent_success_rate * 0.2 +
      agent.response_time_score * 0.1

    Map.put(agent, :score, score)
  end
end

Dependency Resolution

Complex workflows have inter-task dependencies that must be resolved into a valid execution order:

defmodule PrismaticAgents.DependencyResolver do
  @moduledoc """
  Resolves task dependencies using topological sorting to produce
  a layered execution plan where each layer can run in parallel.
  """

  @spec topological_sort(list(task()), map()) ::
          {:ok, list(list(task()))} | {:error, :cycle_detected}
  def topological_sort(tasks, dependency_map) do
    task_map = Map.new(tasks, &{&1.id, &1})
    in_degrees = calculate_in_degrees(tasks, dependency_map)

    initial_layer =
      tasks
      |> Enum.filter(fn task -> Map.get(in_degrees, task.id, 0) == 0 end)

    case initial_layer do
      [] when tasks != [] -> {:error, :cycle_detected}
      _ -> build_layers(initial_layer, tasks, dependency_map, in_degrees, task_map, [])
    end
  end

  @spec build_layers(list(task()), list(task()), map(), map(), map(), list(list(task()))) ::
          {:ok, list(list(task()))} | {:error, :cycle_detected}
  defp build_layers([], remaining, _deps, _degrees, _map, layers) when remaining == [] do
    {:ok, Enum.reverse(layers)}
  end

  defp build_layers([], remaining, _deps, _degrees, _map, _layers) when remaining != [] do
    {:error, :cycle_detected}
  end

  defp build_layers(current_layer, remaining, deps, degrees, task_map, layers) do
    current_ids = MapSet.new(Enum.map(current_layer, & &1.id))
    new_remaining = Enum.reject(remaining, &MapSet.member?(current_ids, &1.id))

    updated_degrees =
      Enum.reduce(current_layer, degrees, fn task, acc ->
        dependents = find_dependents(task.id, deps)
        Enum.reduce(dependents, acc, fn dep_id, deg_acc ->
          Map.update(deg_acc, dep_id, 0, &max(&1 - 1, 0))
        end)
      end)

    next_layer =
      new_remaining
      |> Enum.filter(fn task -> Map.get(updated_degrees, task.id, 0) == 0 end)

    build_layers(next_layer, new_remaining, deps, updated_degrees, task_map, [current_layer | layers])
  end
end

Communication Protocols

Agents communicate through structured message passing, using Elixir/OTP's built-in process messaging:

defmodule PrismaticAgents.Protocol do
  @moduledoc """
  Defines the structured communication protocol for inter-agent
  messaging, ensuring type-safe, traceable interactions.
  """

  @type message :: %{
    id: String.t(),
    from: pid(),
    to: pid(),
    type: :request | :response | :event | :directive,
    payload: map(),
    correlation_id: String.t() | nil,
    timestamp: DateTime.t(),
    ttl: non_neg_integer()
  }

  @spec send_request(pid(), atom(), map(), keyword()) :: {:ok, term()} | {:error, term()}
  def send_request(target_agent, action, payload, opts \\ []) do
    timeout = Keyword.get(opts, :timeout, 30_000)
    correlation_id = Keyword.get(opts, :correlation_id, generate_id())

    message = %{
      id: generate_id(),
      from: self(),
      to: target_agent,
      type: :request,
      payload: %{action: action, data: payload},
      correlation_id: correlation_id,
      timestamp: DateTime.utc_now(),
      ttl: timeout
    }

    :telemetry.execute(
      [:prismatic_agents, :protocol, :send],
      %{payload_size: byte_size(:erlang.term_to_binary(payload))},
      %{from: self(), to: target_agent, action: action}
    )

    GenServer.call(target_agent, {:agent_message, message}, timeout)
  end

  @spec broadcast(atom(), atom(), map()) :: :ok
  def broadcast(agent_group, action, payload) do
    PrismaticAgents.Registry.lookup_group(agent_group)
    |> Enum.each(fn agent_pid ->
      send_request(agent_pid, action, payload, timeout: 5_000)
    end)
  end
end

Supervision and Fault Tolerance

Agent orchestration must handle agent failures gracefully. The platform uses OTP supervision trees to ensure automatic recovery:

defmodule PrismaticAgents.OrchestratorSupervisor do
  @moduledoc """
  Supervision tree for the agent orchestration subsystem.
  Implements restart strategies that maintain system stability
  while recovering from individual agent failures.
  """

  use Supervisor

  @spec start_link(keyword()) :: Supervisor.on_start()
  def start_link(opts) do
    Supervisor.start_link(__MODULE__, opts, name: __MODULE__)
  end

  @impl Supervisor
  def init(_opts) do
    children = [
      {PrismaticAgents.Registry, []},
      {PrismaticAgents.Orchestrator, []},
      {PrismaticAgents.Scheduler, []},
      {PrismaticAgents.MetricsCollector, []},
      {DynamicSupervisor,
        name: PrismaticAgents.AgentPool,
        strategy: :one_for_one,
        max_restarts: 100,
        max_seconds: 60}
    ]

    Supervisor.init(children, strategy: :rest_for_one)
  end
end

The Five-Tier Agent Hierarchy

The platform organizes agents into five tiers, each with different orchestration authority and responsibility:

Tier	Level	Authority	Orchestration Role	Example Agents
L1	Strategic	Supreme command	Orchestrates cross-domain campaigns	Archer Supreme, Supreme Coordinator
L2	Tactical	Domain command	Orchestrates within a domain	Red Commander, Blue Commander
L3	Operational	Team command	Orchestrates specialist teams	Purple Coordinator, White Verifier Commander
L4	Specialist	Task execution	Receives orchestrated tasks, executes autonomously	Edge Finder, Drift Detector
L5	Autonomous	Self-directed	Operates independently within narrow scope	Background monitors, health checkers

Implementation in Prismatic Platform

Archer Supreme Meta-Orchestrator

The Archer Supreme is the platform's L1 strategic orchestrator, capable of coordinating any combination of the 530+ agents across all domains:

defmodule PrismaticAgents.ArcherSupreme do
  @moduledoc """
  L1 Strategic meta-orchestrator that coordinates cross-domain
  agent campaigns. Provides 10x efficiency through intelligent
  task decomposition and parallel execution planning.
  """

  use GenServer

  @spec orchestrate_campaign(String.t(), map()) :: {:ok, map()} | {:error, term()}
  def orchestrate_campaign(objective, params) do
    with {:ok, decomposition} <- decompose_objective(objective, params),
         {:ok, agent_assignments} <- assign_agents(decomposition),
         {:ok, execution_plan} <- build_execution_plan(agent_assignments),
         {:ok, results} <- PrismaticAgents.Orchestrator.execute_plan(execution_plan),
         {:ok, synthesis} <- synthesize_results(results) do
      {:ok, %{
        objective: objective,
        agents_coordinated: count_unique_agents(agent_assignments),
        domains_involved: list_domains(agent_assignments),
        results: synthesis,
        execution_time_ms: calculate_total_time(results)
      }}
    end
  end

  @spec decompose_objective(String.t(), map()) :: {:ok, list(map())} | {:error, term()}
  defp decompose_objective(objective, params) do
    objective
    |> classify_objective()
    |> select_decomposition_strategy()
    |> apply_decomposition(params)
  end
end

AIAD Pipeline Orchestration

The AIAD standard defines reusable pipelines that orchestrate sequences of agent operations:

# Example AIAD pipeline definition
pipeline:
  name: "security-assessment"
  version: "2.0.0"
  stages:
    - name: "reconnaissance"
      agents: ["gray-edge-finder", "red-scenario-generator"]
      parallel: true
      timeout: 300s

    - name: "adversarial-simulation"
      agents: ["red-commander", "red-epistemic-attacker"]
      depends_on: ["reconnaissance"]
      parallel: false

    - name: "defense-assessment"
      agents: ["blue-commander", "blue-drift-detector"]
      depends_on: ["adversarial-simulation"]
      parallel: true

    - name: "synthesis"
      agents: ["purple-coordinator", "purple-closure-analyst"]
      depends_on: ["adversarial-simulation", "defense-assessment"]
      parallel: false

    - name: "verification"
      agents: ["white-verifier-commander"]
      depends_on: ["synthesis"]
      parallel: false

Real-Time Orchestration Dashboard

The platform provides LiveView dashboards for monitoring active orchestrations in real time:

Active workflows with per-task status indicators
Agent utilization heatmaps across domains
Dependency graph visualizations
Latency histograms for inter-agent communication
Failure rate tracking with automatic escalation

Comparison with Alternatives

Orchestration Approach	Architecture	Fault Tolerance	Scalability	Best For
Prismatic (OTP-based)	Supervision trees + GenServer	Process-level isolation, automatic restart	Horizontal (Horde), vertical (BEAM)	AI agent systems, fault-tolerant workflows
Apache Airflow	DAG-based task scheduling	Task retry, SLA alerts	Worker pool scaling	Data pipelines, ETL workflows
Temporal	Durable execution, event sourcing	Automatic replay, saga patterns	Worker fleet scaling	Long-running workflows, microservices
Kubernetes Jobs	Container orchestration	Pod restart policies	Cluster autoscaling	Batch processing, infrastructure tasks
LangChain Agents	Chain-based composition	Limited (application-level retry)	Stateless scaling	LLM prompt chaining, simple agent flows
AutoGen	Multi-agent conversation	Conversation-level retry	Session-based	Collaborative AI agent conversations
CrewAI	Role-based agent teams	Task-level retry	Sequential/parallel	Structured multi-agent workflows

The Prismatic Platform's OTP-based approach provides unique advantages: process-level fault isolation (a crashing agent cannot take down the orchestrator), supervision trees that automatically restart failed agents, backpressure mechanisms that prevent overload, and the BEAM VM's preemptive scheduling that ensures fair resource allocation across thousands of concurrent agents.

Best Practices

1. Design for Failure

Assume any agent can fail at any time. Use supervision trees to define restart strategies, implement timeouts on all inter-agent communication, and design idempotent operations that can be safely retried.

2. Minimize Coupling Between Agents

Agents should communicate through well-defined protocols, not through shared state. The Prismatic Platform enforces this through structured message passing and the AIAD protocol specification.

3. Use Hierarchical Orchestration

Flat orchestration with a single coordinator does not scale. Use hierarchical delegation: strategic orchestrators decompose objectives into domain-level tasks, domain commanders decompose into team-level tasks, and team leads decompose into individual agent tasks.

4. Implement Backpressure

When agent demand exceeds capacity, the system must degrade gracefully rather than collapse. Implement bounded queues, rate limiting, and circuit breakers at orchestration boundaries.

5. Monitor and Observe

Instrument all orchestration paths with telemetry. Track task latency, agent utilization, failure rates, and queue depths. The platform's telemetry system emits events at [:prismatic_agents, :orchestrator, *] for comprehensive observability.

6. Version Orchestration Protocols

As agents evolve, their interfaces change. Version communication protocols and support backward compatibility during transitions. The AIAD standard includes version fields in all agent and command specifications.

Common Pitfalls

Single Orchestrator Bottleneck

Routing all coordination through a single process creates a bottleneck and single point of failure. The platform mitigates this through hierarchical orchestration and the DynamicSupervisor pattern for spawning per-workflow orchestrators.

Deadlocks from Circular Dependencies

If Agent A waits for Agent B which waits for Agent A, the system deadlocks. The dependency resolver detects cycles at planning time and rejects workflows with circular dependencies.

Over-Orchestration

Not every task needs multi-agent orchestration. Simple tasks that a single agent can handle should not be decomposed into unnecessary multi-agent workflows. Over-orchestration adds latency and complexity without benefit.

Ignoring Partial Failures

When one agent in a multi-agent workflow fails, the orchestrator must decide: retry, skip, compensate, or abort. Ignoring partial failures leads to inconsistent results. The platform implements configurable failure policies per workflow stage.

Synchronous Communication Everywhere

Blocking on every inter-agent message serializes execution and eliminates the benefits of parallel orchestration. Use asynchronous messaging where possible, with synchronous calls only for dependency-critical interactions.

Use Cases

Cross-Domain Security Assessment

The Archer Supreme orchestrates a full security assessment by coordinating Gray Team (boundary exploration), Red Team (adversarial simulation), Blue Team (defensive assessment), Purple Team (synthesis), and White Team (verification) in a structured pipeline that produces a comprehensive security posture report.

OSINT Multi-Source Intelligence

The OSINT toolbox orchestrates 120+ adapters across Czech, Global, Sanctions, and EU data sources, gathering intelligence from multiple APIs in parallel and synthesizing results into unified entity profiles.

Automated Quality Evolution

The platform's autoevolve system orchestrates quality scanning agents, remediation agents, and verification agents in a continuous loop that identifies and eliminates quality debt without human intervention.

Compliance Assessment Pipeline

NIS2 and ZKB compliance assessments require coordinating asset discovery, vulnerability scanning, configuration auditing, and evidence collection across multiple domains -- all orchestrated through a single compliance assessment workflow.

Orchestration - The general concept of coordinating distributed components that agent orchestration specializes
AIAD - The AI Agent Definition standard that specifies agent interfaces and orchestration protocols
Agent Registry - The discovery and lookup service used by the orchestrator to find available agents
Multi-Agent System - The theoretical foundation for systems of coordinated autonomous agents
Archer Supreme - The L1 strategic meta-orchestrator that coordinates cross-domain campaigns
Agent Tier - The hierarchical authority system that governs orchestration delegation
Supervision Tree - The OTP fault tolerance mechanism underlying agent orchestration
GenServer - The Elixir/OTP abstraction implementing orchestrator and agent processes
Agent - The autonomous software entity being orchestrated
Agent Pool - Dynamic pool of agent processes managed by the orchestration supervisor
AI Orchestration
Task Dispatch

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Created by Tomas Korcak (korczis) | Open Source under GHL

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Agent Orchestration

Definition

Overview

Orchestration Patterns

Technical Details

Orchestration Architecture

Agent Selection and Routing

Dependency Resolution

Communication Protocols

Supervision and Fault Tolerance

The Five-Tier Agent Hierarchy

Implementation in Prismatic Platform

Archer Supreme Meta-Orchestrator

AIAD Pipeline Orchestration

Real-Time Orchestration Dashboard

Comparison with Alternatives

Best Practices

1. Design for Failure

2. Minimize Coupling Between Agents

3. Use Hierarchical Orchestration

4. Implement Backpressure

5. Monitor and Observe

6. Version Orchestration Protocols

Common Pitfalls

Single Orchestrator Bottleneck

Deadlocks from Circular Dependencies

Over-Orchestration

Ignoring Partial Failures

Synchronous Communication Everywhere

Use Cases

Cross-Domain Security Assessment

OSINT Multi-Source Intelligence

Automated Quality Evolution

Compliance Assessment Pipeline

See Also

Connect & Contribute

Glossary

Related Terms

Agent Orchestration

Definition

Overview

Orchestration Patterns

Technical Details

Orchestration Architecture

Agent Selection and Routing

Dependency Resolution

Communication Protocols

Supervision and Fault Tolerance

The Five-Tier Agent Hierarchy

Implementation in Prismatic Platform

Archer Supreme Meta-Orchestrator

AIAD Pipeline Orchestration

Real-Time Orchestration Dashboard

Comparison with Alternatives

Best Practices

1. Design for Failure

2. Minimize Coupling Between Agents

3. Use Hierarchical Orchestration

4. Implement Backpressure

5. Monitor and Observe

6. Version Orchestration Protocols

Common Pitfalls

Single Orchestrator Bottleneck

Deadlocks from Circular Dependencies

Over-Orchestration

Ignoring Partial Failures

Synchronous Communication Everywhere

Use Cases

Cross-Domain Security Assessment

OSINT Multi-Source Intelligence

Automated Quality Evolution

Compliance Assessment Pipeline

Related Concepts

See Also

Connect & Contribute

Glossary

Related Terms