AI Agent Orchestration | The Garnet Wiki

AI Agent Orchestration is the coordination of multiple autonomous AI agents—each with distinct capabilities, state, and lifecycles—into a cohesive, scalable workflow. It matters when a single agent cannot handle a complex task end-to-end, or when multiple agents must collaborate across time, data, and decisions, such as in a multi-step customer onboarding pipeline, a real-time anomaly response system, or a continuous AI-driven product development cycle.

Defining the Orchestration Problem

An orchestration system manages the flow of control between agents. It decides when to invoke which agent, how to pass data between them, how to handle failures, and how to maintain state across asynchronous steps. The core challenge is not just *running* agents, but *coordinating* them so that their collective behavior is predictable, resilient, and efficient.

At its simplest, orchestration involves a sequence of agent calls. But real systems require agent coordination that includes:

Conditional branching based on agent outputs,
Parallel execution of independent agents,
Reusable subroutines (called *agent workflows*),
Persistent state tracking across agent invocations,
Error handling and retry strategies,
Integration with external systems (databases, APIs, message queues).

Launching and Managing Agent Workflows

To start a workflow, an orchestration engine accepts a request with a unique workflow_id, a start_agent field, and a parameters object.

POST /v1/workflows
{
  "workflow_id": "onboarding-38472",
  "start_agent": "customer-verification",
  "parameters": {
    "user_id": "u-12345",
    "source": "web-signup"
  }
}

The orchestration engine creates a WorkflowInstance record in a workflows table:

CREATE TABLE workflows (
  id UUID PRIMARY KEY,
  status TEXT CHECK (status IN ('pending', 'running', 'failed', 'completed')),
  started_at TIMESTAMPTZ,
  completed_at TIMESTAMPTZ,
  start_agent TEXT,
  parameters JSONB,
  state JSONB,
  metadata JSONB
);

Each agent invocation is a WorkflowStep:

CREATE TABLE workflow_steps (
  id UUID PRIMARY KEY,
  workflow_id UUID REFERENCES workflows(id),
  agent_name TEXT,
  status TEXT CHECK (status IN ('pending', 'running', 'success', 'failed')),
  input_data JSONB,
  output_data JSONB,
  started_at TIMESTAMPTZ,
  completed_at TIMESTAMPTZ,
  retry_count INTEGER DEFAULT 0
);

When an agent completes, it updates its step and triggers the next step. If an agent fails, the orchestration engine records the error and decides whether to retry, skip, or abort.

Configuring Workflow Logic

Orchestration logic is defined in a declarative format, typically JSON or YAML. A workflow definition describes the control flow and agent configuration.

name: customer-onboarding
description: "Verify, onboard, and activate a new customer"
start: customer-verification

steps:
  - name: customer-verification
    agent: "verification-agent"
    timeout: 30s
    retry:
      max_attempts: 3
      backoff: "exponential"
      delay: "1s"
    condition:
      type: "json-path"
      expression: "$.verified"
      value: true
    next: "credit-check"

  - name: credit-check
    agent: "credit-scoring-agent"
    parallel: true
    input:
      user_id: "{{ workflow.user_id }}"
    on_success:
      next: "send-welcome-email"
    on_failure:
      next: "escalate-to-support"

  - name: send-welcome-email
    agent: "email-agent"
    input:
      to: "{{ step.credit-check.output.email }}"
      subject: "Welcome to our platform"
      body: "Thanks for signing up!"
    depends_on:
      - "credit-check"

The condition block uses a JSON path expression to evaluate whether the next step should run. The depends_on field ensures a step waits for specified prior steps to complete.

Handling Parallelism and Dependencies

Parallel execution is common when multiple agents can work independently. The orchestration engine tracks dependencies using depends_on and parallel.

When a step has parallel: true, the engine starts the agent immediately and continues to the next step without waiting. The depends_on field can be a list of step names or a list of workflow_step IDs.

{
  "name": "send-welcome-email",
  "agent": "email-agent",
  "depends_on": [
    "credit-check",
    "profile-setup"
  ],
  "input": {
    "to": "{{ step.credit-check.output.email }}",
    "body": "Welcome! Your profile is ready."
  }
}

If profile-setup completes before credit-check, the email agent starts as soon as both are ready. The engine uses a dependency graph stored in a step_dependencies table:

CREATE TABLE step_dependencies (
  step_id UUID REFERENCES workflow_steps(id),
  depends_on_id UUID REFERENCES workflow_steps(id),
  created_at TIMESTAMPTZ
);

When all dependencies are met, the step is marked ready, and the orchestration engine dispatches it to the agent.

State Management and Persistence

State is stored in the state field of the workflows table. It is a JSON object that evolves across steps.

After customer-verification, the state might look like:

{
  "user": {
    "id": "u-12345",
    "verified": true
  },
  "verification": {
    "result": "approved",
    "score": 0.92,
    "timestamp": "2024-05-12T14:30:00Z"
  }
}

Agents access state via a get_state(key) function. The email-agent might use:

def send_welcome_email():
    state = get_state("user")
    profile = get_state("profile")
    send_email(
        to=state["email"],
        subject=f"Welcome, {profile['name']}",
        body=f"Your account is set up. You're ready to go."
    )

State can be updated after a step:

PATCH /v1/workflows/{workflow_id}
{
  "state": {
    "credit_score": 780,
    "risk_level": "low"
  }
}

The orchestration engine merges the new state with the existing one, preserving previous values.

Error Handling and Recovery

Agent failure is signaled via HTTP status codes and response format. A failed agent returns 4xx or 5xx and includes an error object in the response body.

{
  "status": "failed",
  "error": {
    "code": "verification_timeout",
    "message": "Verification took longer than 30s",
    "details": {
      "timeout": 30,
      "actual": 42
    }
  },
  "output": {
    "verified": false,
    "reason": "ID verification failed"
  }
}

The orchestration engine records this in the workflow_steps table and triggers recovery logic.

Retry strategies are configured per step. The backoff field supports:

"linear", where each retry waits delay more seconds,
"exponential", doubling the delay each time,
"constant", using the same delay.

The orchestration engine uses a retry_queue table:

CREATE TABLE retry_queue (
  id UUID PRIMARY KEY,
  step_id UUID REFERENCES workflow_steps(id),
  attempt_number INTEGER,
  scheduled_at TIMESTAMPTZ,
  failure_reason TEXT,
  payload JSONB
);

When a step fails, the engine inserts a retry record. A background worker polls this queue and re-queues steps based on scheduled_at.

Subworkflows and Reusability

Complex workflows often reuse smaller, well-defined sequences. A subworkflow is a named, parameterized workflow that can be invoked as a step.

name: send-welcome-email
description: "Send a personalized welcome email"
parameters:
  - user_id
  - template

steps:
  - agent: "email-agent"
    input:
      to: "{{ parameters.user_id }}@company.com"
      subject: "Welcome to {{ parameters.template }}"
      body: "{{ get_template(parameters.template) }}"

This subworkflow is invoked in a parent workflow with a call step:

- name: send-welcome-email
  agent: "subworkflow"
  input:
    workflow: "send-welcome-email"
    parameters:
      user_id: "{{ workflow.user_id }}"
      template: "standard"
  on_success:
    next: "assign-tutorial"

The orchestration engine creates a new WorkflowInstance for the subworkflow and links it via parent_workflow_id. The subworkflow’s state is accessible in the parent.

Monitoring and Debugging

When a workflow fails, engineers inspect the workflow_steps and workflows tables. The state field often contains diagnostic data.

A common failure is *state drift*: an agent writes to state but the orchestration engine does not persist the change, causing inconsistencies.

Another silent failure: step dependency resolution delays. If a step depends on two others, but one completes and the other is delayed, the orchestration engine waits for the last one. But if the second step fails and is retried, the first step might be processed multiple times.

A frequent error is missing or incorrect dependency keys in depends_on. The engine expects step names, but a typo like credit-check vs credit-checker causes the email agent to wait forever.

The orchestration engine also logs a trace_id for each workflow instance. This ID is passed through all agent calls, allowing full request tracing across services.

{
  "trace_id": "trace-7a8b9c",
  "workflow_id": "onboarding-38472",
  "step_id": "step-12345"
}

Agents include this in their HTTP headers:

X-Trace-ID: trace-7a8b9c
X-Workflow-ID: onboarding-38472
X-Step-ID: step-12345

Failure Modes and Pitfalls

Orchestration engine stalls on step dependencies: a step is ready but not processed. Cause: queue worker down or database lock contention.
State not updated after step completion: the agent returns {"status": "success"} but the state field is not updated in workflows. Cause: missing PATCH call or incorrect workflow_id in the agent’s payload.
Retry loop goes infinite: retry_count increases but max_attempts is never checked. Cause: max_attempts is in the wrong level (per workflow vs per step).
Subworkflow state not merged: a subworkflow updates its own state, but the parent workflow does not include those values in its state. Cause: subworkflow returns state but parent does not merge it.
Incorrect on_success and on_failure routing: a step has on_success: next: "task" but next is task-1, and task-1 is not defined. Cause: typo in step name or missing step definition.

Key Principles

Use workflow_id as the primary coordination key across all services.
Define a standard workflow_state format: a JSON object with input, output, metadata, and steps.
Ensure all agents use trace_id and workflow_id in logs and headers.
Use state as the shared memory between agents.
Treat workflow_steps as the source of truth for the orchestration process.

Orchestration is not just a pipeline; it is a living, evolving process where agents collaborate, learn, and adapt—each step a node in a network of AI-driven decisions.

This page was rewritten on 17 October 2026. It replaced a templated version that was not specific to its own title. How these pages are written and checked. If anything here is wrong, tell us at [email protected] and we will correct it.