SRAO Workflow Orchestrator

From agent graph to running system in minutes, not months. Generate executable DAG code, deploy with fault tolerance, monitor with Prometheus + Grafana.

Part of the SRAO Framework. Takes agent graph output from SRAO-Agent-Graph and turns it into production-ready workflow code.

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The Problem

You've designed your multi-agent system. Now what?

• Which orchestration engine to use? Temporal? Prefect? Airflow?
• How do you handle retries, timeouts, circuit breakers?
• How do you monitor 20+ agents in real time?
• How do you close the feedback loop for continuous improvement?

SRAO Workflow Orchestrator answers all of these with ready-to-use templates and configurations.

What It Does

Stage 4 - Orchestration Execution

• Select the right engine with a decision tree
• Generate DAG code from agent graph definitions
• Configure fault tolerance (retry, circuit breaker, human escalation)
• Standardize agent interfaces (HTTP/gRPC)

Stage 5 - Feedback Optimization

• Record execution logs with metrics (latency, token usage, cost)
• Monitor agent SLAs with Prometheus + Grafana
• Alert on failures, high latency, circuit breaker triggers
• Close the loop: analyze -> update SRM/Agent Graph -> re-deploy

Engine Selection Decision Tree

Need human-in-the-loop or long-running tasks? +-- Yes -> Temporal +-- No -> Mainly Python data pipelines? +-- Yes -> Prefect +-- No -> Already have Airflow infrastructure? +-- Yes -> Airflow +-- No -> Temporal (recommended default)

Code Templates

Engine	Template	Language
Temporal	references/temporal-template.ts	TypeScript
Prefect	references/prefect-template.py	Python
Airflow	references/airflow-template.py	Python
Monitoring	references/monitoring-setup.md	Prometheus + Grafana

Temporal Example (TypeScript)

` ypescript // Auto-generated from agent graph const [t2Result, t3Result] = await Promise.all([ agentExecute({ agent_id: 'AGT-002', task_id: 'T2', input: t1Result }), agentExecute({ agent_id: 'AGT-003', task_id: 'T3', input: t1Result }), ]);

// Conditional branching if (t4Result.output?.severity === 'high') { await notifyHuman({ message: 'High severity alert' }); } `

Prefect Example (Python)

`python @flow(name="workflow-template") def workflow_template(t1_input): t1_result = agent_execute(agent_id="AGT-001", task_id="T1", input_data=t1_input)

# Parallel execution
t2_future = agent_execute.submit(agent_id="AGT-002", ...)
t3_future = agent_execute.submit(agent_id="AGT-003", ...)
t2_result, t3_result = t2_future.result(), t3_future.result()

return agent_execute(agent_id="AGT-004", input_data={...})

`

Fault Tolerance Out of the Box

`yaml retry_policy: max_attempts: 3 backoff: exponential initial_interval_sec: 2

circuit_breaker: failure_threshold: 5 reset_timeout_sec: 30

fallback: strategy: degrade # or alternate_agent / human_escalation `

Monitoring Stack

Included: Prometheus metrics, Grafana dashboards, alert rules, Docker Compose.

`�ash docker-compose up # Starts Prometheus + Grafana + Alertmanager

Grafana at http://localhost:3000 (admin/admin)

Prometheus at http://localhost:9090

`

Key dashboards:

• Agent success rate (5-min window)
• Agent P95 latency by agent_id
• Workflow execution trends
• Token consumption TOP 10

Quick Start

1. Feed agent graph from SRAO-Agent-Graph
2. Select an orchestration engine (or use the decision tree)
3. Generate DAG code from templates
4. Deploy with docker-compose up
5. Monitor at http://localhost:3000

SRAO Framework Pipeline

+----------------------+ +----------------------+ +-----------------------------+ | SRAO-Domain-Modeler |---->| SRAO-Agent-Graph |---->| SRAO-Workflow-Orchestrator | | (Model & Structure) | SRM | (Decompose & Map) | DAG | (Orchestrate & Monitor) | +----------------------+ +----------------------+ +-----------------------------+

License

MIT