Frequently Asked Questions
Common Questions About Multi-Agent Workflow Design in Python
Everything you need to know before registering.
How do I represent a multi-agent workflow as a Python task graph? +
A multi-agent workflow is represented in Python as a directed acyclic graph where each node is an agent task and each edge is a data dependency between tasks. The task graph is a Python data structure (typically a dictionary of task objects with dependency lists) that the orchestrator traverses to determine the correct execution order. The workshop covers implementing a task graph executor that identifies which tasks can run in parallel versus which must run sequentially based on their declared dependencies.
What Python patterns work best for multi-agent workflow orchestration? +
The most effective Python patterns for multi-agent workflow orchestration are: the pipeline pattern for sequential workflows where each step's output feeds the next, the fan-out/fan-in pattern for parallel workflows where a task is distributed to multiple agents simultaneously and results are collected, and the conditional routing pattern where workflow branches are selected based on agent output content. The workshop implements all three patterns as reusable Python orchestrator components.
How do I design Python interfaces between agents in a multi-agent workflow? +
Python interfaces between agents in a multi-agent workflow use Pydantic models to define typed input and output schemas for each agent. The workflow orchestrator validates that the output schema of one agent is compatible with the input schema of the next agent in the dependency graph before executing the workflow. This schema compatibility checking catches workflow design errors before runtime and produces clear error messages when incompatibilities are found.
How does the Python workflow handle partial completion when one agent fails? +
Partial completion handling in Python multi-agent workflows uses checkpoint-based execution: the orchestrator records each successfully completed task's output in a workflow state store before dispatching the next task. When an agent fails, the orchestrator can retry the failed task, route to a fallback agent, or return a partial result using the outputs of the agents that completed successfully. The Glass-Box logging records which tasks completed and which failed, providing a complete picture of the workflow execution.
How do I test multi-agent workflow designs in Python before production deployment? +
Multi-agent workflow design testing in Python uses a workflow simulator that replaces real MCP agent servers with mock implementations that return configurable test responses. The simulator runs the complete workflow with controlled inputs and verifies the task graph execution order, context routing correctness, failure handling behavior, and output schema conformance at each step. The workshop covers building a workflow simulator that makes workflow design testing fast and deterministic.
Can Python multi-agent workflows be defined declaratively rather than imperatively? +
Yes. The workshop covers both imperative workflow definition (Python code that directly builds the task graph) and declarative workflow definition (a YAML or JSON configuration file that describes the task graph, which the orchestrator loads and executes). Declarative workflow definition makes it easier to modify workflows without code changes and enables non-developer team members to adjust workflow configurations within defined constraints.
