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To build and test goal-driven AI agents, you need an orchestration workflow that scaffolds agent architecture, defines nodes and edges, and executes test suites against success criteria to ensure production readiness.

Automating agent development and debugging requires a coordinated workflow that generates agent structures, runs validation tests to identify runtime issues, and provides actionable recommendations to resolve them.

Workflow orchestration helps with AI agent creation by guiding users through development phases from concept to production, managing structure, node and edge definition, and coordinating specialized sub-skills for testing and debugging.

Yes, you can generate and execute test suites to verify AI agent functionality by validating the agent against predefined success criteria and constraints during the testing phase of the development workflow.

Scaffolding agent architecture involves automating the creation of agent goals, defining the structural nodes, and establishing the edges that connect them to form a repeatable, goal-driven agent build.

You need a debugging workflow for goal-driven agents to identify and resolve runtime issues that arise during initial runs or testing, ensuring the final deployed agent operates without errors.