Scaffold Workflow

Use this workflow to make a Python agent optimizable before running Agent Optimizer in Foundry.

Step 1: Resolve Target and Goal

Stay inside the selected agent root. Confirm the project is Python using requirements.txt, pyproject.toml, setup.py, or Python entrypoints.

Identify the optimization goal from user input, selected evaluationSuites[], .foundry/evaluators/*, recent result summaries, datasets, or code/test comments. If the goal is unclear, proceed conservatively and explain that evaluator-specific targeting improves optimization quality.

Step 2: Inventory Safe Targets

Scan for instructions, model selection, skill folders, function tool definitions, topology, and hosting entrypoint. Record file path, symbol/name, role, current value, and whether it is safe to expose through the optimizer.

Classify topology as single-agent, orchestrator/supervisor, specialist tool-agent, peer multi-agent, or unknown runtime. Do not collapse role-specific prompts into one global prompt. Ask before editing when multiple scopes are plausible.

Use Python Patterns to map evaluator/dataset goals to the smallest useful baseline.

Step 3: Scaffold Baseline Files

Create the required .agent_configs/baseline/ folder in the agent's service source directory (beside the entry point):

.agent_configs/
  baseline/
    metadata.yaml
    instructions.md
    tools.json