Setting the file. One moment.
Chapter 36 · Microsoft Foundry
Subchapter 36.12
foundry-agent/agent-optimizer/references/python-patterns.mdMarkdown4 KBView on GitHub
Use the Azure SDK optimization package and a local baseline folder. The baseline is file-based; call load_config() without code-level fallback parameters.
Add azure-ai-agentserver-optimization to requirements.txt or the project dependency file:
azure-ai-agentserver-optimizationImport from the SDK namespace:
from azure.ai.agentserver.optimization import load_configCreate .agent_configs/baseline/ in the agent’s service source directory (beside the entry point):
<agent-root>/
main.py
.agent_configs/
baseline/
metadata.yaml
instructions.md
tools.json
skills/<skill-name>/SKILL.mdExample metadata.yaml:
model: <existing-chat-model-deployment-name>
temperature: 0.7
instruction_file: instructions.md
skill_dir: skills
tool_file: tools.jsoninstructions.md contains the selected baseline system/developer instructions. Include only skill folders relevant to the optimization goal.
Choose a model value that already exists as a model deployment in the target Foundry project. Do not assume gpt-4o is available.
Use OpenAI function-calling tool objects under top-level tools. Currently, only function tool definition optimization is supported:
{
"tools": [
{
"type": "function",
"function": {
"name": "lookup_policy",
"description": "Look up the company travel policy.",
"parameters": {
"type": "object",
"properties": {
"dept": {
"type": "string",
"description": "Department name"
}
}
}
}
}
]
}Call load_config() with no defaults:
config = load_config()
instructions = config.compose_instructions()
model = config.modelFor Microsoft Agent Framework:
client = FoundryChatClient(
project_endpoint=project_endpoint,
model=config.model,
credential=credential,
)
agent = Agent(
client=client,
instructions=config.compose_instructions(),
tools=tools,
)Patch optimized function tool definitions through the public helper. It updates matching function docs, descriptions, and parameter descriptions:
config.apply_tool_descriptions(tools)Load skills on demand when the runtime has a safe skill/tool mechanism:
from pathlib import Path
from azure.ai.agentserver.optimization import load_skills_from_dir
skills = load_skills_from_dir(Path(config.skills_dir)) if config.skills_dir else []Use evaluator and dataset goals to decide what belongs in the baseline:
| Signal | Prefer |
|---|---|
relevance, task_adherence | primary instructions and model |
intent_resolution | router/orchestrator instructions |
builtin.tool_call_accuracy | tool-calling instructions and OpenAI function tool definitions |
| safety/groundedness | safety, retrieval, citation, or answer-synthesis instructions |
For multi-agent apps, scaffold the target role’s instructions and related skills/tools. Do not merge unrelated role prompts into one baseline.
The SDK reads optimization context from supported runtime sources. Keep .agent_configs/baseline/ present so default load_config() startup has a local baseline. Use load_config(config_dir="my_configs") only for non-default local config directories, and load_config(required=False) only when the app can intentionally run without optimization config.
azure-ai-agentserver-optimizationfrom azure.ai.agentserver.optimization import load_config succeeds.agent_configs/baseline/metadata.yaml exists and points to existing filesload_config() is called without defaults unless using an intentional config_dir or required=False