القائمة
Build production-ready AI agents with Microsoft Foundry and Agent Framework. Covers agent architecture, model selection, orchestration, tracing, and evaluation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | ai-agent-development |
| description | Build production-ready AI agents with Microsoft Foundry and Agent Framework. Covers agent architecture, model selection, orchestration, tracing, and evaluation. |
Purpose: Build production-ready AI agents with Microsoft Foundry and Agent Framework.
Scope: Agent architecture, model selection, orchestration, observability, evaluation.
Python (Recommended):
pip install agent-framework-azure-ai --pre # --pre required during preview
.NET:
dotnet add package Microsoft.Agents.AI.AzureAI --prerelease
dotnet add package Microsoft.Agents.AI.Workflows --prerelease
Top Production Models (Microsoft Foundry):
| Model | Best For | Context | Cost/1M |
|---|---|---|---|
| gpt-5.2 | Enterprise agents, structured outputs | 200K/100K | TBD |
| gpt-5.1-codex-max | Agentic coding workflows | 272K/128K | $3.44 |
| claude-opus-4-5 | Complex agents, coding, computer use | 200K/64K | $10 |
| gpt-5.1 | Multi-step reasoning | 200K/100K | $3.44 |
| o3 | Advanced reasoning | 200K/100K | $3.5 |
Deploy Model: Ctrl+Shift+P → AI Toolkit: Deploy Model
from agent_framework.openai import OpenAIChatClient
client = OpenAIChatClient(
model="gpt-5.1",
api_key=os.getenv("FOUNDRY_API_KEY"),
endpoint=os.getenv("FOUNDRY_ENDPOINT")
)
agent = {
"name": "Assistant",
"instructions": "You are a helpful assistant.",
"tools": [] # Add tools as needed
}
response = await client.chat(
messages=[{"role": "user", "content": "Hello"}],
agent=agent
)
from agent_framework.workflows import SequentialWorkflow
researcher = {"name": "Researcher", "instructions": "Gather information."}
writer = {"name": "Writer", "instructions": "Write based on research."}
workflow = SequentialWorkflow(
agents=[researcher, writer],
handoff_strategy="on_completion"
)
result = await workflow.run(query="Write about AI agents")
Advanced Patterns: Search github.com/microsoft/agent-framework for:
from agent_framework.observability import configure_otel_providers
# Before running agent - must open trace viewer first!
configure_otel_providers(
vs_code_extension_port=4317, # AI Toolkit gRPC port
enable_sensitive_data=True
)
Open Trace Viewer: Ctrl+Shift+P → AI Toolkit: Open Trace Viewer
⚠️ CRITICAL: Open trace viewer BEFORE running your agent.
pip install "azure-ai-projects>=2.0.0b2"
Agent Evaluators:
builtin.intent_resolution - Intent correctly identified?builtin.task_adherence - Instructions followed?builtin.task_completion - Task completed end-to-end?builtin.tool_call_accuracy - Tools used correctly?builtin.tool_selection - Right tools chosen?Quality Evaluators:
builtin.coherence - Natural text flow?builtin.fluency - Grammar correct?builtin.groundedness - Claims substantiated? (RAG)builtin.relevance - Answers key points? (RAG)from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from openai.types.eval_create_params import DataSourceConfigCustom
from openai.types.evals.create_eval_jsonl_run_data_source_param import (
CreateEvalJSONLRunDataSourceParam, SourceFileID
)
endpoint = os.getenv("FOUNDRY_PROJECT_ENDPOINT")
model_deployment = os.getenv("MODEL_DEPLOYMENT_NAME")
with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
):
# 1. Upload Dataset
dataset = project_client.datasets.upload_file(
name="eval-data",
version="1",
file_path="data.jsonl"
)
# 2. Define Data Schema
data_source_config = DataSourceConfigCustom({
"type": "custom",
"item_schema": {
"type": "object",
"properties": {
"query": {"type": "string"},
"response": {"type": "string"}
},
"required": ["query", "response"]
},
"include_sample_schema": True
})
# 3. Define Evaluators
testing_criteria = [
{
"type": "azure_ai_evaluator",
"name": "coherence",
"evaluator_name": "builtin.coherence",
"data_mapping": {
"query": "{{item.query}}",
"response": "{{item.response}}"
},
"initialization_parameters": {"deployment_name": model_deployment}
}
]
# 4. Create Evaluation
evaluation = openai_client.evals.create(
name="agent-eval",
data_source_config=data_source_config,
testing_criteria=testing_criteria
)
# 5. Run Evaluation
run = openai_client.evals.runs.create(
eval_id=evaluation.id,
name="eval-run",
data_source=CreateEvalJSONLRunDataSourceParam(
type="jsonl",
source=SourceFileID(type="file_id", id=dataset.id)
)
)
# 6. Wait for Completion
while run.status not in ["completed", "failed"]:
run = openai_client.evals.runs.retrieve(run_id=run.id, eval_id=evaluation.id)
time.sleep(3)
print(f"Report: {run.report_url}")
Code-based (objective metrics):
code_evaluator = project_client.evaluators.create_version(
name="response_length_check",
evaluator_version={
"name": "response_length_check",
"definition": {
"type": "CODE",
"code_text": """
def grade(sample, item):
length = len(item.get("response", ""))
return 1.0 if 100 <= length <= 500 else 0.5
""",
# ... schema omitted for brevity
}
}
)
Prompt-based (subjective metrics):
prompt_evaluator = project_client.evaluators.create_version(
name="friendliness_check",
evaluator_version={
"name": "friendliness_check",
"definition": {
"type": "PROMPT",
"prompt_text": """
Rate friendliness (1-5):
Query: {{query}}
Response: {{response}}
Output JSON: {"result": <int>, "reason": "<text>"}
""",
# ... schema omitted for brevity
}
}
)
✅ DO:
❌ DON'T:
Development
Observability
Evaluation
Security & Compliance
Operations
Official Documentation:
AI Toolkit:
Ctrl+Shift+P → AI Toolkit: Model CatalogCtrl+Shift+P → AI Toolkit: Open Trace ViewerCtrl+Shift+P → AI Toolkit: Model PlaygroundSecurity:
Related: AGENTS.md for agent behavior guidelines • Skills.md for general production practices
Last Updated: January 17, 2026