Non-conversational AI agents using MLflow

Non-conversational agents process structured inputs to produce specific outputs without maintaining conversation state. Each request is independent and self-contained, making these agents ideal for task-specific operations like document classification, data extraction, batch analysis, and structured question answering.

Unlike conversational agents that manage multi-turn dialogues, non-conversational agents focus on executing well-defined tasks efficiently. This streamlined architecture enables higher throughput for independent requests.

You'll learn how to:

Create a non-conversational agent
Implement comprehensive MLflow tracing and observability
Deploy the agent to Model Serving with automatic trace collection
Set up production monitoring with MLflow 3 scorers

Requirements

Dependencies:

MLflow 3.2.0 or above
databricks-agents 1.2.0 or above
databricks-sdk[openai] for LLM integration
Python 3.10 or above

Workspace access:

Access to Foundation Model APIs (default: Claude 3.7 Sonnet, configurable)
Access to a catalog and schema for registration of the AI model

Python
%pip install --upgrade mlflow[databricks]==3.6.0 pydantic databricks-sdk[openai] databricks-agents databricks-sdk
%restart_python

Example scenario

The agent in this example processes structured questions about financial document content and provides yes/no answers with reasoning. Users provide both the document text and questions directly in the input, eliminating the need for vector search infrastructure in this simplified example. This demonstrates how non-conversational agents can handle well-defined tasks without conversation context.

You can extend this example for production use cases by integrating additional tools and capabilities. Examples include vector search for document retrieval, MCP (Model Context Protocol) tools for external integrations, or other Databricks agents like Genie for structured data access.

Set up the service principal

Non-conversational agents do not support automatic authentication passthrough to write traces from Model Serving. Instead, you must implement custom MLflow 3 tracing integration and handle authentication manually using a service principal.

Create a service principal with OAuth credentials.
Store credentials in a secret scope:

Python
# TODO: Configuration constants - Update these for your environment
CATALOG = "main"
SCHEMA = "default"  # Replace with your schema name
SECRET_SCOPE = "<YOUR_SECRET_SCOPE>"  # Replace with your secret scope name
DATABRICKS_HOST = (
    "https://host.databricks.com"  # Replace with your workspace URL
)

# TODO: If you have not yet stored your service principal's OAuth client id and client secret as Databricks secrets,
# uncomment the following code and replace the <client_id> and <client_secret> with your service principal's id and secret.

# from databricks.sdk import WorkspaceClient

# w = WorkspaceClient()
# w.secrets.put_secret(SECRET_SCOPE, "client_id", string_value ="<YOUR_SERVICE_PRINCIPAL_CLIENT_ID>")
# w.secrets.put_secret(SECRET_SCOPE, "client_secret", string_value ="<YOUR_SERVICE_PRINCIPAL_CLIENT_SECRET>")

Configure the MLflow experiment:

Create the experiment if it does not exist.
Grant the service principal CAN_EDIT permissions to the experiment.

Python
# Mlflow experiment to capture traces
EXPERIMENT_NAME = "/Workspace/Shared/non-conversational"

# LLM Configuration
LLM_MODEL = "databricks-claude-3-7-sonnet"  # Change this to use different models

# Model and endpoint names - do not need to be changed
MODEL_NAME = "document_analyser"
ENDPOINT_NAME = "document_analyser_agent"
REGISTERED_MODEL_NAME = f"{CATALOG}.{SCHEMA}.{MODEL_NAME}"

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.ml import ExperimentAccessControlRequest
from databricks.sdk.service.iam import PermissionLevel
import mlflow

# Set experiment and get the experiment object directly
experiment = mlflow.set_experiment(EXPERIMENT_NAME)
experiment_id = experiment.experiment_id

# Fetch the service principal client_id from secret scope
client_id = dbutils.secrets.get(scope=SECRET_SCOPE, key="client_id")

# Set permissions for the SPN which will later write the traces from the serving endpoint
w = WorkspaceClient()
# Set CAN_EDIT permissions for the service principal
w.experiments.set_permissions(
    experiment_id=experiment_id,
    access_control_list=[
        ExperimentAccessControlRequest(
            service_principal_name=client_id,
            permission_level=PermissionLevel.CAN_EDIT
        )
    ]
)

print(f"✓ CAN_EDIT permissions granted to SPN {client_id[:8]}... for experiment: {experiment_id}")

Input and output format

Unlike conversational agents that use flexible chat message formats, non-conversational agents require structured Pydantic models for inputs and outputs:

Create input schemas with all required fields for task execution.
Include trace metadata (trace_id, span_id) in output schemas to enable feedback logging.
Design outputs that provide detailed reasoning or chain-of-thought explanations when appropriate.
Validate schemas during development to catch errors before deployment.

Input format (`AgentInput`)

JSON
{
  "document_text": "Document content to analyze...",
  "questions": [
    { "text": "Do the documents contain a balance sheet?" },
    { "text": "Do the documents contain an income statement?" },
    { "text": "Do the documents contain a cash flow statement?" }
  ]
}

Output format (`AgentOutput`)

JSON
{
  "results": [
    {
      "question_text": "Do the documents contain a balance sheet?",
      "answer": "Yes",
      "chain_of_thought": "Detailed reasoning for the answer...",
      "span_id": "abc123def456"
    }
  ],
  "trace_id": "tr-xyz789abc123"
}

Structured Input: Users provide both document text and questions in a single request
Detailed Reasoning: Each answer includes step-by-step chain of thought
Traceability: Response includes trace_id and span_id for feedback collection

Build the non-conversational agent

Create the non-conversational agent with MLflow tracing. The agent uses @mlflow.trace decorators to automatically capture LLM calls and the complete request flow for full observability.

Users provide both the document text and questions directly in the input.

Python
%%writefile model.py
import json
import logging
from typing import Optional
import uuid
import os
import sys

from databricks.sdk import WorkspaceClient
import mlflow
from mlflow.pyfunc import PythonModel
from mlflow.tracing import set_destination
from mlflow.tracing.destination import Databricks
from mlflow.entities import SpanType

from pydantic import BaseModel, Field


class Question(BaseModel):
    """Represents a question in the input."""

    text: str = Field(..., description="Yes/no question about document content")


class AgentInput(BaseModel):
    """Input model for the document analyser agent."""

    document_text: str = Field(..., description="The document text to analyze")
    questions: list[Question] = Field(..., description="List of yes/no questions")


class Answer(BaseModel):
    """Represents a structured response from the LLM."""

    answer: str = Field(..., description="Yes or No answer")
    chain_of_thought: str = Field(..., description="Step-by-step reasoning for the answer")


class AnalysisResult(BaseModel):
    """Represents an analysis result in the output."""

    question_text: str = Field(..., description="Original question text")
    answer: str = Field(..., description="Yes or No answer")
    chain_of_thought: str = Field(..., description="Step-by-step reasoning for the answer")
    span_id: str | None = Field(None, description="MLflow span ID for this specific answer (None during offline evaluation)")


class AgentOutput(BaseModel):
    """Output model for the document analyser agent."""

    results: list[AnalysisResult] = Field(..., description="List of analysis results")
    trace_id: str | None = Field(None, description="MLflow trace ID for user feedback collection (None during offline evaluation)")


class DocumentAnalyser(PythonModel):
    """Non-conversational agent for document analysis using MLflow model serving."""

    def __init__(self) -> None:
        """Initialize the document analyser.

        Sets up logging configuration, initializes model properties, and prepares
        the model for serving.
        """
        self._setup_logging()
        self.model_name = "document_analyser_v1"
        self.logger.debug(f"Initialized {self.model_name}")

    def _setup_logging(self) -> None:
        """Set up logging configuration for Model Serving.

        Configures a logger that uses stderr for better visibility in Model Serving
        environments. Log level can be controlled via MODEL_LOG_LEVEL environment
        variable (defaults to INFO).
        """
        self.logger = logging.getLogger("ModelLogger")
        # Set log level from environment variable or default to INFO
        log_level = os.getenv("MODEL_LOG_LEVEL", "INFO").upper()
        self.logger.setLevel(getattr(logging, log_level, logging.INFO))
        if not self.logger.handlers:
            handler = logging.StreamHandler()
            handler.setLevel(getattr(logging, log_level, logging.INFO))
            formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
            handler.setFormatter(formatter)
            self.logger.addHandler(handler)

    def load_context(self, context) -> None:
        """Load model context and initialize clients.

        This method is called once when the model is loaded in the serving environment.
        It sets up MLflow tracing destination, initializes the Databricks workspace
        client, and configures the OpenAI-compatible client for LLM inference.

        Args:
            context: MLflow model context containing artifacts and configuration
        """
        self.logger.debug("Loading model context")
        set_destination(Databricks(experiment_id=os.getenv("MONITORING_EXPERIMENT_ID")))

        self.logger.debug("Instantiate workspace client")
        self.w = WorkspaceClient()
        # You can load any artifacts here if needed
        # self.artifacts = context.artifacts

        self.logger.debug("Instantiate openai client")
        # Get an OpenAI-compatible client configured for Databricks serving endpoints
        self.openai_client = self.w.serving_endpoints.get_open_ai_client()

    @mlflow.trace(name="answer_question", span_type=SpanType.LLM)
    def answer_question(self, question_text: str, document_text: str) -> tuple[object, str | None]:
        """Answer a question using LLM with structured response format.

        Uses the OpenAI-compatible client to call a language model with a structured
        JSON response format. The LLM analyzes the provided document text and returns
        a yes/no answer with reasoning.

        Args:
            question_text (str): The yes/no question to answer about the document
            document_text (str): The document text to analyze

        Returns:
            tuple: (openai.ChatCompletion, str|None) - LLM response and span_id
        """
        # Create a chat completion request with structured response for questions

        question_prompt = f"""
        You are a document analysis expert. Answer the following yes/no question based on the provided document.

        Question: "{question_text}"

        Document:
        {document_text}

        Analyze the document and provide a structured response.
        """

        # Create a separate sub-span for the actual OpenAI API call
        llm_response = self._call_openai_completion(question_prompt)

        # Get the current span ID for this specific answer
        current_span = mlflow.get_current_active_span()
        span_id = current_span.span_id if current_span is not None else None

        return llm_response, span_id

    @mlflow.trace(name="openai_completion", span_type=SpanType.LLM)
    def _call_openai_completion(self, prompt: str):
        """Make the actual OpenAI API call with its own sub-span.

        Args:
            prompt (str): The formatted prompt to send to the LLM

        Returns:
            OpenAI ChatCompletion response
        """
        return self.openai_client.chat.completions.create(
            model=os.getenv("LLM_MODEL", "databricks-claude-3-7-sonnet"),  # Configurable LLM model
            messages=[
                {
                    "role": "user",
                    "content": prompt
                }
            ],
            response_format={
                "type": "json_schema",
                "json_schema": {
                    "name": "question_response",
                    "schema": Answer.model_json_schema()
                }
            }
        )

    @mlflow.trace(name="document_analysis")
    def predict(self, context, model_input: list[AgentInput]) -> list[AgentOutput]:
        """Process document analysis questions with yes/no answers.

        Args:
            context: MLflow model context
            model_input: List of structured inputs containing document text and questions

        Returns:
            List of AgentOutput with yes/no answers and reasoning
        """
        self.logger.debug(f"Processing {len(model_input)} classification request(s)")

        # Get the current trace ID for user feedback collection
        # Will be None during offline evaluation when no active span exists
        current_span = mlflow.get_current_active_span()
        trace_id = current_span.trace_id if current_span is not None else None

        results = []
        for input_data in model_input:
            self.logger.debug(f"Number of questions: {len(input_data.questions)}")
            self.logger.debug(f"Document length: {len(input_data.document_text)} characters")

            analysis_results = []

            for question in input_data.questions:
                self.logger.debug(f"Processing question: {question.text}")

                # Answer the question using LLM with structured response
                llm_response, answer_span_id = self.answer_question(question.text, input_data.document_text)

                # Parse structured JSON response
                try:
                    response_data = json.loads(llm_response.choices[0].message.content)
                    answer_obj = Answer(**response_data)
                except Exception as e:
                    self.logger.debug(f"Failed to parse structured response: {e}")
                    # Fallback to default response
                    answer_obj = Answer(
                        answer="No",
                        chain_of_thought="Unable to process the question due to parsing error."
                    )

                analysis_results.append(AnalysisResult(
                    question_text=question.text,
                    answer=answer_obj.answer,
                    chain_of_thought=answer_obj.chain_of_thought,
                    span_id=answer_span_id
                ))

            self.logger.debug(f"Generated {len(analysis_results)} analysis results")

            results.append(AgentOutput(
                results=analysis_results,
                trace_id=trace_id
            ))

        return results

mlflow.models.set_model(DocumentAnalyser())

Log and register the agent

Before the agent can be deployed to a serving endpoint, it must be logged to an MLflow experiment and registered in Unity Catalog.

Python
import os
import mlflow

import json
from mlflow.pyfunc import PythonModel
from pydantic import BaseModel, Field
from model import DocumentAnalyser, AgentInput, Question


# Create example input for signature inference
def create_example_input() -> AgentInput:
    """Create example input for the non-conversational agent."""
    return AgentInput(
        document_text="Total assets: $2,300,000. Total liabilities: $1,200,000. Shareholder's equity: $1,100,000. Net income for the period was $450,000. Revenues: $1,700,000. Expenses: $1,250,000. Net cash provided by operating activities: $80,000. Cash flows from investing activities: -$20,000",
        questions=[
            Question(text="Do the documents contain a balance sheet?"),
            Question(text="Do the documents contain an income statement?"),
            Question(text="Do the documents contain a cash flow statement?"),
        ],
    )


input_example = create_example_input()

with mlflow.start_run(run_name="deploy_non_conversational_agent"):
    active_run = mlflow.active_run()
    current_experiment_id = active_run.info.experiment_id
    # Set environment variables for the model using current notebook experiment
    os.environ["MONITORING_EXPERIMENT_ID"] = current_experiment_id
    print(
        f"✓ Using current notebook experiment ID for tracing: {current_experiment_id}"
    )

    # Log the non-conversational agent with auto-inferred dependencies
    model_info = mlflow.pyfunc.log_model(
        name=MODEL_NAME,
        python_model="model.py",  # Path to the model code file
        input_example=[create_example_input().model_dump()],
        registered_model_name=REGISTERED_MODEL_NAME,
    )

# Set logged model as current active model to associate it with the below evaluation results
mlflow.set_active_model(model_id=mlflow.last_logged_model().model_id)

print(f"✓ Model logged and registered: {REGISTERED_MODEL_NAME}")
print(f"✓ Model version: {model_info.registered_model_version}")

Evaluate the agent

Before deploying to production, evaluate the agent's performance using MLflow's GenAI evaluation framework with pre-built scorers. Some scorers require a ground truth dataset.

Python
import mlflow
import mlflow.genai.datasets
from requests import HTTPError

# Create an evaluation dataset in Unity Catalog
uc_schema = f"{CATALOG}.{SCHEMA}"
evaluation_dataset_table_name = "document_analyser_eval"

try:
    # Try to create a new evaluation dataset
    eval_dataset = mlflow.genai.datasets.create_dataset(
        uc_table_name=f"{uc_schema}.{evaluation_dataset_table_name}",
    )
    print(f"✓ Created evaluation dataset: {uc_schema}.{evaluation_dataset_table_name}")
except HTTPError as e:
    # Check if it's a TABLE_ALREADY_EXISTS error
    if e.response.status_code == 400 and "TABLE_ALREADY_EXISTS" in str(e):
        print(
            f"Dataset {uc_schema}.{evaluation_dataset_table_name} already exists, loading existing dataset..."
        )
        eval_dataset = mlflow.genai.datasets.get_dataset(
            uc_table_name=f"{uc_schema}.{evaluation_dataset_table_name}"
        )
        print(
            f"✓ Loaded existing evaluation dataset: {uc_schema}.{evaluation_dataset_table_name}"
        )
    else:
        # Different HTTP error, re-raise
        raise

# Define comprehensive test cases with expected facts for ground truth comparison
sample_document = "Total assets: $2,300,000. Total liabilities: $1,200,000. Shareholder's equity: $1,100,000. Net income for the period was $450,000. Revenues: $1,700,000. Expenses: $1,250,000. Net cash provided by operating activities: $80,000. Cash flows from investing activities: -$20,000"

evaluation_examples = [
    {
        "inputs": {
            "document_text": sample_document,
            "questions": [{"text": "Do the documents contain a balance sheet?"}],
        },
        "expectations": {
            "expected_facts": [
                "answer is Yes",
                "balance sheet information",
                "total assets mentioned",
                "total liabilities mentioned",
                "shareholder's equity mentioned",
            ]
        },
    },
    {
        "inputs": {
            "document_text": sample_document,
            "questions": [{"text": "Do the documents contain an income statement?"}],
        },
        "expectations": {
            "expected_facts": [
                "answer is Yes",
                "income statement information",
                "net income mentioned",
                "revenues mentioned",
                "expenses mentioned",
            ]
        },
    },
    {
        "inputs": {
            "document_text": sample_document,
            "questions": [{"text": "Do the documents contain a cash flow statement?"}],
        },
        "expectations": {
            "expected_facts": [
                "answer is Yes",
                "cash flow information",
                "operating activities mentioned",
                "investing activities mentioned",
                "cash flows mentioned",
            ]
        },
    },
    {
        "inputs": {
            "document_text": sample_document,
            "questions": [
                {
                    "text": "Do the documents contain information about employee benefits?"
                }
            ],
        },
        "expectations": {
            "expected_facts": [
                "answer is No",
                "no employee benefits information",
                "financial statements focus",
                "no HR-related content",
            ]
        },
    },
]

# Add the examples to the evaluation dataset
eval_dataset.merge_records(evaluation_examples)
print(f"✓ Added {len(evaluation_examples)} records to evaluation dataset")

# Preview the dataset
df = eval_dataset.to_df()
print(f"✓ Dataset preview - Total records: {len(df)}")
df.display()

Python
import warnings
import mlflow
from mlflow.genai.scorers import (
    RelevanceToQuery,
    Correctness,
    Guidelines,
)

# Suppress harmless threadpoolctl warnings that can appear in Databricks environments
warnings.filterwarnings("ignore", message=".*threadpoolctl.*")
warnings.filterwarnings("ignore", category=UserWarning, module="threadpoolctl")

# Load the logged model for evaluation
model_uri = f"models:/{REGISTERED_MODEL_NAME}/{model_info.registered_model_version}"
print(f"Loading model for evaluation: {model_uri}")

# Load the model as a predict function
loaded_model = mlflow.pyfunc.load_model(model_uri)


def my_app(document_text, questions):
    """Wrapper function for the model prediction."""
    # The evaluation dataset's inputs field contains {"document_text": "...", "questions": [...]}
    # but the predict_fn parameter names must match the keys in inputs
    input_data = {"document_text": document_text, "questions": questions}
    return loaded_model.predict([input_data])


# Define scorers for evaluation including ground truth comparison
correctness_scorer = Correctness()  # Compares against expected_facts
relevance_scorer = RelevanceToQuery()  # Evaluates relevance of response to question
response_schema_scorer = Guidelines(
    name="response_schema",
    guidelines="The response must be structured JSON with an 'answer' field containing 'Yes' or 'No' and a 'chain_of_thought' field with clear reasoning. There also needs to be a 'question_text' field that contains the question that was asked. All these fields are part of the 'results' array field.",
)  # Validates structured output format

# This creates an evaluation run using the MLflow-managed dataset
results = mlflow.genai.evaluate(
    data=eval_dataset,  # Use the MLflow-managed dataset
    predict_fn=my_app,
    scorers=[
        correctness_scorer,
        relevance_scorer,
        response_schema_scorer,
    ],
)

# Access the run ID
print(f"✓ Evaluation completed")
print(f"Evaluation run ID: {results.run_id}")

# Display evaluation results summary
if hasattr(results, "metrics") and results.metrics:
    print("\n📊 Evaluation Results Summary:")
    for metric_name, metric_value in results.metrics.items():
        if isinstance(metric_value, (int, float)):
            print(f"  • {metric_name}: {metric_value:.3f}")
        else:
            print(f"  • {metric_name}: {metric_value}")
else:
    print("✓ Evaluation completed - view detailed results in the evaluation experiment")

# Display link to the evaluation dataset
print(f"\n📊 Evaluation Dataset: {uc_schema}.{evaluation_dataset_table_name}")
print(f"🔗 View dataset in Unity Catalog Data Explorer")

Deploy to Model Serving

Deploy the evaluated agent to a Model Serving endpoint with the necessary environment variables for MLflow 3 tracing. This ensures all production requests are automatically traced and logged to the specified MLflow experiment.

Python
import mlflow
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.serving import (
    ServedEntityInput,
    ServingModelWorkloadType,
    EndpointCoreConfigInput,
)

from model import DocumentAnalyser, AgentInput, Question

workspace = WorkspaceClient()

# Use the model version from the logged model
model_version = model_info.registered_model_version

print(f"Using model version: {model_version}")

new_entity = ServedEntityInput(
    entity_name=REGISTERED_MODEL_NAME,
    entity_version=model_version,
    name=f"{MODEL_NAME}-{model_version}",
    workload_size="Small",
    workload_type=ServingModelWorkloadType.CPU,
    scale_to_zero_enabled=True,
    environment_vars={
        "DATABRICKS_CLIENT_ID": f"{{{{secrets/{SECRET_SCOPE}/client_id}}}}",
        "DATABRICKS_CLIENT_SECRET": f"{{{{secrets/{SECRET_SCOPE}/client_secret}}}}",
        "DATABRICKS_HOST": DATABRICKS_HOST,
        "MLFLOW_TRACKING_URI": "databricks",
        "MONITORING_EXPERIMENT_ID": current_experiment_id,
        "MODEL_LOG_LEVEL": "INFO",
        "LLM_MODEL": LLM_MODEL,
    },
)

# Check if endpoint exists and create or update accordingly
try:
    # Try to get the existing endpoint
    existing_endpoint = workspace.serving_endpoints.get(ENDPOINT_NAME)
    print(
        f"Endpoint {ENDPOINT_NAME} exists, updating with model version {model_version}"
    )

    # Update existing endpoint with new model version
    workspace.serving_endpoints.update_config(
        name=ENDPOINT_NAME, served_entities=[new_entity]
    )
    print("Endpoint update initiated, waiting for completion...")

    # Wait for update to complete
    workspace.serving_endpoints.wait_get_serving_endpoint_not_updating(ENDPOINT_NAME)
    print("Endpoint updated successfully and is ready")

except Exception as e:
    # Endpoint doesn't exist, create it
    print(f"Endpoint {ENDPOINT_NAME} doesn't exist, creating new endpoint...")

    workspace.serving_endpoints.create(
        name=ENDPOINT_NAME,
        config=EndpointCoreConfigInput(name=ENDPOINT_NAME, served_entities=[new_entity]),
    )
    print("Endpoint creation initiated, waiting for completion...")

    # Wait for creation to complete
    workspace.serving_endpoints.wait_get_serving_endpoint_not_updating(ENDPOINT_NAME)
    print("Endpoint created successfully and is ready")

# Final status check
endpoint_status = workspace.serving_endpoints.get(ENDPOINT_NAME)
print(f"Final endpoint status: {endpoint_status.state}")
print(
    f"Endpoint URL: https://{DATABRICKS_HOST.replace('https://', '')}/serving-endpoints/{ENDPOINT_NAME}/invocations"
)

Set up production monitoring with using scorers

Configure automatic quality evaluation for production traffic using MLflow 3 scorers. Scorers automatically analyze traces logged from production requests to provide continuous quality monitoring.

Python
from mlflow.genai.scorers import (
    RelevanceToQuery,
    Guidelines,
    ScorerSamplingConfig,
    list_scorers,
    get_scorer,
)

# Set the active experiment for scoring (use the current notebook's experiment)
print(f"Setting experiment to: {current_experiment_id}")
mlflow.set_experiment(experiment_id=current_experiment_id)

# Verify the experiment is set correctly
current_experiment = mlflow.get_experiment(current_experiment_id)
print(
    f"Current experiment: {current_experiment.name} (ID: {current_experiment.experiment_id})"
)

# Setup scorers for production monitoring
print("Setting up production monitoring scorers...")

# Relevance scorer - always create new to avoid conflicts
relevance_scorer = RelevanceToQuery().register(name="financial_relevance_check")
relevance_scorer = relevance_scorer.start(
    sampling_config=ScorerSamplingConfig(sample_rate=0.5)
)
print("✅ Created relevance scorer (50% sampling)")

# Guidelines scorer for response schema validation
response_schema_scorer = Guidelines(
    name="response_schema",
    guidelines="The response must be structured JSON with an 'answer' field containing 'Yes' or 'No' and a 'chain_of_thought' field with clear reasoning.",
).register(name="response_schema_check")
response_schema_scorer = response_schema_scorer.start(
    sampling_config=ScorerSamplingConfig(sample_rate=0.4)
)
print("✅ Created response schema scorer (40% sampling)")

# List all active scorers
print(f"\nActive Scorers in Experiment {current_experiment_id}:")
scorers = list_scorers()
for scorer in scorers:
    print(f"• {scorer.name}: {scorer.sample_rate*100}% sampling")

Test the deployed agent

Test the deployed agent with sample inputs. Each request will automatically generate MLflow 3 traces that capture the complete request flow, and the production scorers will evaluate these traces for quality monitoring.

Python
from databricks.sdk import WorkspaceClient

# Test the non-conversational agent endpoint using Databricks SDK
workspace = WorkspaceClient()

# Example payload with structured input for the non-conversational agent
test_input = {
    "inputs": [
        {
            "document_text": "Total assets: $2,300,000. Total liabilities: $1,200,000. Shareholder's equity: $1,100,000. Net income for the period was $450,000. Revenues: $1,700,000. Expenses: $1,250,000. Net cash provided by operating activities: $80,000. Cash flows from investing activities: -$20,000",
            "questions": [
                {"text": "Do the documents contain a balance sheet?"},
                {"text": "Do the documents contain an income statement?"},
                {"text": "Do the documents contain a cash flow statement?"},
            ],
        }
    ]
}

# Query the serving endpoint using the workspace client
response = workspace.serving_endpoints.query(
    name=ENDPOINT_NAME, inputs=test_input["inputs"]
)

print("Endpoint Response:")
print(response.as_dict())

# Generate MLflow experiment URL
experiment_url = f"{DATABRICKS_HOST}/ml/experiments/{current_experiment_id}"
print(f"\nMLflow Experiment URL: {experiment_url}")

Log user feedback

Even for non-conversational agents, collecting user feedback is crucial for continuous improvement. User-facing frontend applications can allow users to accept or reject individual answers provided by the agent. This feedback can then be logged to MLflow using the trace_id and span_id included in the response.

Common feedback scenarios for non-conversational agents:

Accuracy feedback: "Was this yes/no answer correct?"
Relevance feedback: "Was the reasoning appropriate for the question?"
Quality feedback: "Was the supporting evidence sufficient?"
Error reporting: "Did the agent misunderstand the document content?"

The following cell demonstrates how to log feedback for an individual answer using the span_id returned in the response.

Python
import mlflow
from mlflow.entities import AssessmentSource

# Get the response from the previous test (extract span_id from first result)
# In a real application, this would come from the API response
response_dict = response.as_dict()
first_prediction = response_dict["predictions"][0]
first_result = first_prediction["results"][0]

# Assert we have the required IDs for feedback logging
assert (
    first_result.get("span_id") is not None
), "span_id is required for feedback logging"
assert (
    first_prediction.get("trace_id") is not None
), "trace_id is required for feedback logging"

span_id = first_result["span_id"]
trace_id = first_prediction["trace_id"]
question_text = first_result["question_text"]
answer = first_result["answer"]

print(f"Logging feedback for question: '{question_text}'")
print(f"Agent answer: {answer}")
print(f"Span ID: {span_id}")
print(f"Trace ID: {trace_id}")

try:
    # Example: User provides positive feedback on this specific answer
    mlflow.log_feedback(
        trace_id=trace_id,
        span_id=span_id,
        name="user_feedback",
        value=True,  # True for positive, False for negative
        source=AssessmentSource(source_type="HUMAN"),
        rationale="Answer was accurate and well-reasoned",
    )

    print("✅ Feedback logged successfully!")

except Exception as e:
    print(f"Note: Could not log feedback in this environment: {e}")

Non-conversational AI agents using MLflow

Requirements

Example scenario

Set up the service principal

Input and output format

Input format (`AgentInput`)

Output format (`AgentOutput`)

Build the non-conversational agent

Log and register the agent

Evaluate the agent

Deploy to Model Serving

Set up production monitoring with using scorers

Test the deployed agent

Log user feedback

Next steps

Example notebook

Non-conversational AI agents using MLflow

Requirements​

Example scenario​

Set up the service principal​

Input and output format​

Input format (AgentInput)​

Output format (AgentOutput)​

Build the non-conversational agent​

Log and register the agent​

Evaluate the agent​

Deploy to Model Serving​

Set up production monitoring with using scorers​

Test the deployed agent​

Log user feedback​

Next steps​

Example notebook​

Non-conversational AI agents using MLflow

Requirements

Example scenario

Set up the service principal

Input and output format

Input format (`AgentInput`)

Output format (`AgentOutput`)

Build the non-conversational agent

Log and register the agent

Evaluate the agent

Deploy to Model Serving

Set up production monitoring with using scorers

Test the deployed agent

Log user feedback

Next steps

Example notebook