How to Integrate Agentic Pipelines with Existing Data Stacks

AI agents need structured, reliable access to your data infrastructure to function in production. While agent frameworks handle orchestration and reasoning, the real bottleneck lies in connecting them to existing data warehouses, databases, and processing pipelines. Most teams end up building fragile middleware that breaks under production load.

This guide shows you how to integrate agentic pipelines with your existing data stack using Typedef's Fenic framework, which treats inference as a first-class operation within DataFrame semantics. You'll learn how to build type-safe data layers, expose them through Model Context Protocol (MCP) servers, and connect them to agent frameworks like LangGraph, LangChain, Mastra, and PydanticAI.

The Integration Challenge

Production AI agents face three critical integration problems when connecting to existing data infrastructure:

Data Format Mismatches

Your data warehouse stores structured SQL tables, your document store contains unstructured markdown and PDFs, and your vector database holds embeddings. Agents need a unified interface that handles all these formats without custom parsing logic for each source.

Context Window Limitations

Agents can't load entire datasets. They need pre-processed, filtered data delivered on-demand through parameterized queries. Traditional data tools weren't designed for this access pattern.

Infrastructure Brittleness

Teams typically build custom microservices to bridge agents and data systems. This creates maintenance overhead, introduces new failure modes, and scatters business logic across multiple codebases. Industry data shows only about 5% of AI pilots deliver measurable business impact, largely due to these infrastructure challenges.

Architecture Patterns for Integration

Production-ready agentic systems require three distinct layers working together:

Data Layer

Transforms raw data into agent-ready formats. This includes extraction from unstructured sources, semantic enrichment, schema validation, and batch preprocessing. Fenic handles this through its DataFrame API with native semantic operators.

Tool Layer

Exposes structured operations agents can invoke. Tools need type safety, parameter validation, runtime error handling, and consistent interfaces across all data sources.

Orchestration Layer

Manages agent state, coordinates tool calls between reasoning steps, implements routing logic, and handles errors gracefully. Frameworks like LangGraph, LangChain, Mastra, and PydanticAI handle this layer.

The key architectural insight: separate data preparation from agent runtime. Process heavy transformations in batch pipelines, then expose clean queryable interfaces through MCP servers.

Setting Up Your Data Layer

Start by configuring a Fenic session with your model providers and data sources:

python
from fenic.api.session import Session, SessionConfig
from fenic.api.session.config import SemanticConfig, OpenAILanguageModel
from pathlib import Path

config = SessionConfig(
    app_name="agent_data_layer",
    db_path=Path("./data/catalog.db"),  # Persistent catalog
    semantic=SemanticConfig(
        language_models={
            "fast": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=100,
                tpm=100000
            ),
            "accurate": OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=50,
                tpm=50000
            )
        },
        default_language_model="fast"
    )
)

session = Session.get_or_create(config)

Loading Data from Multiple Sources

Fenic provides connectors for common data sources:

python
import fenic.api.functions as fc

# Data warehouse tables
warehouse_df = session.read.csv("s3://warehouse/customers/*.csv")

# Document repositories
docs_df = session.read.docs(
    "./documentation/**/*.md",
    content_type="markdown",
    recursive=True
)

# HuggingFace datasets
ml_data = session.read.parquet("hf://datasets/company/training/*.parquet")

# Local files with batch loading
support_tickets = session.read.docs(
    "/data/support_tickets/",
    content_type="markdown",
    recursive=True
)

Processing Unstructured Data

Apply semantic operations to structure your data before agents access it:

python
from pydantic import BaseModel, Field
from typing import List, Literal

class TicketInfo(BaseModel):
    category: Literal["billing", "technical", "account"]
    priority: Literal["low", "medium", "high", "critical"]
    customer_id: str = Field(description="Customer identifier")
    entities: List[str] = Field(description="Named entities mentioned")
    sentiment: str = Field(description="Customer sentiment")

# Extract structured data from unstructured tickets
processed_tickets = support_tickets.select(
    fc.col("file_path"),
    fc.col("content"),
    fc.semantic.extract(
        fc.col("content"),
        response_format=TicketInfo
    ).alias("ticket_info")
)

# Filter and classify
filtered_tickets = processed_tickets.filter(
    fc.col("ticket_info.priority").isin(["high", "critical"])
).select(
    fc.col("ticket_info.*"),
    fc.semantic.classify(
        fc.col("ticket_info.category"),
        ["account_access", "payment_issue", "bug_report"],
        model_alias="fast"
    ).alias("subcategory")
)

# Persist for agent queries
filtered_tickets.write.save_as_table("support_tickets", mode="overwrite")

The semantic extraction capabilities transform unstructured documents into queryable tables with type-safe schemas.

Creating Parameterized Tools

Once data is processed, create tools agents can call with parameters:

python
from fenic.core.mcp.types import ToolParam
from fenic.core.types import StringType, IntegerType

# Define parameterized query
tickets_table = session.table("support_tickets")
search_query = tickets_table.filter(
    (fc.col("category") == fc.tool_param("category", StringType)) &
    (fc.col("priority") == fc.tool_param("priority", StringType))
).limit(fc.tool_param("limit", IntegerType))

# Register as catalog tool
session.catalog.create_tool(
    tool_name="search_tickets",
    tool_description="Search support tickets by category and priority",
    tool_query=search_query,
    tool_params=[
        ToolParam(
            name="category",
            description="Ticket category",
            allowed_values=["billing", "technical", "account"]
        ),
        ToolParam(
            name="priority",
            description="Priority level",
            allowed_values=["low", "medium", "high", "critical"]
        ),
        ToolParam(
            name="limit",
            description="Maximum results",
            default_value=10,
            has_default=True
        )
    ],
    result_limit=50
)

The declarative tool creation approach eliminates boilerplate while ensuring type safety and parameter validation.

System Tools for Common Operations

Fenic auto-generates standard tools for any table:

python
from fenic.api.mcp.tools import SystemToolConfig

# Generate schema, profile, read, search, and analyze tools
system_tools = SystemToolConfig(
    table_names=["support_tickets", "customers"],
    tool_namespace="data",
    max_result_rows=100
)

This creates five tools per table:

data_schema: Returns column names and types
data_profile: Provides statistical summaries
data_read: Paginated filtered access
data_search_summary: Regex search across columns
data_analyze: Raw SQL execution

Deploying MCP Servers

MCP servers bridge your data layer and agent frameworks. Fenic provides multiple deployment options.

Development Server

For local development and testing:

python
from fenic.api.mcp.server import create_mcp_server, run_mcp_server_sync

tools = session.catalog.list_tools()

server = create_mcp_server(
    session=session,
    server_name="AgentDataServer",
    user_defined_tools=tools,
    system_tools=system_tools,
    concurrency_limit=10
)

run_mcp_server_sync(
    server,
    transport="http",
    stateless_http=True,
    port=8000,
    host="127.0.0.1",
    path="/mcp"
)

Production ASGI Deployment

For production environments with horizontal scaling:

python
from fenic.api.mcp.server import run_mcp_server_asgi

app = run_mcp_server_asgi(
    server,
    stateless_http=True,
    path="/mcp"
)

# Deploy with: uvicorn server:app --workers 4 --port 8000

CLI Quick Start

For rapid prototyping:

bash
# Serve all catalog tools
fenic-serve --transport http --port 8000

# Serve specific tools
fenic-serve --tools search_tickets analyze_metrics

# Use stdio for direct integration
fenic-serve --transport stdio

Integration Method: LangGraph

LangGraph excels at managing agent state and orchestrating multi-step workflows. Connect it to your Fenic tools through HTTP requests to the MCP server.

Setting Up Agent State

python
from typing import TypedDict, Annotated, Sequence
from langchain_core.messages import BaseMessage
import operator

class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], operator.add]
    search_results: dict
    analysis: dict

Wrapping MCP Tools

python
from langchain_core.tools import tool
import requests

@tool
def search_tickets(category: str, priority: str, limit: int = 10) -> dict:
    """Search support tickets by category and priority."""
    response = requests.post(
        "http://localhost:8000/mcp/tools/search_tickets",
        json={
            "category": category,
            "priority": priority,
            "limit": limit
        },
        timeout=30
    )
    response.raise_for_status()
    return response.json()

@tool
def analyze_metrics(table_name: str, metric: str) -> dict:
    """Calculate metrics from data tables."""
    response = requests.post(
        f"http://localhost:8000/mcp/tools/data_analyze",
        json={
            "query": f"SELECT {metric}, COUNT(*) FROM {table_name} GROUP BY {metric}"
        },
        timeout=30
    )
    response.raise_for_status()
    return response.json()

Building the Agent Graph

python
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")
tools = [search_tickets, analyze_metrics]
llm_with_tools = llm.bind_tools(tools)

def call_model(state: AgentState):
    """Invoke LLM with current state."""
    response = llm_with_tools.invoke(state["messages"])
    return {"messages": [response]}

def execute_tools(state: AgentState):
    """Execute tool calls from LLM."""
    last_message = state["messages"][-1]
    tool_outputs = []

    for tool_call in last_message.tool_calls:
        if tool_call["name"] == "search_tickets":
            result = search_tickets.invoke(tool_call["args"])
        elif tool_call["name"] == "analyze_metrics":
            result = analyze_metrics.invoke(tool_call["args"])

        tool_outputs.append({
            "tool_call_id": tool_call["id"],
            "output": result
        })

    return {"messages": tool_outputs}

def should_continue(state: AgentState):
    """Determine if more tool calls needed."""
    last_message = state["messages"][-1]
    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
        return "continue"
    return "end"

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", execute_tools)
workflow.set_entry_point("agent")

workflow.add_conditional_edges(
    "agent",
    should_continue,
    {"continue": "tools", "end": END}
)
workflow.add_edge("tools", "agent")

app = workflow.compile()

Integration Method: LangChain

LangChain agents benefit from structured data preprocessing. The integration pattern mirrors LangGraph but focuses on single-step tool execution.

Advanced Semantic Pipeline

Build sophisticated preprocessing pipelines:

python
# Load customer feedback
feedback_df = session.read.docs(
    "./feedback/**/*.md",
    content_type="markdown",
    recursive=True
)

# Extract with embeddings
class FeedbackAnalysis(BaseModel):
    sentiment: Literal["positive", "negative", "neutral"]
    main_topic: str
    action_items: List[str]
    urgency: Literal["low", "medium", "high"]

enriched_feedback = feedback_df.select(
    fc.col("file_path"),
    fc.semantic.extract(
        fc.col("content"),
        response_format=FeedbackAnalysis
    ).alias("analysis"),
    fc.semantic.embed(fc.col("content")).alias("embeddings")
)

# Semantic clustering
clustered = enriched_feedback.semantic.with_cluster_labels(
    by=fc.col("embeddings"),
    num_clusters=10,
    label_column="topic_cluster"
)

clustered.write.save_as_table("feedback_analysis", mode="overwrite")

Creating Hybrid Search Tools

Combine semantic similarity with structured filters:

python
feedback_table = session.table("feedback_analysis")

hybrid_search = feedback_table.filter(
    fc.col("analysis.sentiment") == fc.tool_param("sentiment", StringType)
).with_column(
    "similarity",
    fc.embedding.compute_similarity(
        fc.col("embeddings"),
        fc.tool_param("query_embedding", fc.col("embeddings").data_type),
        metric="cosine"
    )
).filter(
    fc.col("similarity") > 0.7
).order_by(fc.col("similarity").desc())

session.catalog.create_tool(
    tool_name="search_feedback",
    tool_description="Search customer feedback by sentiment and semantic similarity",
    tool_query=hybrid_search,
    tool_params=[
        ToolParam(name="sentiment", description="Filter by sentiment"),
        ToolParam(name="query_embedding", description="Query embedding vector")
    ],
    result_limit=20
)

Integration Method: Mastra

Mastra provides TypeScript-based workflow orchestration. The integration uses Fenic's persistent catalog for durable memory.

Setting Up Persistent Memory

python
from datetime import datetime

# Create memory tables
from fenic.core.types import StringType, TimestampType
from fenic.core.types.schema import Schema, ColumnField

conversation_schema = Schema([
    ColumnField("conversation_id", StringType),
    ColumnField("timestamp", TimestampType),
    ColumnField("role", StringType),
    ColumnField("content", StringType),
    ColumnField("metadata", StringType)
])

session.catalog.create_database("agent_memory")
session.catalog.set_current_database("agent_memory")

session.catalog.create_table(
    "conversations",
    conversation_schema,
    description="Agent conversation history with timestamps"
)

Memory Storage Functions

python
def store_conversation_turn(conversation_id, role, content, metadata=None):
    """Store conversation turn in persistent catalog."""
    df = session.create_dataframe({
        "conversation_id": [conversation_id],
        "timestamp": [datetime.now()],
        "role": [role],
        "content": [content],
        "metadata": [metadata or "{}"]
    })
    df.write.save_as_table("conversations", mode="append")

Memory Retrieval Tools

python
# Tool to retrieve recent conversations
recent_conversations = (
    session.table("conversations")
    .filter(
        fc.col("conversation_id") == fc.tool_param("conversation_id", StringType)
    )
    .sort(fc.col("timestamp").desc())
    .limit(fc.tool_param("limit", IntegerType))
)

session.catalog.create_tool(
    tool_name="get_recent_conversations",
    tool_description="Retrieve recent conversation history",
    tool_query=recent_conversations,
    tool_params=[
        ToolParam(name="conversation_id", description="Conversation ID"),
        ToolParam(name="limit", description="Max messages", default_value=10, has_default=True)
    ],
    result_limit=100
)

Mastra Agent Configuration

tsx
import { Agent } from '@mastra/core/agent';
import { createTool } from '@mastra/core/tools';
import { z } from 'zod';
import { openai } from '@ai-sdk/openai';

const retrieveMemoryTool = createTool({
    id: 'retrieve-memory',
    description: 'Retrieves conversation history from persistent memory',
    inputSchema: z.object({
        conversation_id: z.string(),
        limit: z.number().default(10)
    }),
    execute: async ({ context }) => {
        const response = await fetch('http://127.0.0.1:8000/mcp', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({
                tool: 'get_recent_conversations',
                params: {
                    conversation_id: context.conversation_id,
                    limit: context.limit
                }
            })
        });
        const data = await response.json();
        return data.results;
    }
});

const memoryAgent = new Agent({
    name: 'Memory Agent',
    instructions: 'You have access to persistent memory across sessions.',
    model: openai('gpt-4-turbo'),
    tools: { retrieveMemory: retrieveMemoryTool }
});

Integration Method: PydanticAI

PydanticAI emphasizes type-safe tool calling. The integration leverages Fenic's declarative tool definitions.

Type-Safe Tool Creation

python
# Define tool with complex parameter types
from fenic.core.types import ArrayType, FloatType

user_analysis = session.table("users").filter(
    fc.col("user_id").is_in(fc.tool_param("user_ids", ArrayType(IntegerType)))
).group_by("segment").agg(
    fc.count("*").alias("user_count"),
    fc.avg("lifetime_value").alias("avg_ltv")
)

session.catalog.create_tool(
    tool_name="analyze_user_segments",
    tool_description="Analyze user segments for given user IDs",
    tool_query=user_analysis,
    tool_params=[
        ToolParam(
            name="user_ids",
            description="List of user IDs to analyze"
        )
    ],
    result_limit=100
)

Pydantic Model Extraction

python
class UserProfile(BaseModel):
    name: str = Field(description="Full name")
    role: str = Field(description="Job title")
    skills: List[str] = Field(description="Technical skills")
    experience_years: int = Field(ge=0, le=50)

users_df = session.read.csv("users.csv")

profiles = users_df.select(
    fc.col("user_id"),
    fc.semantic.extract(
        fc.col("bio_text"),
        response_format=UserProfile
    ).alias("profile")
)

profiles.write.save_as_table("user_profiles", mode="overwrite")

PydanticAI Configuration

python
from pydantic_ai import Agent

agent = Agent(
    "openai:gpt-4",
    system_prompt="You are a user data assistant.",
    mcp_servers={
        "fenic_tools": {
            "url": "http://127.0.0.1:8000/mcp",
            "transport": "http"
        }
    }
)

# Agent automatically discovers and validates tools
result = await agent.run(
    "Analyze the user segments for IDs [1001, 1002, 1003]"
)

Production Considerations

Error Handling

Implement robust error handling at every layer:

python
def safe_tool_execution(state):
    """Execute tools with comprehensive error handling."""
    tool_outputs = []

    for tool_call in state["messages"][-1].tool_calls:
        try:
            result = requests.post(
                f"http://localhost:8000/mcp/tools/{tool_call['name']}",
                json=tool_call["args"],
                timeout=30
            )
            result.raise_for_status()
            tool_outputs.append({
                "tool_call_id": tool_call["id"],
                "output": result.json()
            })
        except requests.exceptions.Timeout:
            tool_outputs.append({
                "tool_call_id": tool_call["id"],
                "output": {"error": "Tool execution timeout"}
            })
        except requests.exceptions.RequestException as e:
            tool_outputs.append({
                "tool_call_id": tool_call["id"],
                "output": {"error": f"Request failed: {str(e)}"}
            })

    return {"messages": tool_outputs}

Async Operations for Throughput

Use async UDFs for concurrent external API calls:

python
import aiohttp
from fenic.api.functions import async_udf
from fenic.core.types import StructType, StructField, IntegerType, StringType

@async_udf(
    return_type=StructType([
        StructField("status", IntegerType),
        StructField("data", StringType)
    ]),
    max_concurrency=20,
    timeout_seconds=10,
    num_retries=3
)
async def enrich_from_api(record_id: str) -> dict:
    """Call external API with automatic retries and timeout."""
    async with aiohttp.ClientSession() as session:
        async with session.get(
            f"https://api.example.com/records/{record_id}"
        ) as resp:
            return {
                "status": resp.status,
                "data": await resp.text()
            }

# Apply to DataFrame
enriched_df = df.select(
    fc.col("*"),
    enrich_from_api(fc.col("record_id")).alias("api_data")
)

The async UDF capabilities maintain DataFrame semantics while maximizing I/O throughput.

Monitoring and Cost Tracking

Track performance and costs through built-in metrics:

python
# Query execution metrics
metrics_df = session.table("fenic_system.query_metrics")

# Analyze tool usage
tool_usage = metrics_df.select(
    fc.col("query_text"),
    fc.col("total_lm_cost"),
    fc.col("latency_ms"),
    fc.col("total_lm_requests")
).order_by(fc.col("total_lm_cost").desc())

tool_usage.show(20)

# Aggregate by time window
daily_costs = session.sql("""
    SELECT
        DATE(end_ts) as date,
        SUM(total_lm_cost) as total_cost,
        COUNT(*) as query_count,
        AVG(latency_ms) as avg_latency
    FROM {metrics}
    WHERE query_text LIKE '%search_tickets%'
    GROUP BY DATE(end_ts)
    ORDER BY date DESC
""", metrics=metrics_df)

daily_costs.show()

Rate Limit Management

Configure per-model rate limits in session setup:

python
config = SessionConfig(
    semantic=SemanticConfig(
        language_models={
            "fast": OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,  # Requests per minute
                tpm=500000  # Tokens per minute
            ),
            "accurate": OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=50,
                tpm=100000
            ),
            "reasoning": OpenAILanguageModel(
                model_name="o4-mini",
                rpm=100,
                tpm=100000,
                profiles={
                    "fast": OpenAILanguageModel.Profile(
                        reasoning_effort="low"
                    ),
                    "thorough": OpenAILanguageModel.Profile(
                        reasoning_effort="high"
                    )
                },
                default_profile="fast"
            )
        }
    )
)

Best Practices

Data Preparation Strategy

Preprocess once, query many times

Run expensive semantic operations in batch pipelines. Agents should query clean, structured tables, not raw documents:

python
# Bad: Agent calls semantic operations on each query
def agent_tool(query):
    raw_docs = session.read.docs("./docs/")
    results = raw_docs.select(
        fc.semantic.extract(fc.col("content"), Schema)  # Slow!
    ).filter(fc.col("extracted.topic") == query)
    return results

# Good: Preprocess once, query many times
processed_docs = (
    session.read.docs("./docs/")
    .select(fc.semantic.extract(fc.col("content"), Schema))
    .persist()  # Cache expensive operation
)
processed_docs.write.save_as_table("processed_docs", mode="overwrite")

def agent_tool(query):
    # Fast filtered query on preprocessed data
    return session.table("processed_docs").filter(
        fc.col("topic") == query
    )

Use appropriate model tiers

Configure different models for different task complexities:

python
# Cheap model for simple classification
df.select(
    fc.semantic.classify(
        fc.col("category"),
        ["bug", "feature", "question"],
        model_alias="fast"
    )
)

# Accurate model for complex extraction
df.select(
    fc.semantic.extract(
        fc.col("complex_document"),
        ComplexSchema,
        model_alias="accurate"
    )
)

Tool Design Principles

Keep tools focused

Each tool should perform one specific task. Compose complex operations in the orchestration layer:

python
# Bad: One tool doing too much
session.catalog.create_tool(
    tool_name="everything",
    tool_description="Search, analyze, and summarize all data",
    # ... complex multi-step query
)

# Good: Focused tools composed by agent
session.catalog.create_tool(
    tool_name="search_data",
    tool_description="Search records by criteria",
    # ... simple filtered query
)

session.catalog.create_tool(
    tool_name="analyze_results",
    tool_description="Calculate statistics on search results",
    # ... aggregation query
)

Document thoroughly

AI agents rely on tool descriptions to choose appropriate tools:

python
session.catalog.set_table_description(
    "support_tickets",
    "Customer support tickets from 2023-2025. Includes ticket ID, "
    "category, priority, customer info, resolution status, and timestamps. "
    "Use for analyzing support patterns and customer issues."
)

Validate inputs

Use ToolParam with allowed_values to prevent invalid inputs:

python
ToolParam(
    name="priority",
    description="Priority level for filtering",
    allowed_values=["low", "medium", "high", "critical"]
)

Orchestration Patterns

Implement circuit breakers

Track consecutive failures and stop calling broken tools:

python
def should_retry_tool(state):
    """Circuit breaker for tool failures."""
    consecutive_failures = state.get("consecutive_failures", 0)
    if consecutive_failures >= 3:
        return "fallback"
    return "retry"

workflow.add_conditional_edges(
    "tool_error",
    should_retry_tool,
    {"retry": "tools", "fallback": "human_review"}
)

Separate batch and real-time

Use Fenic for heavy batch processing. Let agents query preprocessed results:

python
# Batch: Run daily to process new data
def daily_preprocessing_job():
    new_data = session.read.csv("s3://bucket/daily/*.csv")
    processed = new_data.select(
        fc.col("*"),
        fc.semantic.extract(fc.col("text"), Schema),
        fc.semantic.embed(fc.col("text"))
    )
    processed.write.save_as_table("daily_data", mode="append")

# Real-time: Agents query preprocessed data instantly
def agent_query(filters):
    return session.table("daily_data").filter(filters).limit(10)

Use stateless HTTP for scale

Enable horizontal scaling of MCP servers:

python
run_mcp_server_sync(
    server,
    transport="http",
    stateless_http=True,  # No session state
    port=8000
)

Production Deployment

Local development, cloud execution

The same code runs locally and in production:

python
# Local development
config = SessionConfig(app_name="dev_pipeline")
session = Session.get_or_create(config)

df = session.read.csv("./local_data.csv")
results = df.select(fc.semantic.extract(fc.col("text"), Schema))
results.write.parquet("./results.parquet")

python
# Production - same code, cloud execution
from fenic.api.session.config import CloudConfig, CloudExecutorSize

config = SessionConfig(
    app_name="prod_pipeline",
    cloud=CloudConfig(
        size=CloudExecutorSize.LARGE  # Scale up
    )
)
session = Session.get_or_create(config)

df = session.read.csv("s3://bucket/data/*.csv")
results = df.select(fc.semantic.extract(fc.col("text"), Schema))
results.write.parquet("s3://bucket/results/")

Real-World Impact

Teams using this integration approach report significant improvements:

95% Reduction in Triage Time

RudderStack integrated Typedef with their Linear workflow, achieving one-pass feature request triage with semantic classification and automatic citation. PMs now review five high-value items each morning instead of spending hours on manual categorization.

100x Time Savings

Enterprise analytics teams transformed OLAP warehouses into dynamic product-signal engines. Product managers query diverse datasets with LLM categorizations in minutes instead of weeks of manual processing.

Days Not Months

Insurance companies deploy semantic extraction pipelines across thousands of policies and transcripts in days, eliminating errors from human analysis and significantly reducing operational risk.

Conclusion

Integrating agentic pipelines with existing data stacks requires separating three concerns: data preparation, tool definition, and orchestration. By preprocessing data with semantic operations, exposing type-safe tools through MCP servers, and connecting to agent frameworks, you build production-grade systems that scale.

The key architectural decisions:

Use Fenic's DataFrame API for batch semantic preprocessing
Create declarative, catalog-backed tools with parameter validation
Deploy MCP servers as the bridge between data and agents
Choose the right integration pattern for your orchestration framework
Implement error handling, monitoring, and rate limiting for production reliability

Whether you're using LangGraph, LangChain, Mastra, or PydanticAI, the pattern remains consistent: build reliable data pipelines that agents can trust.

Ready to start building? Install Fenic and explore the open-source framework:

bash
pip install fenic

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