The Good Tech Companies - How Python Devs Can Build AI Agents Using MCP, Kafka, and Flink

Episode Date: February 12, 2026

This story was originally published on HackerNoon at: https://hackernoon.com/how-python-devs-can-build-ai-agents-using-mcp-kafka-and-flink. Learn how Python developers build real-time AI agents using MCP, Kafka, and Flink—modern agentic workflows explained on HackerNoon. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #python-mcp-ai-agents, #fastmcp-python-tools, #kafka-ai-agent-architecture, #flink-sql-agentic-workflows, #model-context-protocol, #event-driven-ai-microservices, #langgraph-mcp-orchestration, #good-company, and more. This story was written by: @confluent. Learn more about this writer by checking @confluent's about page, and for more stories, please visit hackernoon.com. Python developers can build production-ready AI agents using Model Context Protocol (MCP), Apache Kafka, and Apache Flink. By extending familiar FastAPI microservices with FastMCP tools and event-driven architectures, teams can orchestrate real-time, trustworthy AI workflows without redesigning their stack—turning streaming data into intelligent insights.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. How Python devs can build AI agents using MCP, Kafka, and Flink, by Confluent. By Diptime and Reichottery, staff developer advocate at Confluent engineering teams are experimenting with AI agents at a rapid pace. In fact, Rcent PWC survey found that 79% of companies are already adopting AI agents. For Python developers, this shift doesn't require learning an entirely new Newstack. Existing microservices can be modified with agentic endpoints. Building an AI agent is often, just another API end point, developed with familiar tools like Fast API. By leveraging their existing experience with event-driven architectures and Pythonic frameworks, developers can bridge

the gap between traditional data processing and real-time AI. With orchestrator agents, it becomes possible to combine responses from different agentic invocations and return a single, meaningful insight to theisor. This represents a shift from simple request response cycles to a pattern where heterogeneous microservices exchange messages across multiple enterprise applications. For a Python developer using frameworks like Fast API, this transition is seamless. The orchestrator simply becomes a more sophisticated API endpoint that coordinates data flow between various specialized agents. Model context protocol, MCP, has become the de facto language of choice for IH MCP along with large language models, LLMs, such as Claude, Chad GPT, Orgymanai can take user queries as natural language inputs and select the right set of tools to invoke from MCP servers.

This workflow greatly simplifies agentic interactions. Frameworks such as fast MCP enable Python developers to quickly build deterministic MCP tools so the ambiguity of LLMs trying to understand raw user instructions gets replaced by enterprise components for fetching insights. Fast MCP is the framework of choice for converting fast API-based Rest API endpoints into MCP tool call specification. While MCP abstracts away the individual tool invocation on databases and APIs, the existing Python API codebase needs to be modified to introduce these agentic invocation endpoints. For production deployments, ensure that the exact MCP tool is invoked, instead of listing all tools, which often results in MCP, tool sprawl, and contextblode. To get rid of this, a novel technique is often introduced where a search tool API precedes

the actual MCP tool call. For streaming workloads, Apache Kafka remains the most popular open source data streaming framework for developing microservices that interact with real-time events. Apache Kafka has a battle-tested Python SDK well suited for building microservices which embed Kafka producers and Kafka consumers. Python developers can build event-driven architecture with microservices doing the message exchange over rest endpoints while Kafka durably stores streaming data. To enrich, aggregate and transform streaming data stored in Kafka, Apache FlinkSQL provides a familiar programming model.

With Flink SQL, real-time agentic workflows impose data quality rules and transform in-flight data on Kafka topics. This is an important step within the entire data. data pipeline and ensures that agents invoking data downstream in the pipeline have rich, clean, and trustworthy data. Let's apply these concepts to a real-time agentic workflow for a retail store looking to better understand their customers' behaviors. Imagine we have a clothing store which takes and fulfills orders. Over time, historical order data accumulates, highlighting customer buying behavior noting which products sell out fast, repeat customers,

and which age groups order the most clothes. Insights from this historical data the store decide on a medium to long-term strategy to introduce a new clothing line and devise plans to maximize revenue. At the same time, real-time customer shopping behavior reflects how customers are buying products as it happens. Real-time insights help the store attract repeat purchases and up-sell offers using instant promotions. Let's assume that the historical data is stored as Apache iceberg tables and the real-time behavior is stored on Kafka. Combining these two sources would providence store accurate buyer behavior in real-time, based on past habits. To Dothus, AI agents would need to extract information from an online analytical processing,

OLAP, system, and Apache Kafka. To illustrate this, let's consider the OLAP system to consist of. Parquet files kept within an Apache iceberg lake house which is queried by DuckDB and real-time stream storage consumed from Apache Kafka. Two AI agents, each querying using DuckDB MCP and Kafka, MCP would fetch required information. An orchestrator AI agent would combine results to return a structured JSON output for any natural language query initiated by theesor. Any agentic framework like Lang Graph or AWS strands could invoke tools on these MCP servers and get the required information. On top of the agentic framework, an orchestrator agent could combine responses from these MCP servers and present unique details about store and customer buying behavior in real time. For a Python

developer, the first step for designing this solution would require defining the fast MCP tools for Kafka and Duck DBMCP. Then agentic invocations need to be built, followed by API endpoints which would enable invokers to engage with these agents. The last step would be to add observability features and evaluate AI responses to improve the agentic flow and make it trustworthy. Evaluation of final responses from agents against standards, compliances and benchmarks remains an important step to ensure user acceptance. Since most of the enrichment, cleaning and aggregation of the real-time data would happen at the Flink SQL layer. Developing MCP tools and the invocation layer would only involve pure Pythonic development. Python developers familiar with Kafka and Flink can build

such agentic workflows as the one above while working within the familiar territory of Fast API, Fast MC-Panned open-source agentic frameworks like Lang Graph and AWS strands. With modern Python clients, for Kafka and the rich tooling around MCP and AI agents, it's possible to introduce an agentic workflow within existing microservices in enterprise applications. For modern Python developers, the path to building sophisticated AI agents doesn't require a total architectural overhaul. By combining fast MCP for MCP tools discovery, as well as Apache Kafka and Apache Flink SQL for real-time data, you can ensure your agents are powered by clean, trustworthy data. Whether you are using Lange Graph or AWS strands, the shift toward agentic workflows is essentially an evolution of the

microservices patterns you already know. By treating AI agents as an extension of your existing Kafka and Flink infrastructure, you can move from simple data streaming to delivering real-time, intelligent insights. Chatting with Kafka and Flink, AI agents and Confluent MCP for Python application developers. Kafka and Flink from an application developer's perspective, 150 words, one. Application developers build microservices for business applications. 2. Kafka as the de facto standard for stream storage. 3. Easy to use as a messaging hub for microservices talking with each other. Card API less than to order creation API less than to loyalty API less than to order

fulfillment API, etc. 4. Flink creates enriched, transformed and high quality aggregated data within Kafka topics. What if an eye agent could be embedded within microservices, 150 words, 1. Microservices would always have rest endpoints. 2. Introduce backquote, chat, backquote as the chat with agents. 3. Other endpoints would build the business logic which would be passed to backquote, chat, as context. MCP, model context protocol. The language I agents speak, 200 words, 1. Expose Kafka and Flink tools via MCP streamable HTTP transport mode.

2. Agents would introspect and discover tools and take LLL.L. L.M's help to match user query with the specific tool. Retail store behavior agent, an example, 400 words, 1. Explain the setup, a retailer accepting fast orders through stores. 2. Run Confluent MCP. Describe GitHub repo of MCP Confluent and Kafka, Flink-specific tools. 3. Agent list Kafka and Flink tools and introspect using LLM's help. 4. Agent answers question, which store accepted the most orders in the last?

five minutes. Five, the agent calls MCP tools consume Kafka messages and responds. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn and publish.