Building Real-Time Multi-Agent AI With Confluent

We are entering a new era of artificial intelligence, where intelligence is not just reactive — it is orchestrated. At Agent Taskflow, we are building a new class of systems: multi-agent orchestration platforms. These systems empower teams of AI agents to coordinate, reason, and act in concert — the way human teams do.

But building these systems at scale requires something most AI platforms overlook: real-time, observable, fault-tolerant communication. That is why we built Agent Taskflow on the Confluent data streaming platform, unlocking cloud-native Apache Kafka, managed connectors, Stream Governance, and more.

In this post, I will share why we chose Confluent, how it powers our multi-agent platform, and the real-world impact it is already delivering for our team and customers.

What Is Agent Taskflow?

Agent Taskflow is an AI orchestration platform designed to make multi-agent systems accessible and usable by anyone. With a drag-and-drop builder, real-time messaging backbone, and native memory graph, it provides users with:

• Flow Builder: Drag and drop to compose actions for agents to execute.
• Agent pods: Assign multiple agents to tasks with specific roles, memory, behavior, and personalities.
• Chat interface: Interact with multiple agents in the same conversation or create channels with both human users and AI agents working together.
• Orchestration: Trigger agent responses from chat, webhooks, or scheduled jobs.
• Observability: Watch, debug, and replay agent executions and thought processes in real time.

Our vision is straightforward: make useful, affordable, and effective AI agents accessible to everyone. But we are thinking far beyond single agents or even agent groups. We believe the entire future of software is agent-native.

Agent Taskflow is positioned to lead this transition with an entire suite of agent-native applications and developer tools, including SDKs and public APIs. We are building the default operating system for multi-agent orchestration — a system where any individual or enterprise can deploy intelligent agent teams to handle repetitive work, make decisions, and deliver insights.

Why Multi-Agent Systems Matter for Enterprises

Multi-agent systems are networks of intelligent agents that interact, share context, and collaborate to solve complex problems. Agents will drive a new era of automation, delivering greater cost savings, improving customer experiences through faster response times, and unlocking new revenue opportunities.

In the enterprise, multi-agent systems enable use cases such as:

• Salesforce enrichment flows: One agent scrapes a LinkedIn profile, another maps the data to Salesforce, and a third drafts an outreach email.
• Content moderation and customization: Agents analyze healthcare transcripts, remove banned words, and personalize content for different medical audiences.
• Invoice processing: One agent reads invoice PDFs, another extracts and structures the data, and a third updates enterprise databases.

Multi-agent systems let organizations move from isolated AI tools to end-to-end AI workflows that are autonomous, real-time, and accountable.

These are not hypothetical scenarios. We have already built flows like these with real clients, helping them replace multi-tool handoffs with seamless, agent-led automation. One healthcare client now uses an agent pod to sanitize medical transcripts in real time, personalize content by audience, and pass final assets to marketing — all without human handoffs.

The Enterprise Risk Factor: Why Multi-Agent Systems Need Governance

While the benefits of multi-agent systems are substantial, they also introduce exponential risk compared to single-agent deployments. If human error introduces compliance and security challenges, autonomous AI agents can dramatically multiply these concerns.

Enterprises adopting multi-agent systems face several critical risks:

• Untracked information flow between agents that can leak sensitive data
• Unpredictable emergent behaviors when agents interact in complex ways
• Unclear accountability when mistakes occur across agent boundaries
• Runaway costs as agents call APIs, generate tokens, or trigger expensive processes
• Compliance violations that become harder to trace across distributed agents

This is why enterprises need a comprehensive platform for real-time agent orchestration, observation, and governance. Without these safeguards, enterprises risk creating “shadow AI” that operates outside of established governance frameworks.

Technical Challenges in Building Multi-Agent Systems

To help our customers build effective multi-agent systems, we had to address four key technical challenges:

Multi-Agent Communication

Agents must share state, pass messages, and coordinate execution. Without a consistent stream of structured events, agents act out of order, context is lost, and failures cascade across the system. What makes this particularly challenging is the need for real-time interactivity. Users want to see agents thinking, reasoning, and working — not just the final output.

Observability

We do not just want to know if something failed — we want to know why. That requires:

• Replayable logs
• Per-event tracing (correlationId, causationId)
• Structured schemas across every domain

Each agent action generates events across multiple planes. Without a unified event backbone, tracking and debugging becomes nearly impossible.

Fault Tolerance and Scalability

Multi-agent orchestration is compute-heavy and stateful. Our system must:

• Retry failed steps without replaying the entire job
• Scale individual agents or functions independently
• Handle thousands of flows across organizations

Identity and Permissioning

Each agent must be aware of:

• Which data it is allowed to access
• Which actions it can perform
• Its role within the broader flow or organization

Why We Chose Confluent

I have been a data engineer for over a decade. I have scaled Kafka clusters. I know how to do it. But that does not mean I want to spend my time doing it — especially as a startup founder.

We evaluated multiple data streaming and messaging platforms. Confluent stood out because it let us:

• Get started with fully managed Kafka in minutes
• Integrate new systems quickly with fully managed connectors
• Ensure data security, quality, and compliance with Stream Governance (Schema Registry, Stream Lineage)

We chose Confluent not just because it was easier but because it was the only platform that matched our velocity and standards for safety at scale.

The team at Confluent has been first-rate. Through the AI Accelerator Program, they helped us rearchitect our entire event schema — reducing costs, improving scalability, and delivering unmatched observability for agentic activity. Their expertise and hands-on feedback validated our architecture and accelerated our development.

Agent Taskflow’s Streaming Architecture

Using the Confluent data streaming platform, our architecture is structured into three major planes, each represented in our Kafka-based data architecture:

Each event is typed, traceable, and replayable, providing robust observability and fault tolerance out of the box.

This architecture — where each plane corresponds to a Kafka topic namespace — enables the real-time responsiveness that makes Agent Taskflow feel alive. This decoupled, event-driven approach allows us to scale teams and observability independently. When you chat with an agent, you can see it thinking in real time, watch flow steps running, get notified when it is awaiting feedback, and observe as it dynamically renames the chat based on the conversation.

All of this is powered by structured events flowing through Confluent. We have implemented RAG, where events in topics are vectorized and stored in Qdrant. During agent conversations or flows, we run similarity search and inject relevant memories or documents into the agent’s context window.

How We Use the Confluent Data Streaming Platform Today

Every use case on our platform runs on Confluent because our entire runtime is event-driven. Confluent enables our multi-agents to:

• Detect when they should participate in a flow or chat
• Share state through streaming events
• Handle asynchronous human-in-the-loop operations
• Resume flows or tasks with zero loss of context

Each of these agents subscribes to real-time event streams and coordinates through shared Kafka topics — data streaming is the shared language of agents.

We have integrated Confluent products deeply into our platform:

Connectors

• PostgreSQL Sink Connector: Pushes execution logs, job results, and flow telemetry to our transactional database for querying and audit.
• Apache Iceberg Sink Connector: Stores historical event logs and memory snapshots to our analytical layer for reports and training jobs.
• Custom webhook source connector: Captures external triggers from services such as Salesforce, Notion, and ZoomInfo.

Stream Governance

• Schema Registry: Lets us move fast, evolve quickly, and still maintain strict compatibility across services. Every event has a type. Every consumer expects structure.
• Schema validation, event tracing, and metadata tagging: Maintains crucial data quality and observability across our ever-expanding graph of agent behavior.
• Stream Lineage: Debugs long-tail flow issues and ensures clean ownership across teams.

Benefits We Have Seen

• Zero Kafka Management Overhead: No broker tuning, no ZooKeeper headaches, no self-managed scaling. Hiring someone to run Kafka the way Confluent does it would cost an order of magnitude more than what we pay for Confluent Cloud.
• Improved Performance: Agent response latency dropped noticeably even without retuning our agents. The entire platform feels more responsive.
• Faster Development Cycles: For a startup, velocity is everything. We ship features weekly that would have required infrastructure coordination in a traditional stack. We shipped our entire chat observability layer in under two days and now handle 50,000+ events per day effortlessly.
• Cost Efficiency: For an early-stage company, the economics are undeniable. Paying for managed Kafka is a fraction of hiring even a junior engineer to manage it.

What Is Next

Using Confluent, we are building an agent marketplace for users to share and monetize flows, agents, and data assets. We are building a local model interface for running local LLMs, a suite of agent-native apps, an identity layer for policy enforcement, and a lightweight SIEM product for auditing agent behavior through stream analytics.

Streaming will remain our backbone — every action and insight starts as an event.

If you are building enterprise AI, real time is not optional — it is foundational. At Agent Taskflow, we believe agents are collaborators, not tools. Building multi-agent systems is hard — but Confluent makes it possible.