From Reactive to Orchestrated: Building Real-Time Multi-Agent AI With Confluent

We're entering a new era of artificial intelligence (AI), where intelligence isn't just reactive; it's orchestrated. At Agent Taskflow, we're pioneering a new class of systems: multi-agent orchestration platforms. These systems empower teams of AI agents to coordinate, think, reason, and act in concert—just like human teams.

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

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

What Is Agent Taskflow?

Agent Taskflow is an AI orchestration platform designed to make multi-agent systems (MASs) 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.

• A Slack-style chat interface: Talk 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 simple but powerful: Make useful, affordable, and fun AI agents accessible to everyone. But we're thinking far beyond single agents or even agent groups. We believe the entire future of software is agent-native.

Agent Taskflow is positioned to own this transition with an entire suite of agent-native apps and agent developer tools, including software development kits (SDKs) and public APIs. We want to become 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, which can deliver greater cost savings, improve customer experiences through faster response times, and unlock new revenue opportunities. 

In the enterprise, MASs 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.

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

But what do these systems look like in action? These aren't hypothetical scenarios. We've already built flows like these with real clients, helping them replace clunky, multi-tool handoffs with seamless, agent-led automation.

For example, 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—a risk no executive or security team should accept.

Technical Challenges in Building Multi-Agent Systems

To help our customers build effective MASs, 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

• 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 don't 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, and you're stuck debugging agents by watching chat logs—an approach that completely breaks at scale.

We built our entire system event-first because of these challenges. Every action, thought, and decision is an event first.

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’s allowed to access

• Which actions it can perform

• Its role within the broader flow or organization

Why We Chose Confluent

Let me be candid: I've been a data engineer for over a decade. I've scaled Kafka clusters myself. I know how to do it. But that doesn't 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 (e.g., 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's awaiting feedback

• Observe as it dynamically renames the chat based on the conversation

All of this is powered by structured events flowing through Confluent. We’ve even 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. This allows agents to retrieve relevant knowledge (e.g., customer history, uploaded documents, internal notes) and improve the quality of reasoning and response without relying on model retraining. 

Streaming acts as a data orchestration layer that allows both users and agents to collaborate in ways that simply aren't possible with traditional single-agent systems. It facilitates real-time event processing while providing governance, ensuring data quality, and connecting disparate data sources. By processing data close to the source, it minimizes latency and enhances decision-making, allowing agents to operate with the freshest, most relevant data. 

Agents can respond to each other, share context, and even debate approaches—with humans guiding or observing the process. For example, our dynamic job scheduler built on Confluent ensures that agents can react to real-time events without constant human supervision.

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've 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, where we run reports and training jobs. This may be replaced by Tableflow, which greatly simplifies the process of representing Kafka topics as Iceberg tables.

• 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. That's what makes Agent Taskflow reliable and agile. Without it, our deeply typed system with dozens of event schemas simply wouldn't be possible.

• Schema validation, event tracing, and metadata tagging: Maintains crucial data quality and observability across our ever-expanding graph of agent behavior and system insight. 

• Stream Lineage: Debugs long-tail flow issues and ensures clean ownership across teams.

Benefits We've Seen

Since migrating to Confluent Cloud and Amazon Web Services (AWS) from a competing cloud stack, we've unlocked serious wins:

1. Zero Kafka Management Overhead

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   No broker tuning, no Zookeeper headaches, no self-managed scaling. It's all handled. I'd have to hire someone just to run Kafka the way Confluent does it for me—and they'd cost 10x what I'm paying for Confluent Cloud. Plus, observability means typed, replayable events extend across domains, and debugging and auditing are world-class.

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2. Improved Performance

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   Agent response latency dropped noticeably even without retuning our agents. The entire platform feels more responsive, which translates directly to a better user experience.

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3. Faster Development Cycles and Scalability

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   For a startup, velocity is everything. With Confluent, we ship features weekly that would've required infrastructure coordination in a traditional stack. Case in point: We shipped our entire chat observability layer in under 2 days and now handle 50,000+ events per day effortlessly—with zero need to tune or scale Kafka ourselves.

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4. Cost Efficiency

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   For an early-stage company, the economics are undeniable. Paying for managed Kafka is a tiny fraction of hiring even a junior engineer to manage it—and with dramatically fewer risks.

What's Next for Agent Taskflow

Using Confluent, we're building:

• An agent marketplace: Enabling users to share and monetize flows, agents, and data assets via stream-based discovery.

• A local model interface: Allowing users to run their own local LLMs to back their Agent Taskflow agents.

• A suite of agent-native apps: Powered by real-time data streaming, of course.

• An identity layer: Enforcing policy and permissioning via real-time stream-driven policy enforcement.

• A lightweight security information and event management (SIEM) product: Helping clients audit agent behavior through stream analytics.

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

If you're building enterprise AI, real time isn't optional—it's foundational.

At Agent Taskflow, we believe agents are collaborators, not tools. Building multi-agent systems is hard—but Confluent makes it possible. Together, we're building the infrastructure for a future where AI agents are teammates, working together on workflows that were previously impossible to automate.

Ready to build multi-agents into your stack? Find out what's possible:

• Apply for Confluent's AI Accelerator

• Try Confluent Cloud

• Explore Agent Taskflow