Building Agentic Workflows in a Composable Data Architecture

Key Takeaways:

• Agentic workflows are the future: AI agents that can make decisions, adapt to changes, and collaborate autonomously
• Composable architecture is essential: Modular, API-driven systems let agents plug in without breaking existing pipelines
• Multi-agent workflows work better: Specialized agents collaborating outperform single, complex systems
• Experimentation becomes low-risk: Test new agents in isolated components without rewriting everything
• Resilience is built-in: Each agent workflow can adapt to changes and alert humans when needed

This is the third post in our series exploring the rise of agentic AI and how it's reshaping data engineering:

1. What is Agentic AI?
2. How AI Agents Are Redefining Data Engineering Workflows
3. Building Agentic Workflows in a Composable Data Architecture. In this post, we're diving deeper into the architecture that makes agentic workflows possible and scalable.

What Are Agentic Workflows?

At their core, agentic workflows are data pipelines, powered by intelligent, autonomous, and goal-driven agents, systems capable of making decisions, taking action, and collaborating with other agents or components.

Unlike traditional, rule-based workflows or static scripts, agentic and multi agent workflows offer:

• Dynamic decision-making in response to real-time data and shifting objectives
• Specialized agent collaboration, where different agents handle tasks like data extraction, cleaning, or model training
• Continuous learning, enabling workflows to evolve and optimize over time
• Autonomous problem-solving, so the system can adapt when disruptions occur

Unlike traditional workflows, each agent workflow offers dynamic decision-making and can adapt to real-time conditions without human intervention.

However, agentic AI can’t function effectively in isolation. It requires an architectural foundation that’s just as modular, responsive, and adaptive as the agents themselves.

The Power of Composable Data Architecture

Composable data architecture replaces rigid, all-in-one platforms with modular, interoperable components. Each piece of your data stack, ingestion, transformation, storage, and analytics, is built to plug and play, not lock you in.

Multi-agent workflows thrive in modular environments where each component can evolve independently while maintaining seamless integration.

This approach, outlined in our comprehensive guide on composable data architecture, is based on three core principles:

1. Decoupling

Components can evolve independently without breaking the pipeline. This means you can upgrade your transformation layer without touching your storage or analytics tools.

2. API Connectivity

Systems communicate seamlessly via defined interfaces, enabling real-time data exchange and orchestration across your entire stack.

3. Cloud-Native Flexibility

Infrastructure scales elastically with workloads and agents, ensuring your agentic workflows can handle variable demand without manual intervention.

In short, it's the perfect foundation for agentic AI to thrive.

Why Composability Enables Agentic AI

Here's how a composable architecture supercharges your ability to deploy agentic workflows:

1. Agents Plug into Existing Workflows

Composable platforms expose functionality through APIs. That means AI agents can be added, removed, or replaced without disrupting the entire pipeline. Every agent workflow can plug into existing systems seamlessly. Want an agent to handle real-time data quality checks? Just plug it in.

2. Orchestration Across Modular Systems

Intelligent agents often need to trigger tasks across systems, from warehouses to BI tools. Multi-agent workflows enable smooth orchestration via event-based triggers and shared metadata.

3. Scalable Experimentation

With modularity comes low-risk experimentation. When designing an agent workflow, modularity allows data teams to test new agents (e.g., LLM-based transformers or classifiers) in isolated components of the stack, without rewriting everything. With multi-agent workflows, experimentation becomes truly low-risk.

4. Resilient Agent Workflows

When one component fails, agents can reroute tasks through alternative pathways, maintaining pipeline continuity while alerting teams to issues.

Matillion: Enabling Composable Agentic Workflows

Matillion's Data Productivity Cloud is built for composability from the ground up. Our API-first, cloud-native platform supports agentic workflows by:

• Integrating with every layer of your data stack through 150+ pre-built connectors
• Enabling low-code orchestration of agents and transformations via our visual pipeline designer
• Offering flexible deployment across cloud environments (AWS, Azure, Google Cloud, Snowflake)
• Supporting real-time decision-making through event-driven architecture

This foundation powers Maia, an advanced, generative AI-powered system that provides virtual data engineers designed to work in concert with human teams, operating on Matillion's proven data productivity cloud platform.

Maia is built to thrive in a multi-agent environment. From triggering orchestration tools to coordinating with AI copilots across your stack, Maia is designed to be a revolutionary part of the evolving, interconnected, intelligent workforce.

Maia represents the next evolution of agentic AI, bringing autonomous intelligence directly into your data pipelines.

Real-World Example: Multi-Agent Pipelines in Action

Consider modern multi-agent workflows in e-commerce data pipelines powered by multiple specialized agents:

Agent 1: Intelligent Ingestion

• Monitors multiple data sources (web analytics, CRM, inventory systems)
• Adapts ingestion frequency based on business events (flash sales, seasonal peaks)
• Automatically handles schema changes without manual intervention

Agent 2: Dynamic Transformation

• Applies business rules that evolve based on data patterns
• Detects and corrects data quality issues in real-time
• Optimizes transformation logic for performance as data volumes change

Agent 3: ML Operations

• Monitors model performance and triggers retraining when accuracy drops
• Manages A/B tests for new algorithms against production models
• Automatically deploys winning models after validation

Each agent workflow operates independently within the composable architecture, yet coordinates seamlessly through shared APIs and event streams. When the ingestion agent detects a data quality issue, it immediately alerts the transformation agent, which applies corrective measures while notifying the ML agent to pause model updates until data quality is restored.

Architectural Principles for Agentic Success

Building successful agentic workflows requires adherence to specific architectural principles:

Event-Driven Communication

Agents communicate through events rather than direct calls, enabling loose coupling and better fault tolerance. Every agent workflow should follow event-driven communication principles. When an agent completes a task, it publishes an event that other agents can react to.

Stateless Agent Design

Each agent maintains a minimal state, relying on shared data stores for persistence. Design each agent workflow to be stateless; this enables agents to be easily scaled, replaced, or recovered without losing context.

Observability by Design

Every agent action is logged and monitored, providing full visibility into autonomous decisions and their outcomes. This is crucial for debugging and continuous improvement.

Human-in-the-Loop Integration

While agents operate autonomously, they're designed to escalate complex decisions to human operators when confidence levels drop below defined thresholds.

The Future of Agentic Data Engineering

As we look ahead, several trends are shaping the evolution of agentic workflows:

• Enhanced Multi-Agent Collaboration: Future systems will feature more sophisticated coordination between agents, with some serving as orchestrators that manage teams of specialized agents
• Deeper Cloud-Native Integration: Agents will leverage cloud-native services more extensively, automatically provisioning resources, managing costs, and optimizing performance across cloud providers
• Advanced Learning Capabilities: Next-generation multi-agent workflows will incorporate more sophisticated learning algorithms, enabling them to adapt not just to data patterns but to organizational preferences and business contexts

To see how these concepts are being applied in practice, check out our conversation with Julian Wiffen, Matillion's Chief of AI and Data Science, where he explores agentic AI.

The Future Is Modular and Intelligent

Composable data architecture isn't just a trend; it's the architectural shift that makes scalable agentic workflows possible. 

As AI adoption accelerates across enterprises, composability will separate the flexible from the fragile, enabling organizations to adapt quickly to changing business requirements while maintaining operational excellence.

With Matillion, data teams get the best of both worlds: the agility of a modular platform and the intelligence of agent-driven automation. Our composable approach ensures that as your agentic capabilities mature, your architecture can evolve alongside them without requiring complete rebuilds.

The era of static, brittle data pipelines is ending. Multi-agent workflows represent the future of data engineering, intelligent, adaptive systems that can think, learn, and collaborate, all built on the solid foundation of composable architecture.

Ready to explore agentic AI for your data pipelines? Learn more about our composable data architecture approach or see how Maia brings intelligent automation to data engineering workflows.