Intelligent agents—autonomous software entities capable of perceiving, reasoning, and acting—are redefining how systems operate. Imagine a world where logistics chains self-optimize, manufacturing lines predict and prevent failures before they occur, and financial services dynamically detect fraud and make trading decisions—all without human intervention. This is the promise of intelligent agents, but the real challenge isn’t just making agents smarter—it’s about ensuring they work together efficiently and cost-effectively.

This is where Materialize comes in. Materialize isn’t just a database; it’s the connective tissue that empowers intelligent agents to collaborate in real time, ensuring they act efficiently without wasting resources.

What Are AI Agents, and Why Do They Need Orchestration?

AI agents are autonomous software entities designed to sense their environment, process information, and act to achieve specific goals. They power systems that optimize delivery routes, predict equipment failures, and dynamically tailor customer interactions. These agents promise efficiency and scalability across industries by automating complex workflows.

However, agents rarely operate in isolation. They’re part of interconnected ecosystems where their value depends on how effectively they:

  1. Collaborate: Share a unified understanding of the world.
  2. React: Respond quickly to critical changes without wasting resources.
  3. Scale: Operate cost-effectively, even as systems grow more complex.

Without effective orchestration, the promise of AI agents can quickly unravel. Instead of collaborating, agents may compete for limited resources. They might rely on outdated or inconsistent data, leading to flawed decisions and misguided responses. Worse, they can duplicate efforts, introducing inefficiencies that drain system performance and inflate costs—ultimately failing to deliver on their intended value.

The Orchestration Problem: Why It’s Harder Than It Looks

Consider a modern supply chain. Delivery drivers, routing algorithms, inventory monitors, and customer service bots all act as independent agents. Their shared goal is simple: deliver packages on time, minimize costs, and keep customers happy. But achieving this is far from straightforward.

Think of these intelligent agents as AI-powered microservices—each making decisions, learning, and adapting in real-time. Unlike traditional microservices, however, these agents must perceive their environment, react to changes, and coordinate their actions without creating excessive complexity or noise. Achieving this level of coordination requires overcoming three key challenges:

Key Challenges

  1. State Sharing Chaos: Agents need a shared, real-time understanding of the world. For example, the inventory system might know the stock levels, while the routing system tracks live traffic. But if the delivery agent doesn’t have an up-to-date, unified view of both, decisions are delayed—or worse, wrong.
  2. Meaningless Triggers: Many systems flood agents with irrelevant updates. This constant noise leads to redundant work, wasted compute cycles and unnecessary interruptions.
  3. The Cost Spiral: AI agents often rely on GPU-intensive computations, especially for large-scale models. Unnecessary activations—whether due to irrelevant changes or redundant processing—escalate costs dramatically. Worse, excessive query traffic can overwhelm transactional databases, creating bottlenecks in mission-critical systems.

Materialize: Building an Operational Data Mesh for Agentic Systems

Materialize is purpose-built to enable agents to collaborate, react, and scale. By leveraging incremental view maintenancestrong global consistency, and a SQL-first approach, Materialize overcomes the inefficiencies and limitations of traditional architectures.

  • Act on Fresh Data Without Overhead: Agents always have access to fresh data while only activating when meaningful events occur. This ensures immediate data availability without keeping resource-intensive processes constantly running.
  • Enable Real-Time Collaboration: Shared, synchronized views ensure agents operate cohesively, reducing conflicts.
  • Scale Intelligently: Precomputed, incremental updates keep costs predictable and manageable, even as systems grow in complexity.

Think of Materialize as the operational data mesh for AI-powered microservices, ensuring agents work with a unified, always-updated view of the world. This transforms orchestration from reactive and costly to proactive and efficient, enabling intelligent agents to share state, adapt, and make real-time decisions.

Shared Reality: A Unified Source of Truth

Developers working with intelligent agents often face the challenge of integrating various components, each with its own data needs and requirements.

Imagine you’re building an agent to optimize delivery routes. You have three sources of information: the inventory system tracks stock levels, the routing system monitors live traffic, and the delivery agent uses both to decide the optimal delivery route. Without a unified view, you end up writing complex logic in your application code to poll these systems, reconcile differences, and ensure everything is up-to-date.

With Materialize, you create a materialized view that combines all of this information into one place, continuously updated. For example:

sql
CREATE MATERIALIZED VIEW unified_delivery_state AS
SELECT
    deliveries.delivery_id,
    deliveries.destination,
    inventory.stock_status,
    routing.traffic_status,
    routing.estimated_travel_time,
    CASE
        WHEN inventory.stock_status = 'out_of_stock' THEN 'hold'
        WHEN routing.traffic_status = 'congested' THEN 'reroute'
        ELSE 'proceed'
    END AS delivery_action
FROM delivery_requests AS deliveries
JOIN inventory_status AS inventory USING (product_id)
JOIN traffic_updates AS routing USING (route_id);

This shared reality ensures faster, more accurate decisions, eliminates redundancy and removes the need for complex ETL pipelines or centralized data warehouses. By enabling autonomous agents to collaborate through shared data, Materialize bridges the gap between independence and consistency.

Benefits:

  • Consistent Shared State: Materialize provides a globally consistent data plane that keeps all AI agents in sync. Through Change Data Capture (CDC), when any system updates its database, Materialize instantly captures and propagates those changes through incrementally maintained views. This ensures every component always has access to the latest data state without manual reconciliation or performance overhead. Materialize guarantees transactional consistency with the upstream database, meaning systems can immediately read their own writes and make decisions based on the most current, accurate information.
  • Team Autonomy: Each agent can maintain its own independent compute cluster while accessing shared, consistent data via stable interfaces. This ensures operational isolation and controlled resource allocation, allowing agents to manage their own compute resources and permissions without interference. As teams deploy agents, they create materialized views to define public interfaces, precisely controlling what state is shared with other entities while keeping sensitive details private. These views serve as data contracts, ensuring efficient and controlled data sharing and allowing agents to collaborate while maintaining autonomy. Team Autonomy
  • Multi-Agent Intelligence: Materialize unlocks powerful collaborative insights by combining real-time data streams from multiple AI agents. Agents can instantly analyze and act on each other’s outputs through precomputed materialized views, enabling sophisticated multi-agent reasoning and decision-making. Complex operations like recursive queries are processed incrementally, ensuring agents can build on each other’s knowledge while maintaining consistent, low-latency performance across the system.

Trigger-When-Interesting: Focused, Meaningful Activation

Materialize ensures that agents activate only when meaningful changes occur. Imagine that instead of constantly polling for delays, your delivery agents are triggered only when an order is late beyond a defined threshold. This avoids wasted compute cycles and reduces the operational cost of running GPU-intensive models or large language models (LLMs).

Example: Late Delivery Alerts

sql
CREATE MATERIALIZED VIEW delayed_orders AS
SELECT
    order_id,
    customer_id,
    delay_time
FROM delivery_status
WHERE delay_time > INTERVAL '30 minutes';

Instead of continuously monitoring all delivery statuses, agents subscribe to this view and are triggered only when a delivery exceeds the 30-minute delay threshold.

sql
SUBSCRIBE (SELECT * FROM delayed_orders);

This ensures costly resources like GPUs and LLMs are used only when actionable insights demand their attention.

Benefits:

  • Reduced GPU and LLM Costs: Avoid processing irrelevant data, saving compute time and budget.
  • Efficient Decision-Making: Trigger inference only when outcomes are affected, ensuring resources focus on valuable insights.
  • Scalable Optimization: Minimize wasted compute cycles across thousands of agents, significantly lowering operational costs for large-scale systems.

The Power of Precomputation: Reducing Latency and Load

Materialize takes the heavy lifting out of data processing by precomputing complex query results—such as joins and aggregations—ahead of time. This means agents can get instant answers without straining the system or overwhelming transactional databases.

Example: Optimizing Delivery Routes

sql
CREATE MATERIALIZED VIEW routing.optimized_routes AS
SELECT
    deliveries.delivery_id,
    deliveries.destination,
    traffic.current_conditions,
    routes.suggested_route,
    weather.forecast,
    customers.priority_level,
    COUNT(deliveries.delivery_id) OVER (PARTITION BY routes.region) AS regional_delivery_count,
    SUM(traffic.delay_time) OVER (PARTITION BY routes.region) AS total_delay_time
FROM public.delivery_requests AS deliveries
JOIN public.current_traffic AS traffic ON deliveries.route_id = traffic.route_id
JOIN internal.route_planner AS routes ON deliveries.destination = routes.destination
JOIN external.weather_data AS weather ON deliveries.destination = weather.location
JOIN public.customer_data AS customers ON deliveries.customer_id = customers.customer_id
WHERE deliveries.status = 'pending'
  AND weather.forecast != 'severe'
  AND customers.priority_level > 2;

By using a precomputed view, agents can instantly query for the best route to a given destination without recomputation. This means faster decisions, lower latency, and reduced application complexity—all of which translate to more responsive and cost-effective orchestration.

sql
SELECT * FROM routing.optimized_routes
WHERE destination = 'New York City';

Agents aren’t limited to precomputed views; they can also use SQL to transform the data into the exact shape needed. By transforming data directly at the database layer, agents ensure efficiency and reduce application-layer complexity:

sql
SELECT destination, COUNT(*) AS pending_deliveries
FROM routing.optimized_routes
WHERE current_conditions = 'clear'
GROUP BY destination;

This transformation type is powerful and performant, enabling agents to derive new insights quickly. Doing this in the database layer instead of at the application level simplifies agent logic, reduces data movement, and minimizes computational overhead.

Benefits:

  • Low-Latency Insights: Precomputed views ensure agents receive instant responses, improving decision speed.
  • Reduced OLTP Load: Offloading query workloads preserve transactional database performance for other critical operations.
  • Support for Many Agents: Scales efficiently to handle thousands of agents simultaneously without performance degradation.

Conclusion

Agentic systems are only as effective as the data that powers them. Materialize ensures that your agents have access to fresh, consistent, and actionable insights—enabling them to make smarter decisions, reduce redundancy, and keep operational costs in check. By empowering agents to collaborate through shared, real-time views, Materialize makes orchestration scalable and intelligent, setting the foundation for the future of autonomous systems.

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