Eldert Grootenboer explores why agentic AI systems require advanced messaging capabilities. He outlines how Azure Service Bus helps orchestrate distributed workloads, manage unpredictability, and reduce operational waste at scale.

Enterprise Messaging Patterns for Agentic AI Backends with Azure Service Bus

Agentic AI workloads are getting more dynamic and fast-paced, acting through networks of agents and distributed MCP services rather than simple model calls. This evolution introduces new challenges in integrating, scaling, and reliably orchestrating tasks across many moving parts. Eldert Grootenboer’s analysis focuses on how messaging infrastructure—specifically Azure Service Bus—provides essential capabilities for tackling these challenges.

Agentic AI System Architectures

Agentic networks: Instead of direct, single API calls, agentic systems now orchestrate hundreds or thousands of tasks across multiple cooperating services.
Unpredictable bursts: Work arrives irregularly, overloading some services while others are underutilized. Each model invocation carries compute and cost implications, making efficiency and reliability crucial.
Need for orchestration: Direct calls cannot handle surges, failures, or retries efficiently. Introducing a broker (e.g., Azure Service Bus) helps buffer work, queue up requests, and pace processing in line with downstream capacity and budgets.

Azure Service Bus: Platform Features

Queues/Topics: Provide reliable storage and fan-out capabilities for messages.
Sessions: Maintain order for related work and track progress through distributed workflows.
Dead Letter Queues: Segregate failures so that broken tasks don’t block healthy workloads.
Scheduled Delivery & Deferral: Enable retry logic, controlled timing, and resequencing without custom coding.
Message TTL: Removes stale tasks before they waste resources.
Duplicate Detection: Ensures idempotency, so duplicate requests don’t cause waste.

Messaging Patterns for Agentic Workloads

Scatter / Gather

Pattern: Agents fan tasks out to many backend workers; Azure Service Bus topics enable distribution.
Benefit: Sessions correlate these messages, and dead letter queues manage failures without holding up progress.

Pattern: Agents propose actions, gather partial results, and refine outputs iteratively.
Techniques: Scheduled delivery, deferral, and TTL manage timeout, sequencing, and clean up of out-of-date proposals.

Saga-like Coordination

Pattern: Multi-step workflows need stepwise execution and progress tracking.
Benefit: Session state tracks status, and dead letter queues allow targeted remediation.

Backpressure & Load Shaping

Pattern: Queues buffer sporadic surges, scheduled delivery and concurrency controls shape workloads, and lock renewal protects long-running tasks.
Goal: Keep latencies predictable and prevent failures from snowballing.

Practical Insights

Cost Management: Reduces unnecessary retries and duplicate efforts, translating directly into savings on compute and tokens.
Resilience: Decouples components so failures in one part don’t cascade through the system.
Scalability: Azure Service Bus features are foundational for building agentic backends at enterprise scale.

Historical Perspective

Service-Oriented Patterns: Enterprise messaging has previously helped tame complexity in multi-system integrations, with decoupling and reliable communication still crucial today.
Difference for AI: Agentic AI workloads add more granularity, unpredictability, and higher operational cost for failures—making robust messaging design even more critical.

Conclusion

Agentic AI creates unpredictable, asynchronous workloads with demanding requirements for reliability, cost control, and expansion. Messaging infrastructure, particularly Azure Service Bus, provides the vital orchestration tools needed for these evolving architectures. By applying proven enterprise messaging patterns, architects can ensure their AI backends remain scalable, manageable, and predictable—even as complexity grows.

Author: Eldert Grootenboer