Orchestrating Resilience Modern Asynchronous Systems

In this article, we will discuss what problems we had to solve at Twilio to efficiently build a resilient and scalable asynchronous system and the advantages we got adopting Workflow Orchestration. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/orchestrating-resilience-modern-asynchronous-systems/).

What Happened

InfoQ Homepage Articles Orchestrating Resilience Building Modern Asynchronous Systems

Orchestrating Resilience Building Modern Asynchronous Systems

Building a resilient asynchronous workflow implies a number of challenges including managing state, retries, auditability, and observability.

A workflow orchestration solution abstracts away state management and provides retries, auditability, and observability out-of-the-box.

Using a workflow orchestration solution helps greatly to get a simplified view of any data pipeline, asynchronous system, or event-driven system.

You can adopt and integrate a workflow orchestration solution like Temporal into your existing platform in stages or use it only for parts of your workflow.

Twilio is a customer engagement platform that allows you to engage with your customers on your application using different channels like Voice, Messaging, Whatsapp, email, video.

When you think of SMS, one of the big problems with SMS is spam. Additionally, there’s a lot of phishing going on over SMS (smishing).

Due to the increase in spam messages, many consumers have lost trust in SMS as a form of communication. In the US, A2P 10DLC is the standard that carriers have implemented to regulate this communication pathway.

July 9, 2026, 12 PM EDT Rethinking Logs in the Age of AI Analysis Presented by: Nicolas Jung - Product Manager, Logs at Datadog

Present

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.