Alan Clucas
Staff Software Engineer at Pipekit
Southampton, United Kingdom
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Alan Clucas is an Staff Software Engineer at Pipekit, and project lead on Argo Workflows. His background is in software development and devops, and from this has come a love of open source. In his spare time he works on Crumbhole, a collection of Open Source GitOps tools using the Argo Project as a foundation.
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Argo Workflows 4, what's new and what's next
A whirlwind tour of the main features of Argo Workflows from the last year
* User interface markdown
* Performance improvement details
* Full CustomResourceDefinitions
* Multi-Controller Synchronization
* Artifact Plugins
Followed by a quick summary of the roadmap of the year ahead
* Workflow tracing
* Unified expression engine rework
- More debuggable expressions
- Syntax changes to not overlap with helm
* A pluggable DAG engine, with performance improvements
Trace Context Beyond HTTP: Environment Variables as OpenTelemetry Propagation Carriers
Distributed tracing usually assumes context travels in HTTP headers or message metadata. Yet, many workflows cross process boundaries instead: CI jobs launch scripts, workflow engines start containers, and build tools fork subprocesses, losing trace context.
This talk presents the use of OpenTelemetry environment variables propagation carriers, showing how traces can flow across CLI apps, CI/CD systems, build pipelines, and subprocesses without relying on network transports. The carrier model supports multiple propagation formats, enabling interoperability without reliance on a single ecosystem-specific encoding.
Through two demos it demonstrates how injected TRACEPARENT enables end-to-end tracing, linking stages, subprocesses, and even data lineage in a single span tree. Attendees will gain a practical model for maintaining trace continuity beyond network protocols without custom formats.
Demystifying an Argo Workflows Pod — And How We Stopped Needing an Init Container
When Argo Workflows runs your workload, your container isn't alone in its pod. Argo wraps other containers and volumes around it to feed your code its inputs and carry the results back out, and most people never notice any of that is happening.
This talk opens up the pod. We'll look at what each piece does: how your parameters and artifacts get in before your code starts, how the outputs and logs get back out after it finishes, and the little shared filesystem Argo uses to pass it all between containers. You'll come away actually knowing what Argo does for you every time a step runs.
Then we'll get to what's changed. Every Argo pod used to begin with an init container that staged things before your work started. Kubernetes now supports image volumes, which means we can drop that init container entirely. I'll show how this new opt-in feature in Argo Workflows 4.1 works, what you get for it, and the small things worth knowing before you switch it on.
Bring your own artifact driver to workflows
Argo Workflows 4.0 introduces artifact plugins - a powerful extensibility mechanism that allows you to integrate any storage backend or implement a custom artifact driver without modifying Argo's core. Previously, adding support for a new storage system required contributing code to Argo itself. Now, plugins enable organizations to integrate unsupported or proprietary storage systems. Plugins are language-agnostic, containerized GRPC servers that run alongside workflow pods as sidecars and init containers.
In this talk we'll walk through creating a plugin. This involves implementing a GRPC server as a docker image.
The plugin feature introduces pod management complexities: plugins must be available to the argo-server, and during the init and runtime phases of pods that require them. I'll go over some of these complexities and how they were solved, giving attendees insight into how argo-workflows pods and artifacts are orchestrated.
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