Kestra is an open-source orchestration platform that unifies and modernizes legacy IT scheduling, data pipelines, business processes, infrastructure automation, and AI agents under one declarative, event-driven control plane. Here is why I joined as Global Field CTO, and why enterprise orchestration is the category AI makes impossible to ignore.
Orchestration is the connective tissue of the modern enterprise, and right now nobody owns it. That is the gap I am joining Kestra to help close.
The market is at the same inflection point Apache Kafka was at when I joined Confluent in 2017. The technology is mature, already in production at Apple, JPMorgan, Toyota, and Xiaomi. The commercial story is under-narrated. And there is a two-to-three year window to define who owns the category before it closes.
If you want the backstory on the nine years that led here, including the journey from a Kafka startup to an IBM acquisition, I wrote about it separately in My Confluent Chapter: From Apache Kafka Startup to $11 Billion IBM Acquisition.
Every large enterprise runs four kinds of orchestration tools that do not talk to each other. Legacy IT schedulers like Control-M, ActiveBatch, and cron run date-driven batch jobs with no event awareness. Data pipeline tools like Airflow, Prefect, and Dagster move data, but stay scoped to Python DAGs. Business process platforms like Camunda, Appian, Pega, and UiPath handle approvals and human steps, but stop at the process boundary. Infrastructure automation tools like vRA, ServiceNow, and Terraform provision resources, but stay locked into ITSM lifecycles.
Each tool owns one slice. None of them owns the full picture.
Running these tools separately was manageable when each stayed in its lane. Two forces are changing that: enterprises are actively modernizing these tools, and agentic AI is forcing them to work as one.
The first force is modernization, and it is where most enterprises actually start. They are retiring brittle, expensive legacy schedulers. Consolidating data pipeline sprawl across teams. Replacing rigid BPM suites that stop at the process boundary. Each of those is a funded project on its own. And once you are modernizing one category, the obvious question follows: why run four engines when one declarative platform covers all four?
The second force is agentic AI, and it removes any remaining reason to keep the tools apart. AI workflows refuse to stay inside a single tool. One execution can touch a data pipeline, trigger a process step, call a model, and provision infrastructure. When it fails, it often fails quietly in the gap between tools.
The CTO sees governance risk. The CIO sees infrastructure that cannot keep up. The CDO sees stale data. The COO sees a process that stalled for no visible reason. Four executives, four symptoms, one root cause: no single layer governs the full mix of workloads.
Kestra takes all four categories onto one declarative platform, under one governance model. Instead of a patchwork of disconnected tools, one engine orchestrates across the domains an enterprise depends on. Deterministic workflows and autonomous agents, running side by side, under one control plane.
This is the whole thesis in one picture: four fragmented categories on the outside, one engine in the middle.
Kestra is open source under the Apache 2.0 license, the same permissive license as Apache Kafka. Developers and data engineers can start in minutes, building workflows in YAML, through APIs, or with the no-code editor, with a plugin ecosystem of 1,500+ integrations and tasks that run code in any language (Python, Node.js, R, Go, Shell, and more). Workflows can even be generated by an LLM via MCP, so you describe the orchestration in natural language and let the model build the flow. The platform is language-agnostic and event-driven by design.
For production, there is an enterprise edition and a fully managed cloud, with the governance, scale, and SLAs that regulated organizations require built in rather than bolted on, including air-gapped deployment for those that cannot let a single byte leave their perimeter.
Kestra runs in production well beyond early adopters. Apple’s machine learning team uses it to orchestrate large-scale data pipelines. A senior engineering manager there put it plainly: “I want to highlight their robustness, which is crucial at our scale.” Few orchestration tools earn that kind of statement from a team operating at Apple’s level in AI and ML.
The enterprise logos span industries and regions: Bloomberg, Crédit Agricole, JPMorgan, Toyota, T-Systems, and Xiaomi among them. Behind those production deployments sits a fast-growing open-source community, with execution volume up roughly 20x year over year. The growth curve is the same shape I watched at Confluent. Community adoption first, enterprise gravity following close behind.
I do not join companies for incremental bets. I expect Kestra to be a high-growth startup operating at enterprise scale, with the largest companies in the world adopting it across business units for a widening set of use cases. That is not optimism. It is where the demand is already heading.
Open, scalable, reliable, and flexible workflow orchestration is needed everywhere. Every business unit runs processes that have to be triggered, coordinated, retried, audited, and governed. Finance, operations, data, platform engineering, customer-facing teams: each has its own workflows, and today each reaches for a different tool. One platform that serves all of them is a large and durable market, not a niche.
Agentic AI raises the stakes further. It does not reduce the need for orchestration, it multiplies it. Every agent that takes an action needs a governed, observable workflow underneath it, and those workflows cut across exactly the business-unit boundaries that today’s fragmented orchestration tools cannot span. The more AI an enterprise adopts, the more it needs one control plane across every unit. That is the wave Kestra is built to ride.
Start simple. Run Kestra on a standard database and never think about Kafka at all. For many workloads that is all you need. The platform is event-driven from the ground up, and the database backend handles low and medium scale with no extra moving parts.
After nine years close to Apache Kafka, people expect me to lead with the Kafka angle. So let me deal with it directly, because there is a real and underappreciated opportunity here. But it is an option, not a requirement.
I have seen too many teams force the wrong tool into orchestration. Some use Kafka Streams or Flink to orchestrate workflows. In most cases that is an anti-pattern. Neither was built for it. Others bolt on yet another complex system just to get scale. Others hack together Python scripts and hope they hold.
Kestra takes a cleaner path. Run it on a traditional database for low and medium scale. Switch to an existing Kafka deployment when you need larger throughput. Light when you want it light, Kafka-native when you need the scale. For the thousands of enterprises that already run Kafka as their nervous system, that is a natural fit. Everyone else can stay on the database.
Today that means database or Kafka. With the re-architected core in Kestra 2.0 (coming soon), queue and database become fully independent and pluggable, adding support for Redis, AMQP, and cloud-native messaging from AWS, GCP, and Azure. The orchestration layer adapts to your infrastructure rather than forcing you onto theirs.
But Kafka is one entry point, not the destination. The bigger opportunity sits in everything beyond the place I came from: legacy scheduler modernization, data and AI pipelines, and business process orchestration across the enterprise.
Global Field CTO is the external technology and business voice of Kestra, to customers, partners, analysts, press, and the practitioner community. The job is to help Kestra grow across these orchestration domains, across regions, and across the community of people building on it. I have written before about the daily life of a Field CTO, and the shape of the role carries over directly.
In practice that means three things. Working with enterprise CIOs and CTOs to figure out where orchestration fits in their architecture, in briefings and roundtables. Briefing analysts like Gartner, Forrester, and IDC on the customer patterns and market shifts I see. And doing the public work: reference architectures, conference keynotes, competitive positioning, and content that maps the technology to concrete use cases across financial services, insurance, manufacturing, automotive, telco, retail, logistics, and technology.
That last part is where I have spent years building signature assets. The landscape reports, the industry guides, the books. I will keep producing them, now with orchestration as the connecting layer across data integration, process intelligence, and agentic AI.
One thing will not change from how I worked before. I recommend the right architecture, even when Kestra is not the answer.
Orchestration rarely lives alone. A company might run Celonis for process mining to understand its operations, and Kestra for process orchestration at scale once agentic AI starts acting on those insights. Another might keep Camunda for human-centric, BPMN-modeled approval workflows, and use Kestra for the technical orchestration underneath: the data pipelines, integrations, and agent steps those processes depend on. The two are complementary in both cases. My job is to be clear about where Kestra fits, where it does not, and how it works alongside the rest of the stack.
Credibility is what makes this role effective. I intend to keep mine.
Kestra has its deepest enterprise traction in Europe today. The next move is the United States, where the company is expanding strategically over the coming months and opening a New York presence. I will support that push heavily. Customers in Asia and Australia are already in production, and the Middle East is a deliberate next step.
There is a lot of ground to cover, and a lot of flights ahead to meet customers, partners, and the broader open source community in person.
I am excited to get started.
If you are wrestling with orchestration across data, processes, infrastructure, or agentic AI, I want to be in that conversation. The simplest way: pull me into your architecture discussions. Reach out, and let’s work through the hard problems together.
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