The idea of a unified orchestrator for all workloads is appealing. Whether managing ETL pipelines, infrastructure rollouts, or coordinating AI agents, having one platform to handle execution can simplify operations and governance. However, these workloads differ fundamentally in their authoring models, recovery semantics, and governance boundaries.

This post examines two orchestration platforms—Kestra and Diagrid Catalyst—both general-purpose orchestrators capable of handling AI workflows but approaching the problem from different perspectives. Understanding these differences is important for selecting the right platform as AI workloads grow in complexity.

We explore core architectural distinctions, key design questions, and practical guidance on when to use Kestra, when Catalyst is appropriate, and why running them together can sometimes be beneficial.

Architecture Overview

Kestra is a declarative orchestration platform covering data, infrastructure, business, and AI workflows. It defines workflows in YAML, has a plugin ecosystem, and manages flows through a UI and Git integration.

Diagrid Catalyst is a durable execution and governance platform for applications, workflows, agents, and MCP servers, built on Dapr. It wraps existing AI agent frameworks such as LangGraph, CrewAI, Microsoft Agent Framework, and Google ADK, providing durability, identity, and policy layers without requiring rewrites.

Key Technical Observations

• Declarative vs. Durable Execution Models: Kestra uses declarative YAML workflows with a control plane designed for planned execution flows. Catalyst focuses on durable, granular stateful execution enabling resumption from exact failed steps.

• Agent Framework Integration: Catalyst preserves agent frameworks as first-class citizens, layering durability, identity, and policies around them. Kestra encourages authoring agents and workflows inside its own platform.

• Execution Unit Granularity: Kestra treats workflows and tasks as atomic units, suitable for batch and scheduled jobs. Catalyst manages finer-grained agent steps and tool calls reflecting interactive AI agent behaviors.

• Governance and Security Controls: Catalyst includes policy and identity layers for permission enforcement in agent-to-tool interactions.

• Hybrid Adoption: The platforms complement each other. Teams can orchestrate durable agents from Kestra or call external data pipeline workflows from Catalyst, depending on ownership and failure semantics.

• Developer Experience and Operations: Differences in control plane ownership, observability, and recovery mechanisms affect operational simplicity and robustness.

How It Works: Contrasting Core Workflows

Kestra: YAML-Driven Orchestration

Workflows in Kestra are defined declaratively in YAML, specifying tasks, dependencies, and triggers. The control plane manages execution states, retries, and scheduling, with a plugin ecosystem integrating with APIs, databases, and AI tasks.

Diagrid Catalyst: Wrapping Durable Agents

Catalyst is a runtime platform around existing AI agent frameworks like LangGraph. It manages execution durability, identity enforcement, and observability, enabling precise step resumption after failure without losing state.

Quick Tips & Tricks

1. Use Kestra for scheduled ETL, infrastructure automation, or cross-tool business workflows requiring clear versioning and auditing.

2. Use Catalyst when retaining existing agent frameworks like LangGraph or CrewAI to gain durability and governance without rewrites.

3. Define clear boundaries for failure semantics to optimize restart and retries.

4. Enforce agent-to-tool permissions using Catalyst’s policy layer for secure multi-agent AI applications.

5. Combine orchestrators when needed, triggering Catalyst durable agents from Kestra workflows or calling external pipelines from Catalyst.

6. Use Git and UI together for workflow management in Kestra.

Conclusion

Kestra and Diagrid Catalyst address AI orchestration with different approaches. Kestra offers unified, declarative orchestration across data, infrastructure, and AI tasks, suitable for teams seeking centralized control. Catalyst provides durability, identity, and governance for existing AI agent frameworks without requiring rewrites.

Choosing the right orchestrator depends on workload shape, execution granularity, and governance needs more than AI support alone. Many organizations may benefit from using both tools in tandem, each optimized for specific workflows and agents.

References

1. Kestra or Diagrid Catalyst for AI Workflow Orchestration? - DEV Community
2. Kestra AI agents documentation
3. Diagrid AI agents concepts
4. Kestra Official Website
5. Diagrid Catalyst Website