A multi-agent support flow is still a distributed system. You can make it sound less scary by giving the components friendlier names but the timeouts and the duplicate calls remain part of the system, and so does the question of who owned the handoff that failed.
For example; A triage agent reads a ticket, an account agent fetches customer context, a policy agent checks entitlements, and an action agent calls the order service. Somewhere between the policy decision and the refund API, a worker dies.
At 2 a.m. I care about a couple of things: which step completed, whether the refund can safely repeat, who owned the failed handoff, what an operator can do next, and whether the agent was allowed to make that call at all.
Orkes Conductor's operating model
Orkes Conductor coordinates tasks and workers through workflow definitions stored as JSON, and you can author those definitions in code, through the API, or in a visual builder. The documentation covers static and dynamic workflows, human tasks, event-driven flows, AI tasks, agentic orchestration, and MCP exposure. Orkes also positions Conductor as a platform where existing agents and workflows can invoke one another.
If your platform is already organized around microservice workers and explicit task definitions, that model makes sense. The workflow is a visible coordinator, workers can stay polyglot, and retries and state are handled centrally.
Catalyst's operating model
Catalyst builds on Dapr and treats workflows, agents, MCP servers, and services as workloads on one execution and governance plane. Agents can stay in the frameworks they already run in, while durable runners turn reasoning and tool operations into workflow activities. App identities and policies control which workloads may call other applications or MCP servers.
The interesting part is not the diagram. It is what ends up inside a single runtime boundary: recovery, communication, identity, policy, and operational inspection work the same way for agent and non-agent components.
Walk the failure, not the feature list
Run the same scenario on both platforms.
- Complete triage and account lookup.
- Let the policy agent approve a refund.
- Kill the action worker during the refund call.
- Restore service and watch what recovers.
- Inspect the execution record and the authorization path.
Then ask whether completed steps replay or re-execute, how the refund call is made idempotent, how an operator retries or terminates the run, and how credentials are scoped. A product page that says "durable" will not answer any of that.
Where each may fit
Orkes is a strong candidate if you want Conductor's task-and-worker model stretched across microservices, APIs, humans, and AI, with workflows managed visually and as JSON.
Catalyst is worth evaluating if you already run Dapr, or several agent frameworks, and you want workload identity, policy-controlled communication, durable execution, and deployment governance to come from one platform.
Do not pick on the number of built-in tasks. Pick on the failure contract and the operating contract, because what saves you at 2 a.m. is an on-call engineer who can repair the run without guessing which agent did what.
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