Agents are inherently probabilistic. Business processes can't be. Orkes combines AI reasoning with deterministic workflows, turning agent behavior into predictable, production-ready execution.
Most agents re-decide their next move at every step, so every run is non-deterministic end to end: hard to debug, hard to govern, hard to trust with real customers or real money.
There's a better split. The agent decides once, reasoning through the problem and emitting a full plan. Orkes Conductor then runs that plan the way it already runs your business processes: retries, human-in-the-loop checkpoints, full audit trails, recovery from exactly where it failed. Non-deterministic once, deterministic after that.
In this session, we'll show that split in action on a real agent workflow: the plan generated, converted into a durable sub-workflow, and executed with production-grade guarantees.
What you'll learn:
How agents and workflows call each other, so AI reasoning and business logic aren't stuck in separate systems
How an agent's one-time plan becomes a fixed, durable Conductor sub-workflow
How Conductor recovers a failed agent run from the exact point of failure, instead of restarting from scratch
How to bring LangGraph, OpenAI Agents SDK, Google ADK, or native agents into this same model
