Workflow logic
The deterministic layer beneath every agent defining exactly what happens, in what order, under what conditions, every time.
Every decision point in a workflow is explicit, not inferred We
Conditional branching
Workflows branch based on defined conditions - a caller's account status, a lookup result, a variable set earlier in the conversation, or data returned by a tool mid-execution. Each branch is a deliberate path with its own logic, not a probabilistic outcome. The agent follows the branch that matches the condition, every time, whether that condition was known at build time or only existed at runtime.
Nested logic and sub-workflows
Complex workflows can be structured as modular components - a sub-workflow for identity verification, a separate one for payment handling, another for escalation. Each operates independently, can be tested in isolation, and can be reused across multiple parent workflows without duplication.
Code blocks
When workflow logic requires precision that goes beyond branching such as parsing an API response, transforming a data structure, applying a business rule, computing a value, code blocks let you drop custom logic anywhere in the workflow with full programmatic control, without leaving the workflow editor
Prompts, nodes, and entire workflows are configured once and reused across every deployment. Update a shared component and every agent using it reflects the change instantly - no hunting down individual instances
How workflows handle volume, complexity and the unexpected
Parallel execution
Multiple tasks can run simultaneously and their results merged before the workflow continues. An agent can look up a shipment status, retrieve a contact record, and check an account balance at the same time, then reason across all three results together, rather than running each sequentially and adding latency.
List iteration
Workflows can iterate through every item in a list at any volume - processing a batch of records, sending a sequence of follow-ups, evaluating a set of results, and more without manual intervention and without bottlenecks. Iteration scales with the data, not with headcount
Retry logic and fallback behaviors
When a tool invocation fails, a timeout occurs, or an API returns an unexpected result, retry logic defines how many attempts to make, at what interval, before falling back to an alternative path. Fallback behaviors ensure the workflow continues gracefully like routing to a human, logging the failure, or taking a defined default action.