Edges & Routing

Edges define the execution flow between Nodes in a Workflow. Each edge connects a source node to a target node, with an optional natural language condition that determines whether the edge is followed.

Fields

Field	Type	Required	Default	Description
from	string	REQUIRED	—	Source node ID. MUST reference a key in nodes.
to	string	REQUIRED	—	Target node ID. MUST reference a key in nodes.
when	string	OPTIONAL	—	Natural language routing condition.
max_iterations	integer (≥ 1)	OPTIONAL	unlimited	Maximum times this edge can be followed.

Routing Algorithm

After a node completes, a conforming executor MUST resolve the next node using the following algorithm:

1. Collect all outgoing edges from the completed node (from equals the current node ID).
2. Exclude edges that have exhausted their max_iterations count (i.e., the edge has been followed max_iterations times already).
3. If zero edges remain: execution is complete. The current node is a terminal node.
4. If exactly one edge remains and it has no when clause: follow it unconditionally. The executor MUST NOT invoke the AI model.
5. Otherwise: present all remaining edge conditions to the AI model. The model evaluates each when clause against the accumulated context (workflow input + all prior node results) and selects one edge. The executor follows the selected edge.

Unconditional Edges

An edge without a when clause is unconditional.

If a node has exactly one unconditional outgoing edge and no conditional edges (after filtering exhausted edges), the executor MUST follow it without invoking the AI model. This is a deterministic transition — no evaluation required.

edges:
  - from: gather
    to: investigate

Conditional Edges

An edge with a when clause is conditional. The when field contains a natural language condition.

edges:
  - from: investigate
    to: create_issue
    when: novel_count is greater than 0 AND highest_severity is medium or higher
  - from: investigate
    to: skip
    when: novel_count is 0, OR highest_severity is low

A conforming executor:

• MUST evaluate conditions by providing the AI model with the accumulated context (workflow input + all prior node results) and the list of conditions as choices.
• MUST NOT evaluate conditions programmatically. The AI model is the evaluator.
• MUST follow the edge selected by the AI model.

When a node has a mix of conditional and unconditional outgoing edges, the unconditional edge serves as a default path. The executor presents the conditional edges as choices, with the unconditional edge as a “none of the above” fallback.

Terminal Nodes

A node with zero outgoing edges (after filtering exhausted edges) is a terminal node. Execution ends when a terminal node completes.

A workflow MUST have at least one reachable terminal node (otherwise it would loop forever or have an unbounded cycle, which is structurally invalid).

Bounded Cycles

The max_iterations field limits how many times an edge can be followed, enabling controlled retry loops.

edges:
  - from: review
    to: fix
    when: review found issues that need fixing
    max_iterations: 3
  - from: review
    to: done
    when: review passed, no issues found
  - from: fix
    to: review

A conforming executor:

• MUST track how many times each edge has been followed (keyed by from/to pair).
• MUST exclude an edge from routing once it has been followed max_iterations times.
• If excluding an exhausted edge leaves zero remaining edges, the node becomes terminal.

Self-Loops

A self-loop is an edge where from equals to. Self-loops MUST have max_iterations set. A workflow containing a self-loop without max_iterations is structurally invalid.

edges:
  - from: retry
    to: retry
    when: operation failed and retries remaining
    max_iterations: 3
  - from: retry
    to: done
    when: operation succeeded

Unbounded Cycles

A cycle in the graph where no edge in the cycle has max_iterations is invalid. A conforming validator MUST detect and reject unbounded cycles.

The detection algorithm: remove all edges that have max_iterations set (and all self-loops), then check the remaining subgraph for cycles using depth-first search. If a back-edge is found, the cycle is unbounded.

Examples

Linear Flow

edges:
  - from: gather
    to: investigate
  - from: investigate
    to: notify

Execution: gather → investigate → notify (terminal).

Conditional Branching

edges:
  - from: investigate
    to: create_issue
    when: novel_count is greater than 0 AND highest_severity is medium or higher
  - from: investigate
    to: skip
    when: novel_count is 0, OR highest_severity is low
  - from: create_issue
    to: notify
  - from: skip
    to: notify

After investigate completes, the AI model evaluates both conditions against the investigation results and selects the matching branch. Both branches converge at notify.

Bounded Retry Loop

edges:
  - from: implement
    to: test
  - from: test
    to: implement
    when: tests failed
    max_iterations: 3
  - from: test
    to: done
    when: all tests passed

The test → implement edge can be followed at most 3 times. After 3 retries, the edge is excluded. If all tests passed also doesn’t match, the node becomes terminal.