Fallback to LLM Reasoning provides resilience and human-like adaptability in GLIK workflows. It ensures continuity, captures contextual judgment, and keeps workflows flexible when predefined logic is
In enterprise automation workflows, not all decision paths can be fully predefined. When structured logic reaches a point of uncertainty — missing data, ambiguous cases, or exceptions outside the configured rules — GLIK allows workflows to fall back to LLM reasoning.
This mechanism activates the LLM Block in a controlled manner to evaluate a situation, offer natural language judgment, and continue workflow execution.
Fallback to LLM is typically configured within a Conditional Branch or Tool Node decision graph. It is used:
When no matching rule is found in policy memory
When required data is missing (e.g., incomplete invoice fields)
When downstream systems return ambiguous or null responses
As a last step before human escalation
Fallbacks ensure that workflows don’t break or silently fail. Instead, they:
Maintain continuity in data pipelines
Capture human-like reasoning to explain edge cases
Provide transparent justifications for difficult decisions
This is especially valuable in regulated or auditable environments where AI must be able to explain why a decision was made.
If an invoice exceeds policy thresholds and the policy file is missing, the workflow uses fallback logic:
The LLM Block is then activated:
Clearly define fallback conditions; avoid triggering unnecessarily
Use deterministic and scoped prompts (include relevant context)
Pair LLM output with traceable logs or memory writes
Set guardrails to avoid over-reliance
Use a Tool Node connected to a human escalation system
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- block: Expense Evaluation
type: condition
logic:
- if: invoice_data.amount > policy_memory.threshold
then: fallback_to_llm- block: Fallback Reasoning
type: llm
prompt: >
The policy threshold could not be verified.
Please provide an explanation and recommend whether to APPROVE, REJECT, or ESCALATE.