# Fallback to LLM Reasoning

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.

***

### When Does Fallback Trigger?

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

***

### Why Use Fallbacks in Enterprise Workflows?

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.

***

### Example Use Case: Expense Policy Decision Engine

If an invoice exceeds policy thresholds *and* the policy file is missing, the workflow uses fallback logic:

```yaml
- block: Expense Evaluation
  type: condition
  logic:
    - if: invoice_data.amount > policy_memory.threshold
      then: fallback_to_llm
```

The LLM Block is then activated:

```yaml
- 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.
```

***

### Best Practices

* 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

***

### Alternatives

* Use a `Tool Node` connected to a human escalation system
* Write to GLIK Knowledge and queue for asynchronous review


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