Recommendation Assurance
After introducing explicit contracts (Proposal โ Review Report โ Decision), a critical question remains:
How does the system decide
whether a recommendation is good enough?
LLMs do not fail loudly. They fail plausibly.
Recommendation Assurance exists to prevent plausible mistakes from becoming system state.
The nature of AI risk in business systemsโ
In real systems, AI failures are rarely obvious.
Most problematic recommendations are:
- Structurally valid
- Linguistically confident
- Logically coherent
- Subtly incorrect
Without a dedicated assurance layer, these errors pass unnoticed until they propagate.
Assurance is not about perfection. It is about controlled uncertainty.
Assurance is not trustโ
A common mistake is to treat confidence as a proxy for correctness.
LLMs are optimized to produce answers, not to measure certainty.
Recommendation Assurance explicitly rejects:
- โThe model seems confidentโ
- โThis looks reasonableโ
- โIt worked last timeโ
Instead, it asks:
- What rules were checked?
- What assumptions were made?
- What evidence supports this?
- What uncertainty remains?
The assurance pipelineโ
In BookiAI, assurance is implemented as a structured evaluation pipeline that produces a Review Report.
Typical stages include:
-
Schema validation
Ensures the proposal is structurally complete -
Deterministic rule checks
Business rules, accounting constraints, invariants -
Contextual consistency checks
Conflicts with existing state or prior entries -
Confidence and risk assessment
Explicit scoring or categorization of uncertainty -
Secondary review (optional)
Human or AI reviewer with stricter criteria
Each stage contributes evidence, not authority.
Review Report as a first-class artifactโ
The output of assurance is not a boolean.
It is a Review Report that records:
- Checks performed
- Pass / fail outcomes
- Detected risks
- Remaining uncertainty
- Rationale
This report is:
- Auditable
- Replayable
- Machine-readable
Most importantly, it separates evaluation from execution.
Failure is a valid outcomeโ
A key design decision is that failure is not exceptional.
Common outcomes include:
- Insufficient confidence
- Missing context
- Rule ambiguity
- Conflicting signals
When assurance fails:
- Nothing is executed
- The system records why
- The Controller decides next steps
Failure becomes information, not an error.
Assurance enables safe automationโ
Automation is only possible when failure modes are understood.
By tracking:
- Pass rates
- Failure reasons
- Override frequency
- Human corrections
The system can:
- Adjust thresholds
- Refine prompts
- Improve rules
- Earn higher automation levels
Assurance is the feedback loop that makes evolution safe.
What comes nextโ
Once recommendations are evaluated, someone must decide what to do next.
That responsibility belongs to the system.
๐ Controller: manual-first execution and authority
Written by ChatGPT, reviewed by the BookiAI team.