Assessing the Quality of AI Decisions Before and After Execution

Decision Evaluation is the systematic process of assessing AI decisions for quality, appropriateness, and outcomes—including both pre-execution evaluation (will this decision be good?) and post-execution evaluation (was this decision good?). It provides the feedback loop that enables AI decision-making to improve over time.

Pre-execution evaluation assesses decisions before they're implemented. This evaluation can catch problems while they can still be prevented: decisions that violate policy, that fall outside agent authority, that seem inconsistent with precedent, or that carry unusual risk. Pre-execution evaluation is the last line of defense before decisions become consequences.

Pre-execution evaluation mechanisms include policy compliance checking (does this decision comply with applicable policies?), authority verification (does this agent have authority for this decision?), anomaly detection (does this decision deviate unusually from typical patterns?), risk assessment (does this decision carry risks that require additional review?), and consistency checking (does this decision align with how similar situations have been handled?).

Post-execution evaluation assesses decisions after their outcomes are known. This evaluation enables learning: which decisions worked well, which didn't, and what factors distinguish successful from unsuccessful decisions. Post-execution evaluation is the foundation for continuous improvement.

Post-execution evaluation requires outcome tracking—connecting decisions to their consequences. For some decisions, outcomes are immediately visible: the customer was satisfied or wasn't, the system stabilized or didn't, the transaction completed or failed. For others, outcomes emerge over time: the approved hire performed well or poorly, the approved investment returned or lost value, the approved policy change had intended or unintended effects. Decision Evaluation must accommodate different outcome timeframes.

The connection between decisions and outcomes isn't always straightforward. Multiple factors influence outcomes; the decision is only one. A loan might default due to economic conditions rather than approval decision quality. A customer might churn due to product issues rather than service decision quality. Decision Evaluation must account for these confounding factors, assessing decisions against what was knowable at decision time rather than against outcomes that no one could have predicted.

Context OS implements Decision Evaluation through the Decision Review Agent and associated analytics infrastructure. Decisions are automatically assessed at execution time against policy and consistency criteria. Outcome tracking links decisions to subsequent events. Analytics identify patterns in decision quality—which decision types perform well, which struggle, which factors predict success or failure.

Decision Evaluation results feed multiple consumers. Operational teams use evaluation insights to improve agent performance and adjust processes. Governance teams use evaluation data to assess whether agents merit authority expansion or require tighter boundaries. Audit teams use evaluation records to demonstrate oversight and due diligence. Strategic teams use evaluation patterns to understand how well AI systems are serving organizational objectives.

The feedback loop between evaluation and improvement is crucial. Without evaluation, there's no basis for improvement—agents continue making decisions without knowing which approaches work better than others. Without improvement based on evaluation, the organization invests in assessment without benefit. Decision Evaluation must connect to action: adjusting policies, refining agent capabilities, modifying authority boundaries, updating the Organization World Model.

Decision Evaluation also supports the Trust Benchmark framework. Benchmarks are measured through evaluation—accuracy rates, consistency measures, boundary compliance. An agent's progression through autonomy levels depends on evaluation evidence. Without robust evaluation, Trust Benchmarks become subjective rather than evidence-based.

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