Build a runnable Python harness with complete companion files, deterministic fixtures, and independent grading checks. Test controlled context variations before connecting a read-only application.
Test whether irrelevant context changes alter an AI decision. Preserve task, evidence, and policy while distinguishing genuine behavioral differences from changed settings or grading defects.
Evaluate the complete agent release and its enforcement boundaries. Use fault injection, independently checked outcomes, and explicit limits when introducing production authority.
Start AI use case evaluation with a measurable workflow problem. Compare simpler alternatives, apply hard gates, model complete costs, and design a bounded pilot that can disprove the investment thesis.
Multiple AI reviewers can share the same mistake. Design assurance quorums with isolated judgments, protected evidence, deterministic vetoes, and measured marginal value.
Govern dynamic model routing without allowing fallback to inherit production authority. Record resolved models, qualify route members by action, and define when degraded paths must require approval or hold execution.
Test AI agent execution controls with a sixteen-scenario offline lab. Separate authorization, target effects, and completion evidence before validating a real platform.
Prepare a Recursive Trust Benchmark reviewer pilot with isolated inputs, frozen trial assignments, strict response validation, and complete accounting of valid, invalid, and missing results.
A proposed Recursive Trust Benchmark separates reviewer judgment, control testing, and workflow outcomes to assess detection, prevention, evidence, and useful task completion.
Apply the proposed Assurance Independence Model to six trust boundaries. Assess shared failures, require evidence, and use mandatory gates before expanding agent authority.
Use LLM-as-a-Judge for scoped evaluation without confusing a favorable score with proof or permission. Keep authorization and outcome verification independent.
Explore six dimensions of independent AI assurance, with controls that separate agent judgment, authorization, execution and evidence, plus a proposed benchmark.
Explore five scenarios for AI leadership in 2029, with evidence triggers, counterarguments and a practical method for testing enterprise platform commitments.
Assess AI dark horses including Thinking Machines, SSI, Mistral, World Labs and robotics labs, with evidence gates for enterprise pilots and supplier choices.
Keep AI systems correctable as feedback accumulates. Govern knowledge promotion and withdrawal, protect authorization boundaries, reconcile unknown execution outcomes, and test the conditions that should stop the workflow.
Give token entropy, semantic uncertainty, calibration, and abstention distinct jobs. Evaluate how uncertainty changes the next workflow step instead of relying on a single confidence score.
Evaluate infrastructure claims against scoped evidence. Use a two-cluster example to separate supportability, capacity, isolation, and resilience, including the difference between normal and failure-state capacity.
Prevent a tentative AI diagnosis from becoming accepted knowledge through repetition. Preserve source lineage, separate observations from interpretations, and make changed evidence trigger reassessment.
Turn model recommendations into bounded, authorized actions. Define execution contracts, recheck approvals, reconcile uncertain outcomes, and test the full workflow before expanding an agent’s production authority.
Use the double-slit experiment as a bounded analogy for AI context and evaluation. Distinguish presentation effects from changed evidence, and turn the comparison into a controlled testing approach.
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