Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Prompt injection is not abstract theory in modern AI-enabled consulting. It is a tangible risk that can compromise client data, breach governance constraints, and slow delivery.
Defensible AI is not about a single model or a flashy deployment. It fortifies AI systems by grounding decision making in proprietary data, disciplined governance, and observable workflows that can be audited and scaled.
Defining a practical test oracle for GenAI in production starts with translating real business expectations into verifiable signals that survive data shifts and prompt evolution.
Decision authority in client-deployed autonomous agents is a governance primitive that makes production autonomy possible without sacrificing safety or compliance.
Decision authority in client-facing AI agents isn't merely a gate; it's a design primitive that shapes reliability, auditability, and modernization outcomes in distributed systems.
Defining Done for probabilistic features in production is about codifying acceptance criteria that ensure probabilistic outputs are reliable, measurable, and governable in real systems.
Acceptance criteria for LLM outputs are not abstract ideals; they are the contract that makes AI-driven workflows auditable, safe, and scalable in production.
Skill files are structured, versioned artifacts that codify how AI systems should think, decide, and act across diverse operational contexts.
Skill files empower AI development teams to codify safety boundaries directly into reusable AI blocks. By turning guardrails, evaluation criteria, and prompt constraints into templates, you gain repeatable, auditable behavior across models and deployments.