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The patterns behind production agentic AI
Agentic AI in regulated industries lives or dies on a handful of patterns — decisioning, escalation, retrieval, evals, and audit. These explainers define each one in plain language, written so an engineer, a clinical reviewer, and a risk officer all read the same picture.
- ConceptWhat is agentic AI?The loop that separates an agent from a chatbot: plan, call a tool, read the result, decide the next step.Read more
- ConceptWhat is AI decisioning?When the model’s output is a structured, cited decision — approve, decline, escalate — instead of free-form prose.Read more
- ComparisonAgentic AI vs chatbotsWhy a multi-step, tool-using decisioning agent is a different class of system from a conversational assistant.Read more
- RetrievalWhat is RAG?Retrieval-augmented generation — grounding model output in your own documents instead of model memory.Read more
- RetrievalHybrid retrievalWhy vector search alone misses exact-term matches, and how lexical + graph + vector retrieval fix it.Read more
- Safety patternThe confidence-floor patternA configured threshold that force-escalates low-certainty decisions to a human, regardless of what the model said.Read more
- Safety patternHuman-in-the-loop AIThe only viable production pattern for high-stakes decisions — and the three ways to bake it in architecturally.Read more
- Engineering practiceEval gatesCI-blocking tests that run a golden dataset through the live model on every deploy and block on regression.Read more
- AuditAudit-grade citationsPointers from every decision back to the exact policy passages the agent grounded its reasoning on.Read more
- AuditThe AI audit trailAn append-only record of every action the system takes — reconstructable from cold storage years later.Read more
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