Inherent Blog
Practical writing on LLMs, AI Agents, production RAG, company brain, and the systems needed to make AI agents dependable in the enterprise.
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Operational AI memory improves continuity, but only with explicit retention rules. Here is how to decide what the system keeps, discards, and must cite before acting.
Enterprise search and AI search solve different problems. Operations teams need answers that are current, permission-aware, and evidence-backed — not just relevant.
Why strong AI models still produce wrong answers in production — and how the context layer, not the model, is the thing that's missing.
AI answers are only as fresh as your ingestion pipeline. What operations leaders need to know — and a trace worksheet to find where data goes stale.
Capable AI agents still fail in production because operations depend on approvals, exceptions, source quality, and handoffs — not model IQ. Here is the control map.
Context engineering decides what your AI accesses, trusts, and can prove. Here is how CEOs turn it into a durable competitive advantage.
Enterprise AI fails at the discipline layer, not the model. A decade of jet-engine software on the People, Process, Tools sequence leaders get backwards.
A CEO guide to vector search and vector databases: embeddings, similarity, filters, hybrid search, and the trust controls AI retrieval needs.
A CEO guide to semantic search and AI search: when meaning-based retrieval helps, when keyword or hybrid search is safer, and what trust controls matter.
A CEO guide to the RAG pipeline: how source truth, ingestion, retrieval, freshness, permissions, and evidence decide whether AI answers can be trusted.
A practical guide to RAG vs MCP: when to use native search, Google Drive MCP, Confluence MCP, a RAG system, or a layered approach.
A CEO-level guide to RAG architecture tradeoffs: speed, freshness, traceability, cost, permissions, and when retrieval is ready for production.
A practical AI PRD guide for product leaders: define source truth, acceptable wrongness, fallback behavior, review points, and proof.
A product leader's guide to AI agent architecture: tools, memory, orchestration, context, review loops, and where product control belongs.
Use this AI readiness assessment to decide whether one business workflow has the value, context, and controls required to scale AI responsibly.
Retrieval augmented generation helps AI use company context, but RAG alone does not guarantee freshness, permissions, business rules, or proof.
Knowledge base AI turns company knowledge into AI answers. CEOs should care because stale, unowned, or overshared docs create expensive wrong answers.
Context engineering is the discipline of giving AI the right context, permissions, memory, and proof so business agents can work reliably.
A practical, research-backed operating model for organizing company knowledge before scaling internal search, AI agents, and AI tools.
Agentic AI is AI that runs a business loop: observe context, decide, act, check, and escalate. CEOs should define where that loop stops.
AI agents are useful only when the workflow has clear goals, tools, boundaries, context, and review points. Start with the work.
Enterprise AI is not a model purchase. Leaders are buying faster workflows, better decisions, and reliable context for real operations.
Artificial intelligence in business means software that helps predict, classify, generate, or act. Learn the basics before buying AI tools.
A field guide for forward deployed engineers building RAG ingestion pipelines: freshness, permissions, versioning, and retrieval receipts.
A context engine is the governed memory layer that gives AI agents source truth, permissions, retrieval context, and audit receipts before production work.
A practical checklist for product managers who want stronger PRDs by giving AI the source context, evidence, and artefacts each section depends on.
A knowledge base used to help humans find answers. In AI systems, it becomes the governed truth layer agents depend on for reliable context.
Production RAG needs more than retrieval. Learn how source truth, scoped AI memory, permissions, and audit receipts make RAG systems reliable.