Insights / AI architecture
Clear, practical articles on agentic systems, secure RAG, and the architecture decisions between a promising prototype and a system your team can operate.
01 / Start here
Start with the five practical guides: choose the right execution model, evaluate it, constrain its tools and memory, and recover when actions fail. Each includes a reusable worksheet and sources. The foundational articles follow below.
01 / Agentic AI architecture
Choose between deterministic software, an AI-assisted workflow and an agent using a decision matrix, a worked example and a reusable architecture decision record.
Read insight02 / AI evaluation
Build an AI agent evaluation plan with outcome checks, authorization tests, recovery scenarios, honest denominators and a reusable production release-gate template.
Read insight03 / MCP security
Map MCP identity and authority end to end, prevent confused-deputy failures, and use a practical tool contract before connecting agents to business systems.
Read insight04 / Agent memory
Design AI agent memory with explicit write admission, provenance, fresh permissions and deletion of derived state. Includes a reusable memory contract.
Read insight05 / Agentic AI architecture
Design reliable AI agent actions with stable operation IDs, approval-bound writes, explicit unknown outcomes and a reusable recovery contract.
Read insight06 / Enterprise AI architecture
A practical enterprise AI architecture framework covering five runtime layers, three control planes, and the questions that expose production gaps.
Read insight07 / Secure RAG
Design secure, identity-aware RAG with authorization before retrieval, evidence lineage, safe citations, abstention, and separate retrieval evaluation.
Read insight08 / Agentic AI architecture
A practical bounded-autonomy framework for AI agents: goals, scope, authority, budgets, invariants, stop conditions, and evidence before expansion.
Read insight09 / Architecture review
A practical 12-question AI architecture review checklist for business fit, data, RAG, agent authority, security, evaluation, operations, and ownership.
Read insight02 / The point of view
Ardevant starts with the outcome, authority, data, risks, and operating model. Products and models are chosen only after the system responsibilities are clear.
Use these methods to make decisions your team can document, test, and defend in an architecture review—not just discuss in principle.
No platform is presented as a shortcut around authorization, evaluation, security, or accountable ownership.
03 / Put it into practice
Bring your AI use case, proposed architecture, or a decision that is holding your team back. Work directly with Anton to turn it into a clear next step.