Coderon Training AI Solution Architecture & Guardrails
AI Solution Architecture & Guardrails
Design AI solutions that are safe, measurable and maintainable — guardrails and evaluation from the first decision, not after the first incident.
An AI demo takes a week. The version you can trust in production — with isolation, action control, data protection and quality measurement — takes architectural decisions you have to make up front, because making them later means a rewrite. This training teaches you to design AI like any other critical system: starting from boundaries and guardrails, not from the model.
Who it is for
Training for people responsible for architectural decisions around AI — a natural extension of the Cloud Architecture course. It pays off most for architects and tech leads who have to take an AI solution from pilot to production and own its safety and cost.
How we run it
We work through case studies and real design dilemmas, not a tour of services. Across the course you design the architecture of a sample AI solution: you choose the pattern (RAG, agent, orchestration), set boundaries and isolation, design the guardrails and an evaluation plan, and weigh every decision against the alternatives and their consequences.
What you take away
- A method for choosing an AI architecture pattern — RAG, agent, orchestration — for the problem, not the trend
- Boundaries, isolation and modular architecture that contain the blast radius of a model error
- Guardrails built in from the start: identity, policies, action control and data protection
- Evaluation and observability that catch quality regression before a user does
- The ability to communicate trade-offs — quality, cost, risk — to non-technical stakeholders
AAgenda
AI architecture patterns
- RAG, agents and orchestration
- Boundaries, isolation and modular architecture
- Trade-offs and their consequences
Guardrails and security
- Identity, policies and action control
- Data protection and privacy
- Preventing abuse and leakage
Measurability and maintenance
- Evaluation and observability
- Cost and constraints
- Evolving the solution over time
BWhat you will learn
- Make architectural decisions for AI solutions with full awareness of the trade-offs
- Design guardrails from the first decision, not after an incident
- Measure the quality and cost of an AI solution in production
- Communicate trade-offs to stakeholders in terms of risk and cost
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