AI features fail when bolted onto brittle architectures. Treat model calls as first-class dependencies with timeouts, fallbacks, evaluation harnesses, and data governance—just as you would an external payment API.
Separate retrieval, orchestration, and presentation layers. Keep PII out of prompts unless policy, encryption, and retention explicitly allow it. Prefer retrieval over stuffing entire documents into context windows.
Design for failure: model outages, rate limits, and low-confidence answers. Provide deterministic fallbacks and human-in-the-loop paths for high-risk actions.
Evaluation is not optional. Measure groundedness, latency, cost per task, and business outcomes continuously—not just demo accuracy on a curated set of prompts.
Agentic systems amplify both power and risk. Bound tool access, require confirmation for irreversible actions, and log every tool call with identity and purpose for auditability.
Security teams care about exfiltration, prompt injection, and supply-chain risk in model providers. Architecture reviews should include threat models for AI components alongside traditional app security.
Start with one workflow that has clear ROI and measurable quality bars. Productionize that path, then expand. AI-ready architecture is less about the latest model and more about the platform that surrounds it.