AI Architecture Thinking: From Idea to Impact [Testμ 2026]

At Testμ Conf 2026, Elsevier principal technical evangelist Safeena Banu described her team’s agentic quality-engineering stack and argued that production AI systems should be designed as distributed systems containing probabilistic components rather than as isolated models or prompts. She outlined a layered architecture beginning with governance and observability, followed by context and memory, tool contracts, model routing and reasoning, orchestration, and user interfaces, emphasizing that security, evaluation, cost, auditing, and human usability must be built in from the start. Her team evolved from a single agent into specialized components for planning, inspection, execution, healing, review, and deployment monitoring, while retaining deterministic scripts for tasks such as test execution and limiting each agent’s authority, context, tools, and failure domain. Banu advised treating tools as enforceable API contracts rather than natural-language instructions, isolating untrusted retrieved context from trusted state, routing models according to task requirements, and evaluating the full workflow trajectory rather than merely whether a test passes. She stressed that autonomy is delegated control flow and should be kept as low as possible for the desired outcome, particularly when actions can alter or irreversibly affect systems, with approval gates and audit trails supporting higher-risk operations. She also cautioned against trusting third-party skills or MCP servers solely because of popularity, recommended sandboxing untrusted inputs, and concluded that a system is not ready until each layer has a clear contract, owner, failure mode, and success metric.

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