AI Agents: The Case for Eval Driven Development (sdarchitect.blog)

AI agents are now a foundational architecture for AI-enabled systems—gluing LLMs to databases, tools, business logic and other agents via protocols like Anthropic’s MCP and A2A. The article argues that because LLM-driven systems are stochastic (non-deterministic), traditional “test once until change” practices fail. Instead, teams should adopt Eval Driven Development (EDD): write automated evals before building agent code or prompts, run them continuously, and link failures to a full-stack observability pipeline so root causes—LLM drift or hallucination, bad prompts, ETL/data errors, misconfigured tools, inter-agent faults, or security events like prompt injection—can be diagnosed rapidly. Technically, evals go beyond basic accuracy checks to include model drift, bias, risk/compliance, robustness, reliability, security and human-factor assessments tailored to deployment context. EDD mirrors Test Driven Development’s Red-Green-Refactor loop but emphasizes continuous, layered evaluation and auditability: update evals before changes, keep them automated, and use them as evidence for regulators or internal governance. The approach improves iteration speed, surfaces failure modes earlier, and scales trust as agentic systems move from prototypes to mission-critical services; future posts will cover eval frameworks (including NIST AI RMF and domain-specific guidance).