Following over a hundred enterprise AI agent deployments, Intuz has developed a standardized 12-metric evaluation framework to ensure production readiness. The initiative began after a critical compliance failure where an AI agent hallucinated patient symptoms despite passing standard unit and integration tests. This incident highlighted a lack of production-grade evaluation for hallucination rates, context faithfulness, and tool accuracy. Six weeks later, the team deployed a comprehensive harness that enabled the project to ship successfully. The framework categorizes metrics into four distinct areas: retrieval, generation, agent behavior, and production health. The first category, retrieval, assesses the quality of data fed into the system. Key metrics include context relevance, which measures if retrieved chunks are pertinent to the query, and context recall, which ensures all necessary information is retrieved. Context precision evaluates if the most relevant data is ranked at the top, while retrieval latency tracks the speed of this process, with a target of under 200 milliseconds at the 95th percentile. The second category focuses on generation. Answer faithfulness determines if the model's output accurately reflects the retrieved context, a critical factor for regulated industries. Answer relevance measures whether the response actually addresses the user's specific question. Additionally, the hallucination rate tracks how frequently the model invents facts, aiming for less than 2 percent in general production environments. For agents that utilize tools, the third category adds agent-specific metrics. Tool selection accuracy verifies that the correct tool is chosen for the user's intent, while tool execution success monitors whether those calls complete without errors. Multi-step coherence ensures that logical flow is maintained across complex, multi-turn interactions. The final category measures operational viability. Cost per query calculates the total expense of tokens and infrastructure, aiming for under 5 cents for customer-facing products. P99 latency tracks the end-to-end response time for 99 percent of requests, targeting under 3 seconds for conversational agents to prevent user abandonment. Many teams delay evaluation until after the minimum viable product launches, leading to expensive retrofits and trust issues. Others rely solely on accuracy benchmarks or manual spot checks, which fail to scale or detect real-world hallucinations. This 12-metric framework addresses these pitfalls by requiring instrumentation at every layer before shipping. Implementation typically takes two to three weeks, using LLM-based judges for scalable evaluation and human review for calibration. While various tools like Ragas, TruLens, and LangSmith cover subsets of these metrics, few offer a unified view across all 12 categories. Intuz combines these open-source resources with custom evaluators and standard application performance monitoring to create a cohesive system. The cost of this evaluation infrastructure, often around 30 to 50 percent of inference costs, is justified by the prevention of costly production incidents and the preservation of user trust. Ultimately, successful AI deployment in 2026 depends less on having the best models and more on having the most robust evaluation infrastructure.