Tutorials on Ready For Production

Understanding when an AI agent is done versus when it’s ready directly impacts business outcomes and development efficiency. The distinction determines whether an agent delivers reliable value or remains a prototype stuck in iteration. Industry trends show rapid adoption of AI agents, with production deployment becoming a priority. However, many teams confuse completion with readiness, leading to costly delays and underperforming systems. As mentioned in the Comparing 'Done' and 'Ready' in Agent Development section, clarifying this distinction is foundational to avoiding these pitfalls. An agent is done when its core functionality is built, but it’s ready only after proving stability and reliability in real-world conditions. For a detailed definition of what constitutes "done," see the Defining 'Done' in Agent Development section. Similarly, the Defining 'Ready' in Agent Development section provides benchmarks for readiness, such as integration with existing systems and alignment with user needs. The A Week In AI newsletter’s observation that "AI == boring == ready for production" underscores the need to prioritize predictable performance over novelty. The "settling" phase described in industry reports refers to the time agents spend adapting to real-world complexity, as outlined in the Understanding Agent Development Stages section. Teams that skip this phase risk deploying brittle systems that require constant fixes.