Getting an AI agent to complete a task is a meaningful milestone. But for enterprise AI agents, “it works” is only the beginning. The bigger question is whether the agent can perform reliably, efficiently and consistently enough to move into production.
That means looking beyond a successful output. Could the agent reach the same result in fewer steps? Could it respond faster or cost less to run? And as the system changes, how can we tell whether performance is truly improving?
We encountered these questions while developing an industrial asset maintenance agent with SAS® Retrieval Agent Manager (RAM). To answer them, we had to look at the agent as a complete system. One with workflow design, tool access, data retrieval, model selection and the specific points where AI reasoning added value.
We also built an evaluation process around quality, cost and latency to see whether each change made the agent better, faster or more efficient.
Why isn’t a working AI agent enough for production?
A working prototype tells us something useful, like whether the agent can complete the task. What it does not tell us is whether the agent is doing the task in a way that will hold up as usage grows.
Those inefficiencies are not always obvious from the final answer. An agent may generate the right response by taking extra reasoning steps, using a powerful model to make simple decisions, or working out how to access data before it can address the business problem. Over time, those choices can lead to higher costs, slower responses and inconsistent quality.
An agent’s performance is shaped by the broader system around it. That includes what work is delegated to the model, how the workflow is structured, what tools the agent can use and how responsibilities are divided. In our case, improvement meant examining all those choices rather than simply tuning prompts or switching to a more capable model.
Finding ways to improve the architecture was only part of the work. We also needed a consistent way to know whether those changes were improving quality, cost and latency. Moving AI agents into production requires more than a working prototype. It requires proportional governance, evaluation and system-level controls that match the agent's autonomy and access.
Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027How to evaluate AI agents for quality, cost and latency
We built a repeatable evaluation process around those three measures. Quality had to be defined in the context of the job the agent was expected to do. For an industrial maintenance agent, success might mean correctly identifying the cause of an equipment issue and recommending the right next step. For a customer intake agent, the definition of success would look different.
We also looked at how the agent reached its answer. How it accessed data, what steps it took and how much time and cost were involved. That gave us a consistent way to compare versions and see whether our architectural choices were moving the agent in the right direction.
How to improve AI agent architecture before production
Based on our observations in the workflow, we focused on four areas.
- Use AI reasoning where it adds value. Not every step needs AI reasoning. For example, determining whether a sensor reading falls within a known range can be handled with simple logic. AI reasoning is better reserved for tasks that require interpretation, judgment or context.
- Make tools fit the task. The original agent sometimes had to reason through how to retrieve structured data before it could address the maintenance problem. More purpose-built tools reduced the extra steps and made the workflow more efficient.
- Specialize responsibilities across agents. We separated diagnosing an anomaly from developing a maintenance plan, with specialized agents handling each responsibility.
- Use the right model for the work. Matching model capability to task complexity helped reduce inference cost and latency while keeping deeper reasoning available where it mattered most.
Together, these changes shifted the goal from asking the language model to do as much as possible to designing the system so AI reasoning was used only where it added value.
Measuring the impact of AI agent optimization
To understand the impact, we evaluated the redesigned agent against the original baseline using the same criteria. In our controlled evaluation set, response quality improved by 68%, inference cost per query decreased by 95% and response latency decreased by 61%.
The lesson was clear. Better system design can improve quality, cost and latency simultaneously. These results came from a controlled evaluation, not a production deployment, but they gave us evidence that the architectural changes were moving the agent in the right direction.
Key takeaways for moving AI agents into production
Our experience pointed to three practical considerations for teams moving AI agents beyond experimentation.
- Define what “good” means for the use case. Quality, cost and latency matter differently depending on the task. Setting expectations early gives teams a clearer way to evaluate progress.
- Think about the system, not just the model. Model choice matters, but so do workflow design, tools, responsibilities and where AI reasoning is applied. Those choices shape both performance and economics.
- Measure as you improve. Agent architectures will continue to evolve as models, tools and requirements change. A repeatable evaluation process helps teams understand whether those changes are actually improving the system.
Moving an AI agent into production takes more than a working prototype. It requires ongoing improvement across the full system. RAM gave us a practical way to bring those pieces together to design, evaluate and iterate.
FAQ: AI agents in production
What does it mean to move an AI agent into production?
Moving an AI agent into production means making sure it can perform reliably, efficiently and consistently in real-world conditions. That requires looking at the full system, including workflow design, tool access, model selection, quality, cost and latency.
How should organizations evaluate AI agents?
Organizations should define what “good” means for the specific use case, then measure whether each version improves quality, cost and latency. A repeatable evaluation process helps teams see whether architectural changes are improving performance.
Why does AI agent architecture matter?
AI agent architecture affects how work is divided, which tools are used, where AI reasoning is applied and which models handle specific tasks. These choices directly influence response quality, inference cost and latency.
