Solution Design
Welcome! In this course, you'll learn to design a team of agents that can perform high-value tasks in the real world. From designing a use case to interviewing experts all the way to configuring and orchestrating agents, this course will take you through the process of designing solutions with AMESA.
Learning Objectives
At the end of this course you will be able to:
Define a use case
Complete and document an expert interview
Design and orchestrate a multi-agent system
Course Format
This is a self-paced course designed for you to complete on your own time. Work through each module by watching the videos and doing the practice exercises and quizzes.
This course should take you about 3 hours.
Course Sequence
This course is intended to be completed after the foundational course in this series. In that course, you learned the basics of AMESA and machine teaching, what you can do with a team of agents, and how to identify a use case where a team of agents trained with AMESA will outperform benchmarks.
After you complete this course, move on to the third course in the series. In that course, you will learn to build your team of agents in the AMESA platform.
Course Materials
Case Studies
This course uses a number of case studies for realistic practice. Read them here or download a file with all.
Crude Oil Blending
Oil refineries turn crude oil into everyday products like gasoline, kerosene, and fuel oil. The problem is that not all crude oils are the same. Some are “light” and easy to process, while others are “heavy,” full of impurities like sulfur, wax, and metals. Refineries must work with whatever crude oils are available on the market, which may not match their equipment or product quality needs.
To solve this, refineries blend different crude oils together—like mixing ingredients in a recipe—to get a feedstock that meets product quality standards. For example, heavy crude (cheap but hard to process) can be mixed with light crude (expensive but easier to process) to create a blend that balances cost, quality, and processing efficiency.
The refinery has dozens of possible crude oils available, each with different costs and physical and chemical properties—density, sulfur, viscosity, wax, and metal content—that affect how it behaves when heated and separated. Some properties, like sulfur or density, combine linearly and are easy to calculate by weighted average. Others, like viscosity and stability, are nonlinear, meaning the result cannot be directly predicted from the components. Unstable blends can cause asphaltenes (heavy organic molecules) to precipitate, leading to fouling and reduced throughput.
Refining the crude oil streams into usable products is a multi-step process. The first step requires deciding how much of each crude oil type should be combined in the initial mixing process in an atmospheric distillation unit. This unit then creates several products. Some of these, like kerosene, diesel, and fuel oil, are sold as-is. Another product, naphtha, can be upgraded further in a different piece of equipment called a fixed-bed reformer, to create different octane levels of gasoline. Additional purchased products, including supplemental naphtha or gasoline, can also be added at this phase to increase higher-grade product quantities.
The refining process is continuous, but decisions about crude oil selections and product proportions are made daily to respond to shifting material availability, material cost, and demand for different products. The core automation challenge is to choose inputs, check them against physical and chemical constraints, and find the blending strategy that gives the highest profit while meeting all requirements.
Read a white paper about this use case.
Enzyme Reactor
An enzyme production facility must control conditions within a reactor to ensure that microbes can thrive and produce enzymes efficiently. The system relies on a continuous feed of nutrient “food.” First, the food is pasteurized before entering the reactor. If food is not heated to the proper range—170°F for at least three minutes or 175°F for 2.5 minutes—harmful microbes could contaminate and wipe out the enzyme-producing culture. But heating the food above 190°F creates another problem: caramelization, which fouls equipment and strips away essential nutrients.
Inside the reactor, conditions must be carefully balanced. Microbes grow optimally at 98°F, 17.5% dissolved oxygen, and pH 7.5. Deviations from these values quickly affect growth. For instance, a scarcity of food reduces growth rates and permanently damages some microbes’ ability to reproduce, even if nutrients are restored later. Too much food, however, doesn’t increase growth, since microbial metabolism has a limit. In addition, as microbes multiply, they generate heat, raising the reactor temperature. If left unchecked, this excess heat can push temperatures above 105°F, halting reproduction altogether.
pH is another delicate factor. Growth byproducts are basic, requiring careful acid addition to maintain balance. Both acid and base are adjusted through manual valves, and errors in timing or dosing risk destabilizing the reactor environment. Oxygen also plays a pivotal role; too little dissolved oxygen slows growth, while too much can stress the microbes.
The feed rate itself creates trade-offs. If flow is too fast, microbes are flushed out before reproducing. If it’s too slow, they starve. The “residence time”—how long microbes remain in the reactor—must align with their growth cycle. At startup, flows must remain low to allow the microbial population to build up, but later in continuous mode, flow and level setpoints must be carefully tuned to maintain steady enzyme production.
The overarching challenge is balancing all of these competing factors: feed temperature, flow rates, pH control, oxygen levels, and microbial growth. Every adjustment has consequences across the system, and missteps at one stage ripple through the process. A failure to manage these interdependencies can lead to stalled growth, contamination, wasted feed, fouled equipment, or drastically reduced enzyme yields.
Read a white paper about this use case.
Nitrogen Manufacturing
Air separation units (ASUs) are large-scale industrial facilities that extract nitrogen, oxygen, and argon from air using energy-intensive cryogenic distillation. They are critical to industries such as steel, chemicals, and electronics, where steady supplies of high-purity gases are required.
The process relies heavily on electricity, particularly for compressors and refrigeration cycles, making it highly exposed to fluctuations in electricity prices. For example, electricity tariffs can vary hourly, with moderate swings on some days and extreme peaks on others. Small variations in energy price can drastically affect profitability.
The production system itself is complex. Feed air is compressed, cooled through multi-stream heat exchangers, and then separated in a distillation column. Supporting units include turbines, liquefiers, evaporators, and large storage tanks for liquid nitrogen. These storage systems provide flexibility: the plant can liquefy and store nitrogen when power is cheap and later vaporize it to meet demand when electricity is expensive or production must be reduced. However, relying too much on storage introduces risks of tank depletion, overfilling, or excessive refrigeration costs.
The ASU must also adhere to strict product quality and safety requirements. The nitrogen stream must maintain impurity levels below 1500 ppm, temperature differences in critical heat exchange equipment must stay above 2 K, and reboiler inventories must remain within physical limits. Any violation could compromise safety or lead to off-spec product.
Complicating matters further are external shocks. Customers may unexpectedly reduce demand, for instance, during unplanned maintenance at downstream plants. Conversely, sudden spikes in demand may stretch capacity.
The central problem is therefore one of coordinating production, storage, and energy use under uncertainty and fluctuating market conditions. Decisions occur on different time scales: long-term scheduling of production and storage, medium-term adaptation to electricity pricing patterns, and short-term operational control of compressors, turbines, and distillation units. Poorly timed choices—such as ramping production too late, overusing storage, or failing to anticipate price shifts—can lead to financial losses, excessive energy costs, or constraint violations.
Read a white paper about this use case.
Production Scheduling
he manager of an industrial bakery oversees a daily operation that produces cakes, cupcakes, and cookies at scale. Each product line follows the same three-step process—mixing, baking, and decorating—but with different time requirements and costs. For example, a batch of cookies requires only five minutes to mix, 13 minutes to bake, and 10 minutes to decorate, while a batch of cupcakes takes seven minutes to mix, 30 minutes to bake, and 20 minutes to decorate. Cakes take the longest, with 10 minutes of mixing, 40 minutes of baking, and 30 minutes of decorating. The bakery operates with two mixing units, three ovens, and two decorating stations, all of which must be shared across the three product types.
Complicating matters, the bakery employs four bakers, each with different skills and assignments. Once a baker begins mixing or decorating, they remain tied to that task until it is finished, and cannot be shifted to another role midstream. This constraint means the manager must constantly anticipate bottlenecks: an idle oven waiting for batter, or a decorating station delayed because cakes are not yet ready.
On the financial side, each product has a sharply different cost-to-revenue ratio. A cake costs about $10 to produce but sells for $42, while cupcakes cost $7 per batch to make and sell for $30. Cookies, though quick to prepare, have the widest range—$24 per batch in costs but a $60 selling price. The manager must therefore weigh whether to focus on high-margin cookies, high-demand cupcakes, or large-format cakes, knowing that an imbalance can erode profit or leave customer demand unmet.
External pressures add to the complexity. Demand fluctuates from day to day. Predictable fluctuations include higher demand for cakes on weekends and during the summer wedding season, for cupcakes on weekdays during the school year, and for cookies during the holiday season. But demand can also vary unpredictably, and ingredient costs can likewise rise without warning. Overproduction risks waste, spoilage, or costly storage, while underproduction may result in late deliveries, penalties, or lost sales. Transportation schedules and customer expectations compound the stakes.
Every day brings 480 individual scheduling decisions across an eight-hour shift, each one shaping the bakery’s overall profitability.
Read a white paper about this use case.
Industrial Mixer
In the industrial mixer use case, raw materials are stirred together inside a tank, undergoing a reaction that produces the desired end product.
The goal of the process is to convert as much of the raw material as possible. But as the chemicals mix and the conversion occurs, the tank heats up. If the temperature gets too high, a condition called “thermal runaway” occurs, potentially causing explosions and fires.
To produce as much chemical as possible, the operator must constantly adjust the temperature in the tank, keeping it high enough to allow productivity but low enough to avoid any thermal runaway risk.
As in all machine teaching use cases, this process can be summarized in the form of a goal (maximize yield) and a constraint (avoid thermal runaway) that must be balanced against each other:
The process is controlled by adjusting the mixture's temperature in the tank using a "jacket" filled with coolant. Lowering the coolant temperature in the jacket lowers the temperature in the tank, decreasing the risk of thermal runaway.
However, cooling the tank can also reduce yield. By how much? The answer varies unpredictably – temperature changes affect chemical concentration differently at different parts of the reaction. That nonlinear relationship between temperature and yield is why this is a nuanced process that benefits so much from intelligent automation.
Read a white paper about this use case.
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