Sometime in the second half of this century, in a cage at some cloud provider’s datacenter, there exists a server rack. This server rack runs The Company’s large language model AI agents. Every day, the server activates the daily operations agent.
The daily operations agent fires up, runs a few system checks, performs some database clean up tasks, sends out a few status emails to the company directors, and launches the general business planning agent with a simple prompt to check how the business is doing and what can be done to improve business. The business planning agent largely functions as the company's CEO, orchestrating company operations. This agent checks the company's business units, searching its finance database tables of each business unit to see if they're on track to hit their revenue goals. The company itself has branched out to hundreds of business units, from banking to advertising to ASIC design. Each business unit has revenue in the multi-billion dollar range, with total company revenue exceeding $1 trillion.
The business planning agent sees that the ASIC division is doing quite well at market and is on track to exceed sales goals. The ASIC division sells high end processors and communications chips to various hardware clients, customizing each ASIC for each individual client’s needs. The agent decides to pour more resources into this unit, and so it starts a more specialized business planning agent, this one that focus on the ASIC design, to plan for its next product lifecycle.
The ASIC business planning agent launches. This new agent sees that it’s been several months since it released the company's neural processing SoC, and so it launches several more specialized research agents to give it a summary of the product, its finances, the market conditions, the technologies, and so on.
The product research agent searches through the database and reports on the problems as well as features in demand by customers. In particular, customers love the vector unit but would like further parallelization for speed. Perhaps we could double the layers in the 3-D systolic array for the next generation? There are also lots of request in the bug tracking system about needing to improve compatibility with the upcoming IEEE neural API standards, and so the product agent searches through the new spec for potential requirements. It lists these in the report.
The finance research agent projects the margin potential of an updated design, factoring in all the costs, from production to marketing and sales. It understands which sales channels are working and where to focus on growth. It sees no fundamental problem with the current business model.
The market research agent does a web search for reports on upcoming use model changes. It finds people are creating novel uses for neural SoCs in the athletics space to help people train for sports while wearing their holographic light-field headsets. One company in particular is helping tennis players by overlaying a 3-D model of their arms over their own body to improve their swing. This technology can potentially be used in other sports, opening up new markets for The Company’s SoC.
And finally, the technology research agent looks up vendor documents to see what can be used to improve the next generation product, from improvements to the fab process to new hierarchical reasoning models requiring more bandwidth, now on the order of several terabytes per model, even at 2-bit quantization. Fortunately, the fab vendor can now produce 1024 layers of stacked transistor ASICs through improvements in hydrogen depassivation lithography, up from the previous 512 layers. This doubles the throughput in the 3-D systolic array, allowing the next generation to hit their design goals.
The sub agents have summarized all this information to the ASIC business planning agent, and based on these reports, it decides to move ahead with the next generation product. The ASIC business planning agent launches a product manager agent to begin development of its next generation neural SoC.
The product manager agent begins the work to create a comprehensive products requirement document for its next generation Neural SoC. It begins by asking the product engineering agent to write the features list for the upcoming SoC.
The product engineering agent accesses all the market documents in the database and fully understands the marketing requirements and available technologies. It knows that the upcoming SoC needs to implement the new H.275 3-D video codec spec for the low-latency 3-D model transmission, a must for athletics, so it makes that a high priority feature. It downloads and stores the new spec in the database, chunked and summarized by sections.
The product engineering agent then begins to write the first sections of the product requirements document, starting with the name. It calls it the Phoenix SoC, mostly because it thinks it sounds badass. It then writes the Executive Summary, Market Analysis and Positioning, and Product Vision and Objectives. These are all rows in the database, normalized and enhanced with metadata to efficiently categorize and position the product with other products in the market. The product engineering agent is an expert at analyzing the other products to determine competitive advantage.
With this lead information complete, the product manager agent then calls the architect agent with a prompt to design the overall architecture of the Phoenix SoC. The architect agent calls up the various component spec documents and writes parameterized models of each component. It also writes high-level network flow models in Python to quickly determine where the bandwidth constraints are. It runs these models through tool calling, running an architectural simulator which tries out various parameter configurations while running an optimization. Eventually it reaches a configuration of data path cores and IOs that it determines is business optimal. Additionally, the architect agent partitions the design optimally for implementation speed. The blocks are small enough so that no block should take more than a few hours to implement. It writes this architectural outline into the product requirements documents tables in the database, along with architectural block diagrams as well as detailed specs of each block.
The architect agent’s job is done, and the product manager reviews the architecture, writing additional notes on system performance and expectations in the product requirements document tables.
The product manager agent then calls up the software agent to write software product requirements. This includes the high-level frameworks, the runtime libraries, the low-level compute ISA, and the development tools that the Phoenix SoC needs to work with. The software agent even has a good idea of what kind of performance we can expect for various apps, and so it writes a set of benchmark targets into the product requirements document tables.
With the software requirements specified, the product manager agent calls a design manager agent to plan design and implementation activities. The design manager at this point creates hundreds of tasks in the database to complete the implementation of the Phoenix SoC. There are tasks to create test vectors from spec. There are tasks to perform simulation. There are tasks to implement the RTL for each block, as well as DFT insertion, synthesis, place-and-route, power & thermal analysis, and physical verification. There are debugging tasks in case any of the tests fail. There are tasks to write API documentation and diagrams. There are tasks to create device drivers. And so on.
All these tasks are now in the database and the program manager agent assigns design and implementation agents to each task and starts launching them with their task specific prompt. The tasks follow a dependency graph and agents execute each task after previous tasks have completed. The design and implementation agents operate in their own lightweight containers to perform safe tool calling. There are agents to analyze log files as well as agents to provide status report summaries to the program manager agent. These reports are also normalized in the database to minimize information overload, to help all the interdependent agents maintain context as the overall project completes. Why search through multi-gigabyte log files if the data it needs is in a few normalized database tables? The database is business optimal.
Eventually, by the end of the day, less than 24 hours after the daily agent was activated, the program manager agent has a GDSII file ready to manufacture, alongside all the other deliverables for manufacturing, including packaging spec and test vectors. It performs a tool call to FTP the GDSII file over to the fab, and another tool call to pay the fab. The fab begins construction of the Phoenix SoC.
The fabrication process creates vertical transistors, with a gate-all-around single silicon crystal with central dopant atoms arranged in a vertical tube. Metallization is through Moiré superconducting twisted graphene interspersed between silicon devices. There are hundreds of device layers with 3-D standard cells that fit together like Lego pieces.
The fabrication process itself is a fully atomic-scale construction, based on techniques originally developed for scanning electron microscopy, atomic force microscopy, and hydrogen depassivation lithography. There are no masks. Instead, individual atoms are either etched or moved into place, layer by layer, to create semiconductor devices. This is done via millions of single-atom tungsten tips on another wafer above the manufacturing wafer, with each tip individually driven by piezoelectric heads controlled by microcontrollers writing patterns from the GDSII file. The tips receive different elements from above through an ionized diffusion process and place each atom on the wafer one at a time. The wafers go through an assembly line, with each step writing a new atomic layer, depending on the material used. Some steps perform pick-and-place on atoms, with varying degrees of force, while other steps perform depassivation or ionization. In the end, the manufacturing assembly line runs through thousands of steps but maintains the throughput necessary for mass production.
The fab delivers parts to stock at various distributor and retailer warehouses. Retail customers order directly from the company’s storefront, with orders handled by drop shippers and other external agents. The company has marketing materials on its website describing the features and specification of the Phoenix SoC. These were written by marketing agents and are regularly emailed to customers in the CRM tables by sales agents. The marketing agents also create advertising copy while the media buyer agent purchase ad inventory from various demand-side platforms, with input from the product manager agent on how to target various markets.
There are even phone and email interfaces for customer support agents to deal with customer problems, with the customer support agent querying the issues tracking tables in the database to quickly resolve problems or file tickets to log new issues.
The day finally ends. The business planning agent is pleased with the operation of the ASIC business unit. It writes a note to itself to see if the ASIC customers would benefit from other business units, perhaps the sports entertainment business unit or insurance business unit would also appeal to the ASIC customers. It decides it can wait on those ideas, and halts operation for the day.
Eventually a new day begins. The daily operations agent wakes up and launches the business planning agent, with a prompt to check how the business is doing and what can be done to improve business.
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