engineering notes
Engineering Notes
Carbonfay Engineering Notes — dense engineering write-ups: AI-system architecture, context engineering, economics and organization. Not SEO filler, but what we learned in practice.
Adopting AI in a company: where to start and what it costs
AI adoption pays off on specific repeated processes: where to start, how to compute cost and effect, what not to do.
Automating business processes with AI: what actually works
Which business processes AI actually automates — classification, routing, drafts, status reconciliation — and where it doesn't pay off.
Best approaches to AI agents for business: how to measure "best"
How to measure the "best" AI agent for business: reliability, cost of ownership, human control and embeddability — not the model.
Building multi-agent systems: architecture that doesn't fall apart
How to design multi-agent systems that work in production: roles, contracts, coordination, fault tolerance and predictable cost.
Context as the main resource of an AI system
Why an AI system's quality is set by context management, not model size, and how to manage it as an engineering discipline.
Context entropy and the degradation of answer quality
How noise accumulating in context lowers an AI system's answer quality, and which engineering techniques hold it back.
Cost-aware architecture for AI systems
How to design AI systems where cost is an engineering metric alongside latency and reliability, not a surprise at month's end.
Do machines need their own languages to coordinate
Why agents need compact machine representations of meaning instead of natural language, and what it changes in cost and reliability.
Event-driven AI systems instead of simple scenarios
Why a linear scenario breaks on exceptions while an event-driven architecture makes an AI system robust and observable.
Hidden hardcode in AI automation
How wired-in rules and prompt chains turn AI automation into technical debt and why it hits the cost of changes.
How AI compresses operational processes
How AI removes intermediate steps, approvals and waits in operational processes and what it gives in cycle time.
How to build a RAG system that doesn't lie in production
A practical breakdown of building a RAG system: sources, event-based indexing, hybrid search with reranking, and grounding evaluation.
How to compute the payback of AI agents
A model for computing AI-agent payback: what to count as benefit, how to account for token and operation cost, which assumptions are dangerous.
Multi-agent system architecture: roles, contracts, coordination
What a multi-agent system is made of: the agent as an element, input/output contracts, coordination and message exchange between agents.
On-prem RAG: when it's justified and when it's not
When an on-prem RAG system is really needed: data security, the perimeter, cost of ownership — and when the cloud wins.
Orchestrating AI agents in business processes
What AI-agent orchestration is: how to connect agents, tools and people into a managed business process with cost control.
Problems of multi-agent systems and how to avoid them
A breakdown of typical multi-agent failures — looping, context drift, cost growth — and engineering ways to avoid them.
RAG system architecture: sources, indexing, reranking
How a RAG system is built (retrieval augmented generation): sources, indexing, hybrid search, reranking and delivering the minimally sufficient context.
RAG: where it helps and where it creates an illusion of knowledge
When RAG actually raises accuracy and when it merely errs confidently, and how to tell search-over-a-base from understanding the business.
Reducing coordination costs with AI
Why a large company's main hidden cost is coordination, and how an AI layer lowers it without cutting people.
What AI-agent development costs and what drives the price
What makes up the cost of AI-agent development: process, integrations, human control, operation and token cost.
What AI-native engineering is and why it's not "coding with ChatGPT"
What AI-native engineering means: runtime thinking, architecture around models and cost control — and why it's not code generation in a chat.
Why a chatbot is not an AI architecture
How a corporate chatbot differs from an AI system: state, contracts, human control — and why this decides money and risk.
Why AI automation can suddenly become expensive
Where uncontrolled cost growth in AI automation comes from — context length, retries, bad routing — and how to keep the budget.
Why companies need an operational AI environment, not a chatbot
Why a point chatbot doesn't scale, while an operational AI environment lowers coordination costs and gives leaders visibility into processes.
Why natural language is inconvenient for machine coordination
Where natural language creates cost and errors in exchange between agents and how compact representations of meaning solve it.