Anthropic

After building the specialist agents, the output looked impressive. It was not useful enough. The system produced: 12 technical findings 14 risk findings 10 cost findings timeline findings That is a lot of analysis. It is also a lot to read. The coordinator is the piece that turns those separate findings into something a person can act on. Aggregation is not synthesis The first version of the coordinator just ran the agents and returned their results. ...

The first post covered why I split document analysis into multiple agents. This one covers how the specialists are actually built. The Python code is not the hard part. The specialist behavior mostly comes from: the system prompt the output schema the boundaries around what the agent should ignore The code is intentionally repetitive. Once you’ve written a couple agents, it’s a breeze. The shared base class Every agent needs the same basic execution logic: ...

I built a document analysis system for RFPs and contracts using multiple specialist LLM agents instead of one general-purpose prompt. The architecture is simple: PDF → text extraction → Technical Analyzer → Risk Analyzer → Cost Analyzer → Timeline Analyzer → Coordinator synthesis → final report The interesting part is not that it calls an LLM. That’s easy. The interesting part is how much the output changes when the model is forced to analyze the same document through different lenses before producing a final answer. ...

The previous posts focused on search. This one turns to the AI assistant — a floating chat widget that answers product questions, recommends complementary gear, and builds camping loadouts on request. Under the hood, it is a multi-turn conversation system with history, context injection when viewing a product, and dynamic retrieval when the query requires cross-product knowledge. What the assistant does Three core capabilities: Product Q&A — user is viewing a tent, asks “Is this waterproof?”, assistant answers from the product description without retrieving anything. ...

I built Ozark Ridge, a mock outdoor gear retail site with AI-powered product search and a Rufus-style product assistant. The project exists to demonstrate RAG (Retrieval-Augmented Generation) in a realistic e-commerce context. This is the first post in a series documenting the build. This one covers the architecture and stack decisions. Later posts cover the RAG pipeline, keyword vs semantic search comparison, and building the AI assistant. What it does Two features: ...