Picking between OpenAI and Anthropic is a smaller decision than most briefs make it out to be - the real work is a prompt treated as a spec instead of a vibe, structured outputs and tool calling doing the parsing instead of regex, and context management once a conversation outgrows the window. What actually goes into wiring an LLM API into a real product, including where multimodal input fits.
OpenAI APIAnthropic APIPrompt EngineeringContext ManagementStructured OutputsMultimodal AI
An "AI agent" that automates a business process is not one model call - it is context engineering, tool/function calling, structured outputs validated before they touch a real API, embeddings and a vector database when search is genuinely the job, and a human checkpoint before anything irreversible ships. What actually breaks in these builds, and where I put the human back in.
AI AgentsWorkflow AutomationTool CallingHuman-in-the-LoopVector DatabasesContext Engineering
"Add AI" is not one feature. It is streaming responses wired with the Vercel AI SDK, Figma-to-Code drafts pulled through an MCP server, an internal tool that reads a screenshot instead of a support ticket, and a decision about which of those is worth building first. What each of these actually looks like in a real codebase, and where multimodal AI earns its cost.
Vercel AI SDKStreaming UIMCPMultimodal AIAI-assisted developmentNext.js
ChatGPT, Gemini, and the chat widget on someone else’s site are already recommending vendors and answering buyer questions. A bot can only hand you a lead if it can read something that names you first - a profile inside someone else’s app rarely qualifies. What “own article, blog, page with a domain” actually means for lead generation, and the minimum setup I would build this month.