How senior developers leverage multi-agent automation to design, test, and ship complex applications while shifting focus from boilerplate syntax to domain architecture.
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.
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.
"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.