Agentic Design Primer
Vinija Jain published a primer that organizes LLM systems into patterns like prompt chaining, routing, reflection, tool use, planning and multi‑agent coordination. (x.com) The guide lays out explicit structures for decomposition, validation and error recovery intended to help teams translate research patterns into product implementations. (x.com)
Large language model systems are moving from single prompts to step-by-step workflows, and Vinija Jain has published a primer that maps those workflows into reusable design patterns. (vinija.ai) Jain’s guide groups agent behavior into patterns including prompt chaining, routing, parallelization, reflection, tool use, planning, prioritization, pattern composition, and multi-agent systems. Her site identifies her as a Staff Engineer at Google working on generative artificial intelligence and agentic systems with Gemini. (vinija.ai 1) (vinija.ai 2) The basic idea is simple: instead of asking one model call to do everything at once, a system can split work into smaller calls, send tasks to specialized branches, check its own output, or call outside tools such as search and code execution. Anthropic described the same shift in December 2024 as a move toward “simple, composable patterns” for teams building large language model agents. (vinija.ai) (anthropic.com) Reflection is one of the clearest examples. DeepLearning.AI described it in March 2024 as a loop where a model drafts an answer, critiques that answer, and then revises it, sometimes with outside checks such as unit tests or web search. (deeplearning.ai) Tool use and planning push the same logic further. OpenAI’s documentation says reasoning models can be run with adjustable reasoning effort, while its Agents software development kit defines an agent as a model configured with instructions, tools, guardrails, and structured outputs. (developers.openai.com) (openai.github.io) Multi-agent design takes the decomposition idea from one workflow to a team of workflows. Jain’s primer lists network, supervisor, supervisor-as-tool, hierarchical, and custom topologies, while Google Cloud’s Agent Development Kit materials argue that enterprises often get better results from specialized agents than from one general-purpose “super” agent. (vinija.ai) (cloud.google.com) The primer arrives as more vendors are turning these patterns into product documentation and software kits. Google published a multi-agent pattern guide for its Agent Development Kit in January 2026, and Anthropic, OpenAI, and others now document routing, orchestration, and tool-calling as standard building blocks rather than research demos. (developers.googleblog.com) (anthropic.com) (openai.github.io) What Jain adds is a single map that ties those pieces together with implementation sections, failure modes, and composition rules. That makes the primer less a theory document than a field guide for teams deciding when to chain prompts, when to branch work, and when to add review loops or extra agents. (vinija.ai)
