5 AI Agentic Workflow Patterns-Reflection, Tools, ReAct, Planning, Multi‑Agent

Key Takeaways

Today, we're breaking down five of the most important AI agentic workflow design patterns you'll see in modern LLM systems. First up is the reflection pattern. In this setup, the user sends a query and the LLM generates an initial answer. Instead of shipping that straight to the user, the system sends the answer back into a reflection step. The model reviews its own output, looks for mistakes, gaps, or weak reasoning, and then iterates a few times to improve the response. After n refinement loops, the final polished answer is returned. This pattern is great for quality sensitive tasks like coding help, reasoning problems, and long form writing where a single pass from a model isn't reliable enough. Next is the tool use pattern. Here the LLM isn't just guessing from its internal weights. It has access to external tools like web search, vector databases, and APIs. The user sends a query. The LLM decides which tools it needs, calls them to fetch fresh or specialized data, and then synthesizes a final answer using those tool results. This is the pattern behind things like retrieval, augmented generation, browsing enabled chat bots, and agents that can look up prices, query internal systems, or call thirdparty services. The third is the reason and act pattern. This takes tool use a step further. The model doesn't just call a tool once. It reasons step by step about what to do next, possibly chaining multiple tool calls together. The workflow typically looks like the user sends a query, the LLM plans or reasons about the next action, calls tools, databases, web APIs, interprets the results, and then either takes another action or generates the final answer. This is useful when you need both non-trivial reasoning and real world actions like booking a trip, orchestrating multi-step data workflows, or running complex business processes. Fourth is the planning pattern. In this pattern, the LLM doesn't directly execute everything. Instead, it acts as a highle planner. The user provides a prompt or a goal and the LLM decomposes it into a sequence of tasks. A task agent or executive then runs those tasks step by step. After each execution, the system can feed results back to the planner, which may replan or adjust the remaining steps. This is powerful for larger projects such as building an app, automating multi-day workflows or coordinating long pipelines where you need structure, dependencies, and the ability to adapt when something changes. Finally, there is the multi- aent pattern. Rather than a single model doing everything, you create multiple agents, each with specialized role. One might be a planner, another a researcher, another a coder, and another a reviewer. Taken together, these five patterns, reflection, tool use, reason and act, planning, and multi-agent form a practical toolkit for designing robust agentic AI systems. You can mix and match them depending on your use case. Add reflection for quality, tools for grounding, reasoning and acting for autonomy, planning for structure.