Design Patterns of Agentic AI
The Design Patterns Nobody Told You About Agentic AI Here's a confession: the first "agent" I built was basically a while loop with a prompt stapled to it. It called a function, read the result, and called another function. I thought I'd invented something. Turns out I'd just rediscovered ReAct, badly, three years after everyone else. That's the funny thing about agentic AI right now. Underneath…
Agentic AI, despite the hype surrounding it, is being driven by a limited set of recurring design patterns. Once you become familiar with these patterns, they start to appear everywhere, from LangChain's documentation to Claude Code's architecture and various open-source repositories.
The first pattern, ReAct, involves a model reasoning, selecting an appropriate tool, executing it, and repeating the process. This pattern allows the model to adapt its approach based on new information it gathers, resembling a detective investigating a crime scene and picking up clues one at a time.
Another pattern is the Planner and Executor, where a planner breaks down a goal into smaller steps, and an executor carries them out. This approach is like bureaucracy, where decisions are inspected before turning into actions. This pattern is beneficial when a module needs to be refactored, allowing for inspection and modification before execution.
Reflection is the third pattern, which involves the model grading its own work by generating an answer and then having a second pass to critique it against the original goal. This process is repeated until the output meets the desired quality, although it is crucial to implement a hard stopping condition to prevent an unbounded loop that could waste resources.
Tool use is the foundation upon which other patterns depend. It involves providing the model with a menu of actions, each with clear names and inputs, allowing it to decide which action to take based on the task at hand. Proper tool design is akin to good API design, with single responsibility, obvious naming, and predictable output shapes.
The Orchestrator and Workers pattern involves a coordinator breaking down a large task into independent chunks and assigning them to specialized workers. This design enables parallelism, as workers run simultaneously rather than sequentially. However, it introduces coordination complexity, which can be mitigated by clearly defining subtasks and final synthesis steps.
Memory is the sixth pattern, which enables an agent to remember its interactions and context. Short-term memory functions as a scratchpad for the current task, while long-term memory persists across sessions. Retrieval discipline is crucial, as it determines which information is relevant and should be retained for the current task.
Finally, the Human in the Loop pattern is a safety mechanism that pauses an agent before taking irreversible actions, such as sending emails, deploying code, or making financial transactions. This pattern ensures that humans have the final say before any external interaction occurs, adding a layer of accountability and control to agentic AI systems.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
