Beyond the Hype: 4 Agentic Design Patterns Every Dev and PM Needs to Know

In the rapidly evolving AI landscape, it can be challenging to distinguish between genuine architectural advancements and mere marketing hype. To cut through the noise, four core AI design patterns are essential for developers and product managers to understand:

1. The Pipeline, or Prompt Chaining, treats tasks as a series of specialized nodes. Each node takes the output of the previous one as input, providing greater granularity to the development process. For instance, in Spec-Driven Development, a design node establishes the concept, followed by a specification node that translates it into technical requirements, a task definition node that breaks the spec into actionable tickets, a development node that executes the code, and a review node that evaluates the final output.

2. The Router, or Decision Maker, becomes crucial as systems scale. It introduces branched logic, allowing a gatekeeper to analyze the input query and direct it to the most appropriate specialized worker. This can be implemented via LLM-based classification, which categorizes intent, or embeddings-based similarity, which converts the query into a vector and compares it against department-specific clusters for faster, more cost-efficient results.

A Human-in-the-Loop fallback ensures reliability when the Router's confidence falls below a set threshold.

3. The Planner-Executor, or Orchestrator, brings the Brain and Workers dynamic to life. This pattern is ideal for open-ended tasks where steps to a solution are unknown upfront. The Planner, using a powerful model, defines a multi-step plan without execution, while Executors, often smaller, cheaper models, carry out the specific steps.

This tiering of models—expensive for planning and cheap for execution—results in cost-effective, scalable agentic systems, visualized as a diamond diagram with parallelization and synthesis steps.

4. The Evaluator-Optimizer, or Adversarial Loop, ensures high-quality results through an adversarial process. Two agents, a Generator and a Critic, compete against each other. The Critic evaluates the Generator's output against a specific rubric, rejecting vague or incorrect outputs and demanding specific functional benefits. The Conservation of Work principle ensures that the Optimizer only fixes specific failures identified by the Critic while preserving existing functionality.

This loop-driven refinement consistently outperforms any single-shot prompt, improving upon it with each iteration.