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

Marcos Cruz — Wed, 26 Aug 2026 17:00:53 +0000

The current AI landscape is thick with "smoke." Between infinite buzzwords and thousands of AI posts and infographics, it is becoming increasingly difficult to discern what is actually a new architectural breakthrough versus what is just marketing noise.

With that in mind, here is a breakdown of the 4 core AI design patterns for development:

1. The Pipeline (Prompt Chaining)

The Pipeline is the foundational pattern of AI Engineering. It treats tasks as a linear sequence of specialized nodes, where the output of one instance becomes the structured input for the next. This provides much-needed granularity to the development process.

A classic example of this is Spec-Driven Development. Rather than asking an AI to "build an app," we chain specialized instances together:

Design Node: Establishes the initial concept and architecture.
Specification Node: Translates design into technical requirements.
Task Definition Node: Breaks the spec into actionable tickets.
Development Node: Executes the code based on the tasks.
Review Node: Evaluates the final output against the original design.

In a content pipeline, you might have a technical writer agent generate a draft and a marketing-specialized agent generate a high-engagement title. By separating these into nodes, you can swap out models to match the task—using a high-reasoning model for the spec and a faster, more creative model for the title.

2. The Router (The Decision Maker)

As your system scales, linear flows become too rigid. The Router pattern introduces branched logic, where a "gatekeeper" analyzes the input query and directs it to the most appropriate specialized worker.

In a customer support context, a Router determines whether a query belongs to Billing, Technical Support, or Returns. You can implement this via:

LLM-based Classification: A model categorizes the intent. This is highly flexible but incurs token costs and latency.
Embeddings-based Similarity: The query is converted into a vector and compared against department-specific clusters. This is fast and cost-efficient.

Pro-Tip for Architects: Always implement a Human-in-the-Loop fallback. If the Router’s confidence threshold falls below a set level (e.g., 70%), the system should automatically route the ticket to a human agent. This ensures production reliability even when the model is uncertain.

3. Planner-Executor (The Orchestrator)

The Planner-Executor pattern brings the "Brain and Workers" dynamic to life. This is the preferred pattern for open-ended tasks where the steps to a solution are not known upfront.

The Planner: Acts as the high-level architect (using a powerful model like Claude 3.5 Sonnet). It defines a multi-step plan but does not execute.
The Executors: Specialized workers (often smaller, cheaper models) that carry out the specific steps.

This is best visualized as a Diamond Diagram:

Initial Prompt: The task enters the system.
Fan-out: The system parallelizes multiple tasks at once across different workers to save time.
Fan-in: A final agent performs a synthesis step to merge diverse outputs into a cohesive result.

This tiering of models—expensive for planning, cheap for execution—is the key to building cost-effective, scalable agentic systems.

4. Evaluator-Optimizer (The Adversarial Loop)

To achieve high-quality results, you need an Adversarial Loop. This pattern pits two agents against each other: a Generator and a Critic.

The Critic evaluates the Generator's output against a specific rubric (e.g., "Must be under 30 words," "Avoid vague adjectives"). For example, if the Generator describes a backpack as "good and useful," the Critic rejects it for vagueness and demands specific functional benefits.

The key technical nuance here is the Conservation of Work. In a well-designed loop, the Optimizer is instructed to fix only the specific failures identified by the Critic while preserving everything that already works. This "delta-only" improvement prevents the model from introducing new hallucinations or errors while iterating. This loop-driven refinement consistently outperforms any single-shot prompt, no matter how well-crafted.

Key Takeaways

Agentic Design Patterns represent a fundamental shift in software architecture. We are moving away from monolithic, unmanageable prompts toward semantic separation—the art of organizing modular, maintainable agent logic.

As an Engineer (or AI Engineer, or Agent Architect), your primary value is no longer just writing code; it lies in context engineering and the strategic orchestration of these patterns to simulate reasoning. The more you modularize your logic, the easier it becomes to debug, scale, and optimize.
Is your AI architecture a single-shot "black box," or a structured agentic workflow?

Harness Engineering? Loop Engineering?

Marcos Cruz — Mon, 10 Aug 2026 10:23:12 +0000

In the world of AI, a new concept appears every week. It's nonstop! The reality is that we're constantly bombarded with new technical words, sometimes a repackaged concept and idea with a new, viral name. Many of these concepts even become outdated or are renamed within few weeks.

How do you avoid getting frustrated along the way? The key is not to blindly follow the hype, but to understand what's behind it.

Two terms that have generated the most confusion lately are Harness Engineering and Loop Engineering.

Here's an explanation of their meanings:

Harness Engineering (The tools): This involves preparing the entire ecosystem that an artificial intelligence agent needs to perform its job.
In other words, it's when you implement skills, tools, and connections (like MCPs) for the AI so it can interact with your system.
Loop Engineering (The automation): This refers to the orchestration and design of workflows (loops). Instead of working in the traditional way by typing a prompt in a chat for the AI to do something on a one-off basis (what's called an "open loop"), you design a cycle where the AI acts automatically in response to certain events.
A practical example: Imagine you configure a loop where, if a system detects an error in your code, an AI agent is automatically triggered, analyzes the error, writes a test, fixes it, and sends you the proposed change ready for review.

Hope it helps to clarify! Cheers!