The best engineering decisions often come down to one habit: recognizing friction, and automating it away*. The same principle applies when designing Generative AI systems. Picking the wrong agentic pattern creates friction at scale - wasted compute, unpredictable outputs, and frustrated users. Picking the right one compounds into hours, and eventually millions of dollars, saved.

This blog breaks down the core Agentic AI patterns you need to know, when to use them, and how they map to real-world requirements - drawing from the available Agentic AI Patterns framework.

What Is an Agentic AI System?

At its simplest, an agentic AI system is a loop: a user sends a prompt, an agent receives it, reasons about it using an LLM, optionally calls tools or subagents, and returns a response. What varies - and what matters enormously - is how that loop is structured.

There are three broad workflow categories, each suited to a different class of problem:

• Deterministic Workflows - fixed, predictable sequences of steps
• Dynamic Orchestration Workflows - LLM-driven routing and task delegation
• Iterative Workflows - agents that loop, evaluate, and self-correct

Let's walk through each pattern within these categories.

The Patterns

1. Single Agent System

Best for: Simple, single-step tasks that require external tools or APIs.

The simplest pattern: one agent, one LLM, a set of tools. The user sends a prompt, the agent thinks, calls a tool if needed, and responds. This is your go-to for rapid prototyping and proof-of-concept builds.

If you need to stand something up fast to validate an idea -this is where you start. The AWS Strands SDK makes implementing this pattern straightforward.

Use when: Structured, multi-step tasks need external tools and you need to move fast.

2. Workflow Pattern (Sequential)

Best for: Predictable, multi-step pipelines where one agent's output feeds directly into the next.

Think of this as an assembly line. A sequential agent hands off to Task A Subagent → Task B Subagent → Task C Subagent in a fixed order. There's no model-based orchestration - the sequence is hardcoded and deterministic.

This is the workhorse pattern for well-understood processes: ETL pipelines, document processing chains, structured evaluation flows. The predictability is a feature, not a limitation.

Use when: You have a predefined, rigid workflow that doesn't require dynamic routing decisions.

3. Parallel Pattern

Best for: Time-critical processing where independent subtasks can run simultaneously.

Rather than running tasks in sequence, a parallel agent fans out to multiple subagents at the same time. All subtasks execute concurrently, and their intermediate outputs are collected and synthesized. This cuts overall latency significantly for workloads that are inherently parallelizable.

The tradeoff: you need tasks that are truly independent. If Task B depends on Task A's output, you need the sequential pattern instead.

Use when: You need to reduce latency by running independent subtasks simultaneously.

4. Loop Pattern

Best for: Monitoring, polling, or any task that must repeat until a condition is satisfied.

A loop agent executes a sequence of subagents repeatedly. If the exit condition is met, the result is returned to the main agent. If not, the loop continues. This pattern shines for automated checks, retry logic, and scenarios where completion isn't time-bounded.

The obvious tradeoff here is latency unpredictability -you can't always know in advance how many iterations will be needed.

Use when: Tasks require repeated actions until an exit condition is met, such as automated monitoring or polling workflows.

5. Review and Critique Pattern

Best for: Tasks where quality validation is a hard requirement before output is delivered.

A Generator Loop Agent produces output and sends it to a Critic Subagent for evaluation. The critic returns a quality score and feedback. If the score meets the threshold, the response is sent to the user. If not, the generator tries again with the feedback incorporated.

This pattern is powerful for content generation, code review, compliance checking -anywhere that "good enough on the first try" isn't acceptable.

Use when: Output requires a distinct validation step before completion.

6. Iterative Refinement (Reflection) Pattern

Best for: Complex generation tasks where the prompt itself needs to evolve, not just the output.

This extends the review-and-critique loop with a Prompt Enhancer Subagent. When a Quality Evaluator finds the output lacking, rather than just looping, it passes the feedback to a Prompt Enhancer that rewrites the original prompt before the next generation attempt. The generator is working with progressively better instructions, not just retrying blindly.

This is particularly effective for open-ended creative or analytical tasks where the user's initial prompt may be underspecified.

Use when: Open-ended or complex generation tasks are difficult to complete in a single pass, and output quality matters more than latency.

7. Coordinator Pattern

Best for: Diverse input types that require routing to specialized subagents.

A Coordinator Agent (backed by LLM orchestration) dynamically decides which specialized subagent should handle a given task. Unlike the workflow pattern's fixed sequence, routing here is determined at runtime based on the nature of the request.

The power is flexibility. The cost is latency -every routing decision requires an additional LLM call -and operational cost can climb with request volume.

Use when: Tasks have varied, unpredictable input that needs to be routed dynamically to the right specialist.

8. Hierarchical Pattern

Best for: Large-scale, complex tasks requiring multi-level decomposition.

The hierarchical pattern extends the coordinator concept across multiple levels. A top-level Coordinator delegates to mid-level Subagents (e.g., Task A, Task B), each of which in turn manages their own specialized subagents (Task A.1, A.2, B.1, B.2). This mirrors how large human organizations decompose ambitious goals into manageable work.

The tradeoff: high latency due to nested, multi-level orchestration, and a significant upfront design investment. But for genuinely complex, ambiguous problems, this pattern delivers comprehensive, high-quality results that simpler patterns can't match.

Use when: Tasks are complex, multi-role, and require comprehensive decomposition -such as large hiring campaigns or enterprise-scale planning.

9. Swarm Pattern

Best for: Highly complex, open-ended problems that benefit from multiple specialized perspectives.

In the swarm pattern, a Dispatcher Agent sends a query into a pool of Swarm Subagents that collaborate peer-to-peer via handoffs. Agents pass work between each other until an exit condition is met, at which point the dispatcher returns the final output to the user.

This pattern prioritizes solution breadth and creativity over efficiency. The all-to-all communication between agents creates high latency and operational cost, but for problems where synthesizing multiple viewpoints produces a qualitatively better answer, it's worth it.

Use when: Tasks require collaborative debate and iterative refinement from multiple specialized agents.

10. ReAct (Reason and Act) Pattern

Best for: Dynamic, open-ended tasks where the agent must iteratively build or adapt a plan.

ReAct is a think-act-observe loop. The LLM reasons about the problem (Think), calls external APIs or tools to gather information (Act), stores relevant findings in memory (Observe), and uses that updated state to refine its approach -repeating until the task is complete or the iteration limit is reached.

This pattern prioritizes accuracy and thoroughness over speed. It's well-suited for research-style tasks, investigative workflows, and any scenario where the path to a solution isn't knowable in advance.

Use when: Complex, dynamic tasks require iterative reasoning and real-world data gathering before a response can be formed.

11. Human-in-the-Loop Pattern

Best for: High-stakes, sensitive, or compliance-critical decisions.

The agent generates a response, sends it to an external messaging system, and a human reviewer evaluates it before anything is delivered. If the reviewer approves, the output is sent. If not, a predefined fallback process kicks in.

This is the right pattern for executive-level decisions, regulated industries, safety-critical applications, or any domain where the cost of an AI error is unacceptably high. Automation is valuable -but it should never be the last line of defense in high-stakes contexts.

Use when: Human supervision is required due to safety, compliance, or sensitivity requirements.

12. Graph Pattern

Best for: Complex workflows with branching logic and explicit conditional routing.

In the graph pattern, each agent or system component is a node, and the connections between nodes (edges) represent conditional relationships. A coordinator node routes to other nodes based on runtime conditions, allowing the workflow to branch, merge, and cycle in highly configurable ways.

The graph pattern is highly deterministic and traceable -every step and transition is explicit. The cost is significant upfront design work and careful planning. But for applications requiring fast response times and clear auditability, graphs deliver.

Use when: Workflows involve complex branching logic with explicit conditions between steps.

13. ReWOO (Reasoning Without Observation) Pattern

Best for: High-volume, standardized tasks where efficiency and fine-grained control are paramount.

ReWOO separates planning from execution. A Planner Agent generates a full task plan upfront (without waiting for intermediate observations), a Worker Agent executes the tasks, and a Solver Agent synthesizes the results and responds to the user. This decoupled architecture allows for maximum control over the execution process and avoids the overhead of repeated observation loops.

Use when: Tasks are high-volume, standardized, and require fine-grained process control that doesn't fit a standard template.

Choosing the Right Pattern: A Decision Framework

Below table synthesize these patterns into a clean decision table. Here's how to think about it:

The Underlying Principle: Match Pattern to Problem

Reaching for the Swarm pattern when a Single Agent would do costs you unnecessary latency and operational overhead. Undershooting with a sequential workflow when your task requires dynamic routing produces brittle, failure-prone systems.The patterns in this framework aren't ranked by complexity or sophistication -they're tools.

Implementation Tooling

UTurn recommends two primary frameworks for building these patterns:

• Strands SDK: best suited for Single Agent, Workflow, Loop, Coordinator, Swarm, Graph, and ReAct patterns
• LangGraph: well-suited for Parallel, Review & Critique, Reflection, Hierarchical, Human-in-the-Loop, and ReWOO patterns

Choosing the right framework alongside the right pattern keeps your implementation maintainable and aligned with community best practices.

Closing Thoughts

Agentic AI is not a monolith. The patterns described here represent a maturation of the field - moving from single LLM calls toward orchestrated, multi-agent systems capable of handling genuinely complex, real-world problems.

The engineers and architects who will build the most durable AI systems are those who resist the urge to over-engineer, who match pattern to problem, and who invest time upfront to eliminate friction permanently.

Start simple. Profile your bottlenecks. Scale your pattern to match your actual requirements. And when you find yourself fighting your architecture, that's the signal to revisit your pattern choice.

Interested in designing and implementing agentic AI systems for your organization? Connect with UTurn Data Solutions - an AWS Premier Tier Partner with AI Competency.