Engineering Agentic AI: A Developer’s Guide to 6 Essential Design Patterns

[Mohan-Sharma]

Treating the LLM as an engine within a broader architectural system, we can design software "agents" that reason, utilize tools, and collaborate. However, moving from a single LLM to a multi-agent system introduces significant software engineering challenges regarding state management, latency, cost, and debugging. There is no "one size fits all" architecture.

Choosing the right design pattern is critical to balancing your system's speed, accuracy, and flexibility. Based on established architectural guidelines, this post provides a comprehensive engineering breakdown of the six core agentic design patterns. We will explore the concepts, technical trade-offs for each, moving from fundamental single-agent setups to advanced orchestration patterns.

Building effective Agentic AI systems is an exercise in managing constraints, here is a detailed look at the 6 patterns you need to know to architect your solution correctly.

1. Single Agent Pattern
The Concept: The Single Agent pattern is the most fundamental architecture. A single Large Language Model (LLM) acts as the sole brain, processing the user prompt, utilizing any provided tools, and generating the final response. It does not hand off tasks to other agents.
When to use it: Simple prototypes, straightforward Q&A, or tasks where you don’t need complex routing or rigorous quality checks.
Pros: Simple and highly flexible.
Cons: Lack of control, it can hallucinate or fail if the task is too complex for one prompt.

single agent pattern

2. Sequential Agent Pattern
The Concept:
In this pattern, multiple specialized agents are chained together in a strict, linear order. The output of the first agent becomes the input for the second agent, and so on.
When to use it: Structured workflows where tasks must happen in a specific order (e.g., Extract Data -> Summarize Data -> Translate Summary).
Pros: Reliable and orderly, easy to track exactly where a failure occurs.
Cons: Inflexible. If step 1 fails, the whole pipeline stalls.

sequential-workflow

3. Parallel Agent Pattern
The Concept:
Instead of waiting for one agent to finish before starting the next, this pattern assigns multiple independent sub-tasks to different specialized agents simultaneously. Once all agents finish, their outputs are aggregated.
When to use it: When tasks are independent and speed is critical (e.g., researching a topic across three different databases at once).
Pros: Fast and highly efficient.
Cons: Higher compute costs since you are running multiple LLM calls concurrently.

parallel agents pattern

4. Loop & Critic Pattern
The Concept:
This is an iterative refinement workflow. A "Generator" agent creates an initial output (like a trip plan). A second "Critique" agent evaluates that output against strict criteria. If the criteria aren't met, the Critique agent sends feedback back to the Generator to try again. This loops until the conditions are met or a max iteration limit is reached.
When to use it: Quality-critical tasks with non-negotiable constraints (e.g., "The hotel must be less than 30 minutes from the event venue").
Pros: Ensures the output meets specific criteria and quality standards.
Cons: Higher latency and cost due to multiple iterations. Risk of infinite loops if exit conditions aren't designed carefully.

Loop critic pattern

5. Coordinator (Router) Pattern
The Concept:
Think of the Coordinator as a smart project manager. When a user submits a complex request, the Coordinator agent analyzes it, breaks it down into sub-tasks (hierarchical task decomposition), and dynamically routes those tasks to a team of specialized sub-agents. The sub-agents take full control of their assigned pieces.
When to use it: Dynamic routing for highly complex problems that require different expertise depending on the user's prompt.
Pros: Extremely flexible and capable of handling wide-ranging user intents.
Cons: Complex to debug, and requires extra model calls (and costs) just for routing the task.

orchestrator pattern

6. Agent as Tool Pattern
The Concept:
On the surface, this looks like the Coordinator pattern, but the core difference lies in control and state. In a Coordinator pattern, the manager hands off the project, and the sub-agent takes over. In the Agent as Tool pattern, the primary agent retains full control. It calls a sub-agent like a stateless tool or function (like a craftsman picking up a hammer, using it, and putting it down), retrieves the specific data it needs, and continues managing the overall state itself.

When to use it: When you need a central agent to maintain context over a long interaction but still need specialized help for stateless sub-tasks.
Pros: Full control and state management remain with the primary agent.
Cons: Requires careful architectural design to ensure the main agent doesn't get overwhelmed with context.

agent as tool pattern

Building effective Agentic AI systems isn't about finding the single "best" pattern, it's about choosing the right pattern for your specific constraint.

1. Speed? Go Parallel.
2. Accuracy? Go Loop & Critic.
3. Flexibility? Use a Coordinator.