Image by Author
# Introduction
Agentic AI systems, which use large language models (LLMs) to reason, plan, and execute multi-step tasks, promise a new era of automation. However, their non-deterministic nature — producing a different result each time the same piece of data is entered — introduces unique challenges, like LLMs being unpredictable, multi-step workflows failing in the middle of execution, and agents losing important context. Building systems that are not just functional but capable of handling failures and managing state reliably is the key to moving from prototype to production.
In this article, you will learn the five essential design patterns that address these fundamental challenges. Using LangChain and its LangGraph extension as our reference framework, we will look at patterns that provide structure, resilience, and observability to your agentic applications. The following table provides a quick overview of these patterns and their primary benefits.
Pattern
Core Idea
Key Mechanism for Robustness
Ideal Use Case
Single Agent with ReAct Loop
An autonomous agent that plans and acts iteratively
Self-correction through an integrated “thought” step
Open-ended tasks requiring dynamic tool use (e.g., research, analysis)
Multi-Agent Sequential Workflow
A chain of specialized agents passing output linearly
Modularity isolates failures and clear data contracts
Structured, reproducible pipelines (e.g., data extract, clean, load)
Multi-Agent Parallel & Gather
Multiple agents work simultaneously; outputs are synthesized
Latency reduction and diverse perspective gathering
Tasks with independent sub-tasks (e.g., multi-source analysis, validation)
Manager-Controller with State Checkpointing
A central controller manages a persistent, resumable state graph
Fault tolerance via state snapshots and human-in-the-loop intervention
Long-running, complex, or mission-critical workflows
Reviewer-Critic Feedback Loop
A generator’s output is validated by a dedicated critic agent
Quality control through independent, objective validation
Outputs requiring high accuracy or adherence to rules (e.g., code, content generation)
# Implementing Single Agent with ReAct Loop
The foundational pattern for agentic systems is a single agent equipped with tools and guided by the Reason and Act (ReAct) framework. This agent operates in a loop; it reasons about the task and its current state, decides on an action (often using a tool), acts, and then observes the result before repeating the cycle.
Single Agent with ReAct Loop | Image by Author
In LangChain, this is usually implemented using an AgentExecutor. The robustness of this pattern comes from the agent’s ability to adapt its plan based on observations, providing a basic form of error recovery. However, its main obstacle is complexity limitations. As tasks grow more complex, a single agent’s performance can decline.
Implementation focus on LangChain shows robustness here depends heavily on prompt engineering and tool design. Clear tool descriptions and a well-structured system prompt that instructs the agent to “think step-by-step” are critical for reliable reasoning.
# Managing Multi-Agent Sequential Workflow
For complex, structured tasks, the work can be broken down and assigned to a sequence of specialized agents. Each agent is an expert in a specific subtask, and the output of one agent becomes the input for the next in a predefined, linear pipeline.
This pattern increases robustness through modularity and clear contracts. A failure in one agent is contained and easier to debug than a complex failure in a monolithic agent’s logic. For example, in a data pipeline, a “Data Extractor” agent might pass raw data to a “Data Cleaner” agent, which then passes it to a “Data Loader” agent.
Multi-Agent Sequential Workflow with structured data handoffs | Image by Author
The challenge here is the primary risk is context loss or corruption during the transfer of control. Prevent this by enforcing structured output schemas (e.g. JSON) between agents and using a shared state object (as in LangGraph) to pass context cleanly, rather than relying on unstructured natural language.
# Coordinating Multi-Agent Parallel and Gather
When a task can be broken into independent sub-tasks, the parallel pattern can significantly reduce latency. Multiple specialized agents are invoked simultaneously, and their outputs are later gathered and synthesized by a final agent.
Multi-Agent Parallel And Gather | Image by Author
A classic use case is analyzing a customer support ticket where one agent analyzes sentiment, another extracts key entities, a third categorizes the issue, and a final agent writes a summary based on all these parallel analyses.
The challenge this pattern introduces is coordination complexity and the risk of the synthesis step failing due to conflicting inputs. Implement timeouts and circuit breakers for each parallel branch to prevent one slow or failing agent from blocking the entire process. The synthesis agent’s prompt must be designed to handle missing or partial inputs gracefully.
# Utilizing Manager-Controller with State Checkpointing
This is a meta-pattern for setting up complex, long-running, or conditional workflows, best implemented with LangGraph. Here, a central StateGraph defines different nodes (which can be agents, tools, or logic) and the conditional edges (transitions) between them. The graph manages a persistent state object that flows through the system.
The cornerstone of robustness in this pattern is the checkpoint. LangGraph automatically persists the state object after each node execution. If the workflow crashes or is intentionally paused, it can be resumed exactly from the last completed node without repeating work or losing context. This also enables human-in-the-loop patterns, where a human can approve, modify, or redirect the workflow at specific points.
Manager-Controller pattern with a central State Graph for persistence and checkpointing | Image by Author
// Implementation Focus (LangGraph)
Design your state schema carefully, as it is the single source of truth for the workflow. Use LangGraph‘s built-in persistence and interrupt capabilities to build traceable, restarting systems that are reliable enough for production.
# Applying Reviewer-Critic Feedback Loop
Quality assurance can be hard-coded into the system through the reviewer-critic (or generator-critic) pattern. This is often a specialized implementation of a loop pattern. One agent (the Generator) creates an output, which is then evaluated by a separate, independent agent (the Critic or Reviewer) against specific criteria (accuracy, safety, style).
This pattern is crucial for generating high-stakes content like code or legal text. The critic provides an objective, external validation layer, dramatically reducing hallucinations and specification drift.
Reviewer-Critic Feedback Loop | Image by Author
The critic must be genuinely independent. It should use a different system prompt, and possibly even a different large language model, to avoid sharing the generator’s assumptions or reasoning blind spots. Always implement a maximum iteration limit to prevent endless evaluation loops.
# Summarizing Robust Design Patterns
These patterns are not necessarily separate; some of the most robust production systems combine them. You might have a Manager-Controller graph (Pattern 4) that orchestrates a sequential workflow (Pattern 2), where one step involves a parallel gather (Pattern 3) of information, and a final step involves a Reviewer-Critic loop (Pattern 5) to ensure quality.
The path to robustness begins with acknowledging that failures are inevitable in agentic systems. By adopting these structured patterns, especially those enabled by stateful, checkpointed orchestration with LangGraph, you move from building fragile chains of prompts to engineering resilient systems that can handle uncertainty, recover from errors, and provide the transparency needed for continuous improvement and user trust.
I hope this detailed guide provides a solid foundation for designing your robust agentic systems.
Shittu Olumide is a software engineer and technical writer passionate about leveraging cutting-edge technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also find Shittu on Twitter.
