Photo by author
# Introduction
Agenty AI systems, which use Major language models (LLMs) to reason, plan, and execute multi-step tasks, promising a new era of automation. However, their nondeterministic nature—producing a different result every time the same piece of data is entered—introduces unique challenges, such as the unpredictability of LLMs, Multistep workflow is failing. In the middle of an execution, and agents are missing important context. Building systems that are not only functional but also able to handle failures and manage state reliably is the key to moving from prototype to production.
In this article, you’ll learn five essential design patterns that address these fundamental challenges. to use Lang China And his Lang Graf Expanding on our reference framework, we’ll look at patterns that provide structure, flexibility, and observability to your agent applications. The following table provides a quick overview of these models and their primary benefits.
| The pattern | Basic idea | Key mechanisms for robustness | Ideal use case |
|---|---|---|---|
| Single agent with ReAct loop | An autonomous agent that plans and works iteratively. | Self-correction through an integrated “thinking” step | Open-ended tasks require dynamic tool use (eg, research, analysis). |
| Multi-Agent Sequential Workflow | A series of specific agents that linearly transmit output. | Modularity isolates failures and cleans data contracts. | Structured, reproducible pipelines (eg, data extraction, clean, load) |
| Multi-agent parallelism and aggregation | Multiple agents work simultaneously. The outputs are synthesized. | Latency reduction and diverse perspective gathering | Work with independent subtasks (eg, multisource analysis, validation) |
| Manager-controller with state checkpointing | A central controller manages a persistent, restartable state graph. | Fault tolerance through state snapshots and human intervention | Long-running, complex, or mission-critical workflows |
| Evaluator Critic Feedback Loop | The output of the generator is validated by a dedicated critic agent. | Quality control through independent, objective verification | Output that requires high accuracy or adherence to rules (eg, code, content creation) |
# Implementing a single agent with a ReAct loop
The basic paradigm of agentic systems is a single agent equipped with tools and guided by a cause-and-act (react) framework. This agent works in a loop; It reasons about the task and its current state, decides on a course of action (often using a tool), executes the action, and then observes the result before repeating the cycle.
Single Agent with ReAct Loop | Photo by author
i Lang Chinathis is usually implemented using a AgentExecutor. The robustness of this pattern derives from the agent’s ability to adapt its plan based on observations, providing a basic form of error recovery. However, its main limitation is complexity limitations. As tasks become more complex, the performance of a single agent may decline.
Focus on implementation. Lang China The robustness shown here relies heavily on prompt engineering and tool design. Clear tool description and a well-structured system prompt that instructs the agent to “Think step by stepCredibility is critical to reasoning.
# Managing multi-agent sequential workflows
For complex, structured tasks, the task can be broken down and assigned to a sequence of specialized agents. Each agent specializes in a specific subtask, and the output of one agent becomes the input for the next in a predetermined, linear pipeline.
This pattern increases robustness through modularity and explicit conventions. A failure in an agent is contained and easier to debug than a complex failure in a single agent logic. For example, in a data pipeline, a “data extractor” agent might pass raw data to a “data cleaner” agent, which then passes it on to a “data loader” agent.
Multi-Agent Sequential Workflow with Structured Data Handoff | Photo by author
The challenge here is that there is a fundamental risk of context loss or corruption during transfer of control. By implementing structured output schemas (such as JSON) between agents and using shared state objects (such as Lang Graf) to convey clean context rather than relying on unstructured natural language.
# Multi-agent parallelization and aggregation
When a task can be broken down into independent subtasks, the parallel pattern can significantly reduce latency. A number of specialized agents are invoked simultaneously, and their results are later collected and synthesized by a final agent.
Multi-agent parallelism and aggregation Photo by author
A classic use case is analyzing a customer support ticket where one agent performs sentiment analysis, another extracts key entities, a third classifies the problem, and a final agent writes a summary based on all these parallel analyses.
The challenge this paradigm introduces is the risk of the synthesis step failing due to the complexity of coordination and conflicting inputs. Implement timeouts and circuit breakers for each parallel branch to prevent a slow or failed agent from stopping the entire process. The synthesis agent’s prompt should be designed to handle missing or partial inputs gracefully.
# Using manager-controller with state checkpointing
It is a meta-pattern for configuring complex, long-running, or conditional workflows, with best implementations. Lang Graf. Here, a central StateGraph Defines the various nodes (which can be agents, tools, or logic) and the conditional edges (transitions) between them. A graph manages a persistent state of objects that flow through the system.
The cornerstone of strength in this style is the outpost. Lang Graf A state object is automatically persisted after each node is executed. If the workflow crashes or is intentionally stopped, it can be restarted from the very last completed node without repeating the task or losing context. It also enables the human-inside loop pattern, where the human can approve, modify or redirect the workflow at specific points.
Manager-Controller Pattern with Centralized State Graph for Persistence and Checkpointing | Photo by author
// Implementation Focus (LangGraph)
Design your state schema carefully, as it is the only source of truth for the workflow. use Lang GrafKey has built-in persistence and disruptive capabilities to create traceable, restartable systems that are reliable enough for production.
# Applying an evaluator-criterion feedback loop
Quality assurance can be hard-coded into the system through the reviewer-criter (or generator-criter) pattern. This is often a special implementation of the loop pattern. An agent (generator) produces an output, which is then evaluated against specified criteria (accuracy, security, style) by a separate, independent agent (criter or evaluator).
This pattern is very important for developing high-level content such as code or legal text. The critic provides a layer of objective, external validation, dramatically reducing the flow of illusions and specifications.
Evaluator Critic Feedback Loop | Photo by author
The critic must be truly independent. It should also use a different system prompt, and possibly a different major language model, to avoid sharing generator assumptions or reasoning blind spots. Always implement a maximum iteration limit to prevent endless evaluation loops.
# Summary of strong design patterns
These patterns are not necessarily separate. Some very robust production systems combine them. You might have a manager-controller graph (Pattern 4) that lays out a sequential workflow (Pattern 2), where one step involves parallel collection of information (Pattern 3), and the last step involves a reviewer-review loop (Pattern 5) to ensure quality.
The path to robustness begins with the recognition that failures are inevitable in agentic systems. Adopting these structural patterns, especially those enabled by state, watchdog orchestration. Lang Grafyou move from building delicate chains of signals to engineering flexible systems that can handle uncertainty, recover from mistakes, and provide the transparency needed for continuous improvement and user trust.
I hope this detailed guide provides a solid foundation for designing your robust agent system.
Shatu Olomide A software engineer and technical writer with a knack for simplifying complex concepts and a keen eye for detail, passionate about leveraging modern technology to craft compelling narratives. You can also search on Shittu. Twitter.