Automating the Critic: Building Self-Refining AI Agents with LangGraph

In the rapid evolution of Generative AI, we have quickly moved past the initial awe of "Look what the chatbot can do" to the more pragmatic reality of "How do we make this reliable enough for production?"

For IT professionals and CTOs, the limitations of Large Language Models (LLMs) are well-documented. When tasked with complex reasoning or code generation in a "zero-shot" manner (a single prompt and response), LLMs frequently hallucinate, omit critical requirements, or produce code that looks correct but fails to compile. The traditional solution has been Human-in-the-Loop (HITL), where a human verifies the AI's output. While effective, this creates a bottleneck that negates the speed advantages of automation.

The next frontier in AI orchestration is the AI-in-the-Loop approach—specifically, building self-refining agents. By automating the "critic," we can create recursive feedback loops where one agent generates content and another critiques and improves it iteratively. In this post, we will explore how to implement this architecture using LangGraph, transforming your AI from a simple text generator into a robust, self-correcting system.

The Shift from Linear Chains to Cyclic Graphs

Most early LLM applications were built on the concept of a "Chain" (hence, LangChain). A chain is a Directed Acyclic Graph (DAG)—a linear sequence of steps where data flows in one direction: Input -> Step A -> Step B -> Output. This works perfectly for simple retrieval-augmented generation (RAG) or summarization tasks.

However, complex problem-solving is rarely linear. Consider how a senior developer writes code. They don't just type a script from start to finish and push it to production. They write a draft, run it, see an error, debug, rewrite, and optimize. This is a cycle, not a straight line.

To achieve human-like reliability, our AI architectures must mimic human-like iteration.

This is where LangGraph enters the picture. Unlike standard chains, LangGraph allows us to define cycles in our agent workflows. It treats the agent's workflow as a state machine. This capability is critical for building self-refining agents because it allows the system to loop back to a previous step—regenerating or refining an answer based on feedback—until a specific quality threshold is met.

The Architecture of an Actor-Critic System

The most effective pattern for self-refining AI is the Actor-Critic architecture. This design splits the workload into two distinct roles (or separate LLM calls):

• The Actor (Generator): This agent is responsible for the initial creation. Whether it is writing Python code, drafting a legal contract, or architecting a cloud solution, the Actor focuses on executing the prompt.
• The Critic (Reflector): This agent does not generate new content. Instead, it analyzes the Actor's output against a set of strict criteria. It looks for logic errors, security vulnerabilities, or deviations from the user's instructions.

In a standard zero-shot prompt, the LLM has to be both the creative writer and the harsh editor simultaneously—a cognitive load that often leads to mediocrity. By decoupling these roles, we can use different prompting strategies, or even different models (e.g., a fast model for generation and a more reasoning-heavy model like GPT-4o or Claude 3.5 Sonnet for critiquing) to optimize performance.

The workflow operates as follows:

1. The Actor generates a draft.
2. The Critic reviews the draft and provides structured feedback (e.g., "Line 40 is vulnerable to SQL injection").
3. The system checks a conditional edge: Is the critique clear?
4. If yes, the feedback is passed back to the Actor, which refines the draft.
5. This loop continues until the Critic is satisfied or a maximum iteration count is reached.

Implementing Recursive Loops with LangGraph

Let's look at how this translates into code. LangGraph manages the flow using a shared State object that is passed between nodes. This state tracks the conversation history, the current draft, the critique, and the iteration count.

Here is a conceptual example of how you might define this graph in Python:

from typing import TypedDict, List
from langgraph.graph import StateGraph, END

# Define the State
class AgentState(TypedDict):
    draft: str
    critique: str
    iteration_count: int

# Define the Workflow
workflow = StateGraph(AgentState)

# Add Nodes
workflow.add_node("generator", generate_draft_agent)
workflow.add_node("critic", critique_draft_agent)

# Define Edges
workflow.set_entry_point("generator")
workflow.add_edge("generator", "critic")

# Conditional Logic
def decide_next_step(state):
    if state['critique'] == "APPROVED" or state['iteration_count'] > 3:
        return END
    return "generator"

workflow.add_conditional_edges(
    "critic",
    decide_next_step
)

app = workflow.compile()

In this architecture, the decide_next_step function is the router. It determines if the cycle should continue. This recursive approach drastically improves output quality because the "Generator" isn't guessing; it is responding to specific, actionable feedback from the "Critic."

For developers and CTOs, the trade-off here is clear: Latency vs. Accuracy. A recursive system will take longer and consume more tokens than a single-shot system. However, for high-stakes tasks—such as automated infrastructure deployment or financial analysis—the cost of an error far outweighs the cost of a few extra API calls.

Strategic Implications for Enterprise AI

Implementing self-refining agents moves your organization up the AI Maturity Curve. While competitors may still be struggling with prompt engineering to get a decent first draft, a recursive system automates the quality assurance process.

Key Use Cases:

• Automated Code Review & Refactoring: Agents that don't just write code, but critique it for PEP8 compliance or security flaws before showing it to a human.
• Data Analysis Reporting: An agent generates an insight report, while the critic checks the numbers against the raw dataset to prevent hallucinations.
• Content Governance: Ensuring marketing copy aligns with brand voice guidelines before it ever reaches a CMS.

At Nohatek, we are seeing a shift in client demands. The request is no longer just "build a chatbot." It is "build an agent that can perform a task and guarantee it followed the rules." LangGraph and recursive feedback loops are the architectural patterns that make this guarantee possible.

The era of accepting the first draft from an LLM is ending. By treating AI agents not as magic boxes but as components in a stateful, iterative system, we can achieve levels of reliability that were previously impossible. Automating the critic using tools like LangGraph allows us to build systems that self-correct, learn from immediate feedback, and deliver production-grade results.

Building these systems requires a deep understanding of state management, graph theory, and prompt engineering. If your organization is looking to move beyond basic chatbots and deploy robust, self-refining AI agents, Nohatek is here to guide the way. Let's build the future of intelligent automation together.