Exploring Different AI Agent Architectures: From Reactive to Cognitive

Agent architecture defines how AI agents organize components (sensors, reasoning, actuators) to perceive, decide, and act.

Choosing the right architecture impacts response speed, task complexity, learning ability, and resource consumption. Reactive agents are fast but lack planning; deliberative agents plan ahead at higher computational cost; hybrid agents combine both; neural‑symbolic agents merge neural perception with symbolic reasoning; cognitive agents model human‑like cognition.

Reactive Architecture (ReAct)

Basic pattern where a large language model performs a reasoning‑action loop: analyze the current state, decide an action, execute it, observe the result, and repeat.

from dotenv import load_dotenv
from openai import OpenAI
_ = load_dotenv()
client = OpenAI()

class Agent:
    def __init__(self, system=""):
        self.system = system
        self.messages = []
        if self.system:
            self.messages.append({"role":"system","content":system})
    def __call__(self, message):
        self.messages.append({"role":"user","content":message})
        result = self.execute()
        self.messages.append({"role":"assistant","content":result})
        return result
    def execute(self):
        completion = client.chat.completions.create(
            model="gpt-4o",
            temperature=0,
            messages=self.messages)
        return completion.choices[0].message.content

Example: an agent uses two tools—one for calculating and one for retrieving average dog weight—to answer a question about the combined weight of a Border Collie and a Scottish Terrier, demonstrating the Thought‑Action‑Pause‑Observation‑Answer cycle.

Deliberative Architecture

Model‑goal‑driven agents follow a Sense → Model → Plan → Act cycle, evaluating multiple possible actions before acting.

# Pseudocode for a deliberative agent with goal‑oriented planning
initialize_state()
while True:
    perceive_environment(state)
    options = generate_options(state)  # possible plans
    best_option = evaluate_options(options)  # select best plan
    commit_to_plan(best_option, state)
    execute_next_action(best_option)
    if goal_achieved(state):
        break

This approach is useful for tasks such as path planning, where several routes are generated and the shortest safe one is chosen.

Hybrid Architecture

Combines a reactive layer for urgent inputs with a deliberative layer for goal‑driven planning, allowing both layers to operate in parallel.

percept = sense_environment()
if is_urgent(percept):
    action = reactive_module(percept)  # quick reflex
else:
    update(world_model, percept)
    action = deliberative_planner(world_model, current_goal)
execute(action)

Neural‑Symbolic Architecture

Integrates neural networks for perception with symbolic modules for logical inference, enabling both pattern recognition and explainable reasoning.

percept = get_sensor_data()
nn_insights = neural_module.predict(percept)      # perception
sym_facts = symbolic_module.update(percept)      # translate to logical facts
sym_conclusions = symbolic_module.infer(sym_facts)  # reasoning
decision = policy_module.decide(nn_insights, sym_conclusions)
execute(decision)

Cognitive Architecture

Models human cognition with cycles of Perceive → Update Working Memory → Apply Production Rules → Act, supporting learning, planning, and multiple memory systems (declarative, procedural, episodic).

percept = perceive_environment()
update_working_memory(percept)
action = cognitive_reasoner.decide(working_memory)
execute(action)

Representative systems include SOAR (with working memory, production memory, chunking learning) and ACT‑R (modular visual, motor, and memory components), which combine symbolic reasoning with sub‑symbolic mechanisms.

Agent Design Patterns in LangGraph

LangGraph groups patterns into three categories:

Multi‑agent systems (networked agents, supervisory agents, hierarchical teams) for collaborative task decomposition.

Planning agents that generate sub‑tasks, delegate them to specialized agents, and aggregate results (e.g., ReWOO, LLMCompiler).

Reflective and critical agents that incorporate self‑inspection, tree‑search, or Monte‑Carlo techniques to improve outputs.

These templates provide reusable blueprints for building scalable, modular, and goal‑driven AI solutions.

Conclusion

The evolution from simple reactive agents to sophisticated cognitive systems shows that modular, transparent, and hybrid designs enable scalable, goal‑driven AI solutions. Applying the discussed architectures and LangGraph patterns is essential for constructing collaborative, reflective, and autonomous agents.