How Does Agentic AI Work?
Architecture, Components, and Mechanisms Explained
Key Takeaways
- Agentic AI operates through a Perceive → Reason → Plan → Act → Learn continuous loop that enables autonomous task execution
- The architecture consists of six core modules: Perception, Reasoning Engine, Planning, Memory, Action/Tools, and Orchestration
- Memory systems (short-term and long-term) enable context retention across sessions and continuous learning from outcomes
- Popular frameworks like LangGraph, CrewAI, and AutoGen implement these patterns with different architectural approaches
AGENTIC AI ARCHITECTURE ADOPTION 2026
Sources: Precedence Research, Axis Intelligence
Overview: The Agentic AI System
An agentic AI architecture is a system design that transforms passive large language models (LLMs) into autonomous, goal-oriented agents capable of reasoning, planning, and taking action with minimal human intervention. If you're new to this concept, start with our guide on what agentic AI is. Unlike traditional AI that provides single-turn responses, an agentic architecture orchestrates a continuous feedback loop that allows the AI to adapt and execute complex, multi-step tasks.
The Fundamental Principle
"Agentic systems operate across three logical layers: Tool, Reasoning, and Action. Each layer has a specific role in enabling the agent to retrieve, process, and act on information effectively."
According to AWS Prescriptive Guidance, modern agentic AI extends LLMs with an orchestration layer that manages control flow, tool invocation, and state across multi-step execution. This transforms a stateless model into a stateful, goal-pursuing system.
The Core Loop: Perceive-Reason-Plan-Act-Learn
At the heart of every agentic AI system is a cognitive loop known as the Perceive–Reason–Plan–Act–Learn cycle. This loop enables an intelligent agent to understand their environment, apply logic to make decisions, act on those decisions, and learn from the outcomes to improve future performance.
Agentic AI Architecture Diagram
Source: Architecture pattern based on Amplework - Agentic AI Loops and Exabeam Architecture Guide
The Five Stages Explained
1. Perceive
The agent gathers data from its environment using sensors, APIs, or other input sources. This includes natural language processing (NLP) for text, computer vision for images, and API calls for structured data. The perception process performs feature extraction, object recognition, and semantic interpretation to create a meaningful model of the current situation.
2. Reason
The reasoning engine—typically powered by an LLM—processes perceived information to evaluate potential actions. This involves logical analysis, probabilistic inference, and drawing conclusions from the knowledge base. The LLM applies chain-of-thought reasoning to understand context and formulate potential solutions.
3. Plan
The planning module breaks high-level business objectives into small, executable steps. It designs dependencies, sequences tasks, and determines what needs to happen in what order. Plans can be hierarchical, with a "planner agent" orchestrating tasks across multiple "worker agents."
4. Act
The action module executes the plan by interfacing with either digital or physical environments. This includes calling external tools like APIs, writing code, sending emails, updating databases, or controlling devices. The execution module tracks task status, checks for failures, and revises plans when conditions change.
5. Learn
The feedback loop allows the agent to evaluate outcomes and learn from successes and failures. Using reinforcement signals, self-reflective evaluation, or human feedback, the system refines its internal models and strategies. This enables continuous improvement over time.
Architecture Components Deep Dive
According to Exabeam's architectural analysis, agentic AI systems are built on several core modules that work together to enable autonomous operation:
Perception Module
The perception module is the agent's sensory system that gathers and interprets data from the environment. It uses technologies like natural language processing (NLP), computer vision, and APIs to process various data types.
Key Functions
- • Feature extraction from raw inputs
- • Object/event recognition
- • Semantic interpretation
- • Context modeling
Data Sources
- • Natural language input
- • Structured databases
- • External APIs
- • Sensor/IoT data
Reasoning Engine (LLM Brain)
The reasoning engine is the "brain" of the agent, typically powered by a large language model (LLM). It processes perceived information and knowledge to make intelligent decisions and inferences, drawing logical conclusions from facts and patterns.
How it works: If a rule states "IF A AND B THEN C," and the agent perceives A and B, it can infer C. This extends to complex chains of reasoning across multiple domains and knowledge sources.
Planning Module
The planning module breaks high-level objectives into executable steps. According to Kore.ai, the planner designs dependencies, sequences tasks, and ensures actions are explainable and aligned with business logic.
| Planning Approach | Description | Best For |
|---|---|---|
| Fixed Planning | Plan created upfront before execution | Predictable tasks |
| Adaptive Planning | Plan evolves with environmental feedback | Dynamic environments |
| Hierarchical | Nested sub-plans with delegation | Complex workflows |
Action Module (Tool Integration)
AI agents become truly actionable only when they can interact with enterprise systems. APIs let agents trigger transactions, fetch data, update workflows, and connect with CRMs, ERPs, HR systems, or cloud platforms.
Orchestration Layer
The orchestration layer coordinates communication between all modules, managing overall control flow, handling iterations, errors, and new information. According to Communications of the ACM, without orchestration, agents remain siloed—unable to share memory, coordinate efforts, or adapt dynamically.
A strong orchestration platform enables: Memory and state management for long-running workflows, task decomposition and parallelization, fine-grained access control, and dynamic model selection based on context.
Memory Systems: Short-Term and Long-Term
Memory is a crucial component for maintaining context across interactions. According to IBM's analysis of AI agent memory, agentic AI introduces layered memory systems that persist across time, contexts, and agents—enabling continuity, learning, and adaptation.
Short-Term Memory
Provides temporary storage for context and state during task execution. Allows the agent to maintain continuity across multiple steps without losing track of immediate objectives.
Key Functions:
- • Context retention within sessions
- • Conversation history maintenance
- • Task progress tracking
- • Intermediate result storage
Challenge: As interactions extend, transcript replay inflates cost and latency, making early mistakes harder to recover from.
Long-Term Memory
Stores historical data including previously executed actions, outcomes, and environmental observations. Enables agents to retain learned behaviors and apply insights across different contexts.
Key Functions:
- • Knowledge base storage
- • Vector store embeddings
- • Knowledge graph relationships
- • Cross-session learning
Technology: Often implemented using vector databases and knowledge graphs for efficient retrieval.
The Memory Scaling Challenge
According to AI News, as foundation models scale toward trillions of parameters and context windows reach millions of tokens, the computational cost of remembering history is rising faster than the ability to process it. Organizations face a bottleneck where the sheer volume of "long-term memory" (Key-Value cache) overwhelms existing hardware architectures.
Orchestration and Multi-Agent Coordination
In a multi-agent system (MAS) architecture, multiple independent agents—each powered by language models—collaborate to tackle complex tasks. Unlike single-agent systems where one agent handles everything, MAS leverages each agent's unique roles, personas, and tools to enhance efficiency and decision-making.
Multi-Agent Architecture Pattern
✓Advantages of Multi-Agent
- • Scalability: Add agents without significant redesign
- • Fault tolerance: If one fails, others continue
- • Specialization: Each agent optimized for its role
- • Parallelization: Multiple tasks simultaneously
⚠Considerations
- • Coordination overhead: Managing communication
- • Consistency: Shared state management
- • Debugging: Tracing across agents
- • Cost: Multiple LLM calls
Key Design Patterns: ReAct, Plan-Execute, and More
According to IBM's research on ReAct agents, several key design patterns have emerged for building effective agentic AI systems. These patterns define how agents reason, plan, and execute tasks.
ReAct (Reasoning + Acting)
First introduced by Yao et al. in 2023, ReAct combines chain-of-thought (CoT) reasoning with external tool use. The agent operates in a continuous Thought → Action → Observation loop for step-by-step problem-solving.
The ReAct Loop:
Strengths
- • Grounds reasoning in real-world feedback
- • Reduces hallucination risk
- • Improved interpretability
- • Incremental decision-making
Challenges
- • Non-deterministic outputs
- • Potential infinite loops
- • Higher API costs from iterations
Plan-and-Execute
Unlike ReAct's incremental approach, Plan-and-Execute creates a complete plan upfront before execution. According to By AI Team's analysis, this pattern is better suited for tasks requiring reliability and consistency.
| Aspect | ReAct | Plan-and-Execute |
|---|---|---|
| Planning | Incremental, step-by-step | Complete plan upfront |
| Adaptability | High—adjusts each step | Lower—replans if needed |
| Consistency | Variable | Higher—structured approach |
| Best For | Dynamic, exploratory tasks | Well-defined workflows |
Reflection Pattern
Agent evaluates its own outputs, identifies errors, and self-corrects. Improves quality through iterative refinement.
Tool Use Pattern
Agent determines which external tools to use and how to use them to accomplish tasks effectively.
Human-in-the-Loop
Agent pauses at critical decision points to get human approval before proceeding with high-stakes actions.
Multi-Agent Collaboration
Multiple specialized agents work together, with clear leadership and task division across the team.
Popular Frameworks Compared
Several frameworks have emerged to implement agentic AI architectures. According to DataCamp's comprehensive comparison, each framework takes a different architectural approach suited to different use cases. For an in-depth look at framework options, see our agentic AI frameworks guide.
| Framework | Architecture | Best For | Learning Curve |
|---|---|---|---|
LangGraph by LangChain | Graph-based workflows with nodes and edges | Complex decision pipelines, branching logic | Steeper |
CrewAI Role-based | Team metaphor with role-defined agents | Rapid prototyping, structured workflows | Beginner-friendly |
AutoGen by Microsoft | Conversational agents, dynamic role-playing | Enterprise deployment, human-in-the-loop | Moderate |
Semantic Kernel by Microsoft | Plugin-based with semantic functions | Enterprise integration, .NET/C# ecosystems | Moderate |
Sources: Latenode Comparison, Turing AI Agent Frameworks, Codecademy Guide
LangGraph
Uses nodes (functions), edges (execution flow), and stateful graphs (persistent data). Ideal for complex decision pipelines with conditional branching.
CrewAI
Introduces "Crews and Flows" design pattern. A Crew is a collection of role-defined agents plus tasks, with sequential or parallel execution.
AutoGen
Emphasizes natural language interactions and dynamic role-playing. v0.4 supports asynchronous architecture for scaling larger teams.
Architecture Types: Reactive, Deliberative, Cognitive
According to Vectorize.io's architectural analysis, three broad categories of agent architectures are commonly referenced: Reactive, Deliberative, and Cognitive. While these originated in classical AI theory, they map well onto modern LLM agent designs.
Reactive Architecture
Maps situations directly to actions without deeper reasoning or internal planning. Simple stimulus-response behavior based on predefined rules or learned mappings.
Deliberative Architecture
Maintains an internal model of the world and uses reasoning to plan actions. Considers goals, evaluates options, and formulates multi-step plans before acting.
Cognitive Architecture
The most advanced category, aiming to emulate human-like cognition. Combines perception, reasoning, memory, learning, and meta-cognition (reasoning about reasoning).
Modern Reality: Most production agentic AI systems are hybrids, combining reactive responses for simple queries with deliberative planning for complex tasks. Research from Nature Communications shows that brain-inspired modular architectures—where specialized LLM modules interact like cognitive systems—are showing promise for improved planning capabilities.
Implementation Considerations
Building production-ready agentic AI systems requires careful attention to several factors. Based on research from Akka's enterprise guide and Microsoft's Agent Framework, here are the key considerations:
Best Practices
- ✓Well-defined system promptsClear instructions and constraints for agent behavior
- ✓Clear task divisionDistinct roles and responsibilities for each agent
- ✓Dedicated reasoning phasesSeparate planning, execution, and evaluation stages
- ✓Human or agentic feedbackCheckpoints for validation and course correction
- ✓Intelligent message filteringReduce noise and focus on relevant context
Common Pitfalls
- ×Unbounded loopsAgents running indefinitely without termination conditions
- ×Context window overflowLosing important context as conversations grow
- ×Tool call failuresInadequate error handling for external services
- ×Cost explosionUnexpected API costs from iterative loops
- ×Hallucination propagationErrors compounding through multi-step workflows
Summary: How Agentic AI Systems Work
CORE ARCHITECTURE
Agentic AI systems transform LLMs into autonomous agents through the Perceive-Reason-Plan-Act-Learn loop, enabling continuous task execution with minimal human oversight.
KEY COMPONENTS
Perception Module, Reasoning Engine (LLM), Planning Module, Action/Tool Integration, Memory Systems (short and long-term), and Orchestration Layer.
DESIGN PATTERNS
ReAct (reasoning + acting), Plan-and-Execute, Reflection, Tool Use, and Multi-Agent Collaboration—each suited for different task types and requirements.
FRAMEWORKS
LangGraph (graph-based), CrewAI (role-based teams), AutoGen (conversational), and Semantic Kernel (enterprise) offer different architectural approaches.
Build With Agentic AI Architecture
At Planetary Labour, we're implementing cutting-edge agentic AI architectures to create autonomous digital workers. Our systems leverage the perceive-reason-act loop to handle complex tasks with human-like adaptability.
Explore Planetary Labour →Continue Learning
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