Agentic AI Architecture is a system design enabling AI, typically an LLM, to autonomously plan, act, observe, and reflect to achieve complex goals.

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Context

Quick orientation

What is it?An AI system design for autonomous goal achievement.

Why does it exist?To overcome LLM limitations in complex, multi-step tasks.

Where is it used?Autonomous agents, software development, advanced assistants.

What came before it?Single-turn LLM prompts, basic prompt chaining.

What does it depend on?Large Language Models (LLMs), tool integration, memory systems.

Prerequisites

Foundational knowledge required for understanding agentic AI.

Core Mechanism

The fundamental design and operation of agentic AI systems.

The One Big Idea

An agentic AI extends an LLM with a planning loop and external tools, transforming it from a text generator into an autonomous problem-solver.

The Agentic Loop

This iterative cycle allows the AI to continuously make progress towards a goal.

Plan

Break down the main goal into smaller, executable steps.

Act

Execute a planned step, often by calling a tool or generating code.

Observe

Receive feedback or results from the executed action.

Reflect

Evaluate the observation against the plan and adjust strategy if needed.

Key Components

These include a Planner (for task decomposition), Memory (for retaining context), Tools (for external interaction), and a Critic (for self-correction).

The One Big Idea

These components augment an LLM, enabling it to break down complex goals, retain context, interact with the environment, and self-correct, moving beyond single-turn responses to achieve sustained, autonomous problem-solving.

Core Functions of Each Component

Each component serves a distinct, vital role in allowing an agentic AI to operate autonomously and effectively towards its objectives.

Planner

Decomposes a high-level goal into smaller, executable steps. It strategizes the sequence of actions needed to achieve the overall objective.

The One Big Idea

The Planner breaks down complex, ambiguous goals into a sequence of small, concrete, and achievable steps. This overcomes an LLM's limited 'look-ahead' capability, allowing it to tackle multi-step problems reliably.

How a Planner Works

The planner functions as the agent's strategic brain, guiding it from a high-level objective to actionable instructions.

Understand Goal & State

Receives the user's high-level goal and current environmental observations.

Decompose Goal

Breaks the high-level goal into smaller, manageable sub-goals or tasks.

Select Tools/Actions

Identifies appropriate tools or internal actions needed for each sub-goal.

Sequence Steps

Orders the sub-goals and actions into a logical, executable plan.

Generate Detailed Instruction

Creates a specific instruction for the next immediate action to take.

Types of Planners

Planners vary in their complexity and how they approach goal decomposition.

Example: Booking a Trip

Goal: 'Book a trip to Paris for next month.' Planner decomposes into 'Find flights', 'Find hotels', 'Book activities', 'Confirm dates'.

Not a Separate Pre-programmed Module

A common misconception is that the Planner is always a separate, hard-coded algorithm. Often, the LLM itself performs the planning by prompting itself to 'think step-by-step'.

Planning Depth vs. Adaptability

Deep, detailed plans can be robust but rigid. Shorter, iterative plans are more adaptive but might miss long-term consequences.

Memory

Stores past interactions, observations, and learned knowledge. It provides context for current actions and enables long-term learning and recall.

The One Big Idea

Memory is vital because it allows AI agents to transcend the limited context window of LLMs, maintaining persistent state, learning from past interactions, and accessing relevant long-term knowledge to achieve complex, multi-step goals over time.

How it works

Agentic AI memory typically combines different storage and retrieval mechanisms to provide relevant context for decision-making.

Memory vs. LLM Context Window

AI agent memory is distinct from an LLM's fixed context window.

Dimension	Agent Memory	LLM Context Window
Persistence	Persistent across sessions	Transient; forgotten after each prompt
Capacity	Virtually unlimited	Fixed token limit
Content	Structured knowledge, past actions, beliefs	Raw input/output for current interaction
Purpose	Statefulness, learning, long-term context	Immediate conversational turn context

Key Types of Memory

Memory in agentic AI can be categorized by the type of information stored.

Not a Single Brain

A common misconception is that AI memory is a single, unified 'brain' that inherently understands or feels. It is a structured system for storing and retrieving data.

Remembering User Preferences

An agent assisting a user remembers their preferred language or formatting from a past interaction days ago, applying it automatically to a new task.

Capacity vs. Performance

Storing more memory allows for richer context and broader learning, but increases the computational cost and time required for memory retrieval and maintenance.

Tools

External functions or APIs that the AI can use to interact with the real world or specific systems (e.g., search engines, code interpreters).

The One Big Idea

Tools empower Agentic AI by breaking its knowledge isolation. They extend the AI's capabilities beyond its static training data, enabling real-time interaction with external systems, dynamic information retrieval, and complex computations to achieve diverse goals.

How an AI uses Tools

The AI follows a structured process to identify, select, execute, and interpret results from external tools to achieve its objectives.

Finding current weather

An AI needs current weather for London. It identifies a 'get_weather(location)' tool. It calls this tool with 'London', and the tool returns '15°C, cloudy', which the AI then uses.

Types of Tools

Tools can be categorized by the primary function they perform for the AI agent.

Tools vs. Internal Knowledge

Tools and an AI's internal knowledge serve distinct purposes, complementing each other.

Dimension	Tools	Internal Knowledge/Reasoning
Nature	External, dynamic, specialized	Internal, static (trained), general
Update Cycle	Continuously updated by external systems	Requires model retraining or fine-tuning
Capability	Real-world interaction, specific functions	General understanding, inference, creativity

Misconception: Tools equal intelligence

A common mistake is thinking tools inherently make an AI 'smarter.' Tools provide capabilities; the AI's intelligence lies in its ability to reason when and how to effectively use them.

Risks of Poor Tool Design

Inaccurate or ambiguous tool descriptions can lead to the AI hallucinating arguments, making incorrect calls, or misinterpreting results, hindering effective agent behavior.

Critic

Evaluates the outcome of actions against the planned steps or overall goal. It identifies errors or suboptimal performance and suggests corrections.

The One Big Idea

The Critic is the agent's self-correction mechanism. It ensures the AI learns and adapts by evaluating actual performance against intentions, preventing repetitive errors and guiding the agent towards robust goal achievement.

How the Critic Works

The Critic follows a sequential process to evaluate actions and provide feedback, closing the loop in agentic AI architectures.

Receive Context

Receives action outcome, current state, original plan, and goal from the environment or other components.

Evaluate Performance

Compares the observed outcome against the expected plan steps or the ultimate goal.

Identify Discrepancies

Detects errors, suboptimal results, deviations from the plan, or missed opportunities.

Formulate Feedback

Generates insights explaining what went wrong or right, and suggests improvements.

Communicate Feedback

Sends evaluation report to the Planner or Actor for plan revision or action adjustment.

Risk: Flawed Feedback Loop

If the Critic's evaluation logic is poor or based on inaccurate information, it may provide unhelpful or misleading feedback, leading the agent to repeat mistakes or enter unproductive cycles.

Flight Booking Example

An AI agent booked a flight. The Critic verifies if the booked dates, destination, and price match the user's initial request. If the flight is too expensive, it flags this for re-planning.

Critic vs. Planner vs. Actor

The Critic has a distinct role from the Planner (who strategizes) and the Actor (who executes actions).

Dimension	Critic	Planner	Actor
Primary Role	Evaluates outcomes and provides feedback.	Formulates strategies and plans.	Executes actions based on plan.
Input	Observations, plans, goals.	Goals, environment state, critic feedback.	Action plans from Planner.
Output	Evaluation report, suggested revisions.	Sequence of actions (plan).	Actions in the environment.
Focus	Past performance and learning.	Future strategy and goal-setting.	Present execution.

Components are Separate LLMs

A common misconception is that each component is a distinct LLM. Often, one LLM orchestrates all components, switching roles or using different prompts for each function.

Travel Planning Assistant

Planner breaks 'plan a trip' into 'find flights', 'book hotel'. Memory stores preferences. Tools search for flights/hotels. Critic reviews bookings, suggests adjustments.

Complexity vs. Robustness

More components or sophisticated interactions increase an agent's capability and robustness, but also significantly increase system complexity and computational overhead.

Why Iterative Refinement

An iterative loop allows agents to recover from errors, adapt to new information, and tackle problems too complex for a single pass.

Applications

Real-world scenarios where agentic AI architectures are employed.

Challenges & Trade-offs

Limitations, risks, and inherent tensions in designing agentic AI systems.

Contrast

How agentic AI differs from other approaches to using large language models.

Agentic vs. Direct Prompting

Agentic AI is distinct from simply sending a single prompt to an LLM.

Dimension	Agentic AI	Direct Prompting
Goal Complexity	Multi-step, complex	Single-step, simple
Execution	Iterative, autonomous	One-shot, manual
Error Handling	Self-corrects	Manual intervention
Autonomy	High	Low

Extensions

Advanced concepts and future directions in agentic AI.

Common Questions

Key questions to test your understanding of agentic AI architecture.

Learning Path

A suggested sequence to deepen your understanding of Agentic AI Architecture.

Relationships

How this topic connects to the broader landscape

Part of	Artificial Intelligence	Broader field of intelligent systems
Depends on	Large Language Models	Core reasoning and generation engine
Made of	Planning Module	Decomposes goals into actionable steps
Alternative	Direct Prompt Engineering	Simpler, single-turn LLM interaction
Used in	Software Development	Automating coding, testing, debugging
Limitation	Reliability	Can produce errors or unintended outcomes

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APEX

Agentic AI Architecture is a system design enabling AI, typically an LLM, to autonomously plan, act, observe, and reflect to achieve complex goals.

Made with Rinto — analyse your own content free

CTX

Context

Quick orientation

What is it?An AI system design for autonomous goal achievement.

Why does it exist?To overcome LLM limitations in complex, multi-step tasks.

Where is it used?Autonomous agents, software development, advanced assistants.

What came before it?Single-turn LLM prompts, basic prompt chaining.

What does it depend on?Large Language Models (LLMs), tool integration, memory systems.

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SECT

Prerequisites

Foundational knowledge required for understanding agentic AI.

SUP

LLMs & Prompt Engineering

Powerful LLMs serve as the agent's 'brain,' processing information, reasoning, and generating actions based on prompts.

SUP

Tool Use

The ability for an LLM to call external functions or APIs is crucial for interacting with the real world.

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SECT

Core Mechanism

The fundamental design and operation of agentic AI systems.

INSG

The One Big Idea

An agentic AI extends an LLM with a planning loop and external tools, transforming it from a text generator into an autonomous problem-solver.

FLOW

The Agentic Loop

This iterative cycle allows the AI to continuously make progress towards a goal.

Plan

Break down the main goal into smaller, executable steps.

Act

Execute a planned step, often by calling a tool or generating code.

Observe

Receive feedback or results from the executed action.

Reflect

Evaluate the observation against the plan and adjust strategy if needed.

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SUP

Key Components

These include a Planner (for task decomposition), Memory (for retaining context), Tools (for external interaction), and a Critic (for self-correction).

INSG

The One Big Idea

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SUP

Core Functions of Each Component

Each component serves a distinct, vital role in allowing an agentic AI to operate autonomously and effectively towards its objectives.

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SUP

Planner

Decomposes a high-level goal into smaller, executable steps. It strategizes the sequence of actions needed to achieve the overall objective.

INSG

The One Big Idea

FLOW

How a Planner Works

The planner functions as the agent's strategic brain, guiding it from a high-level objective to actionable instructions.

Understand Goal & State

Receives the user's high-level goal and current environmental observations.

Decompose Goal

Breaks the high-level goal into smaller, manageable sub-goals or tasks.

Select Tools/Actions

Identifies appropriate tools or internal actions needed for each sub-goal.

Sequence Steps

Orders the sub-goals and actions into a logical, executable plan.

Generate Detailed Instruction

Creates a specific instruction for the next immediate action to take.

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SUP

Types of Planners

Planners vary in their complexity and how they approach goal decomposition.

SUP

Single-Pass Planner

Generates a full plan once upfront; less adaptive if environment changes during execution.

SUP

Iterative Planner

Plans one or few steps, executes, observes, then re-plans based on new information.

SUP

Hierarchical Planner

Decomposes goals into sub-goals across multiple abstraction layers, like a tree.

EXMP

Example: Booking a Trip

Goal: 'Book a trip to Paris for next month.' Planner decomposes into 'Find flights', 'Find hotels', 'Book activities', 'Confirm dates'.

OPP

Not a Separate Pre-programmed Module

A common misconception is that the Planner is always a separate, hard-coded algorithm. Often, the LLM itself performs the planning by prompting itself to 'think step-by-step'.

TRDF

Planning Depth vs. Adaptability

Deep, detailed plans can be robust but rigid. Shorter, iterative plans are more adaptive but might miss long-term consequences.

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SUP

Memory

Stores past interactions, observations, and learned knowledge. It provides context for current actions and enables long-term learning and recall.

INSG

The One Big Idea

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SUP

How it works

Agentic AI memory typically combines different storage and retrieval mechanisms to provide relevant context for decision-making.

SUP

Short-Term (Context Buffer)

Holds recent interactions, observations, and intermediate thoughts, providing immediate context for the current task. It's usually small and transient.

SUP

Long-Term (Knowledge Base)

Stores past experiences, learned facts, and persistent data over extended periods. Often implemented using vector databases for efficient semantic search.

SUP

Retrieval Mechanism

Queries long-term memory to fetch relevant information based on the agent's current goal, query, or observation, injecting it into the context buffer.

CMPR

Memory vs. LLM Context Window

AI agent memory is distinct from an LLM's fixed context window.

Dimension	Agent Memory	LLM Context Window
Persistence	Persistent across sessions	Transient; forgotten after each prompt
Capacity	Virtually unlimited	Fixed token limit
Content	Structured knowledge, past actions, beliefs	Raw input/output for current interaction
Purpose	Statefulness, learning, long-term context	Immediate conversational turn context

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SUP

Key Types of Memory

Memory in agentic AI can be categorized by the type of information stored.

SUP

Episodic Memory

Records specific events, actions, and observations in chronological order, like a diary of the agent's experience.

SUP

Semantic Memory

Stores general facts, concepts, and learned rules, often extracted or synthesized from episodic experiences.

OPP

Not a Single Brain

A common misconception is that AI memory is a single, unified 'brain' that inherently understands or feels. It is a structured system for storing and retrieving data.

EXMP

Remembering User Preferences

An agent assisting a user remembers their preferred language or formatting from a past interaction days ago, applying it automatically to a new task.

TRDF

Capacity vs. Performance

Storing more memory allows for richer context and broader learning, but increases the computational cost and time required for memory retrieval and maintenance.

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SUP

Tools

External functions or APIs that the AI can use to interact with the real world or specific systems (e.g., search engines, code interpreters).

INSG

The One Big Idea

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SUP

How an AI uses Tools

The AI follows a structured process to identify, select, execute, and interpret results from external tools to achieve its objectives.

SUP

Tool Description

The AI receives a description (e.g., OpenAPI spec) of available tools, their functions, and required arguments.

SUP

Tool Selection & Argumentation

Based on its goal and current state, the AI decides which tool is best. It then formulates the correct arguments to pass.

SUP

Tool Execution

The AI generates a function call (e.g., JSON) which is executed by a runtime environment, triggering the external tool.

SUP

Result Interpretation

The tool's output is returned to the AI, which interprets the result and integrates it into its ongoing reasoning process.

EXMP

Finding current weather

An AI needs current weather for London. It identifies a 'get_weather(location)' tool. It calls this tool with 'London', and the tool returns '15°C, cloudy', which the AI then uses.

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SUP

Types of Tools

Tools can be categorized by the primary function they perform for the AI agent.

SUP

Information Retrieval

Accessing up-to-date data (e.g., search engines, databases, real-time APIs).

SUP

Computation & Logic

Performing calculations or executing code (e.g., code interpreters, calculators, symbolic solvers).

SUP

External Action

Interacting with external systems (e.g., sending emails, controlling smart devices, API calls to web services).

CMPR

Tools vs. Internal Knowledge

Tools and an AI's internal knowledge serve distinct purposes, complementing each other.

Dimension	Tools	Internal Knowledge/Reasoning
Nature	External, dynamic, specialized	Internal, static (trained), general
Update Cycle	Continuously updated by external systems	Requires model retraining or fine-tuning
Capability	Real-world interaction, specific functions	General understanding, inference, creativity

OPP

Misconception: Tools equal intelligence

A common mistake is thinking tools inherently make an AI 'smarter.' Tools provide capabilities; the AI's intelligence lies in its ability to reason when and how to effectively use them.

RISK

Risks of Poor Tool Design

Inaccurate or ambiguous tool descriptions can lead to the AI hallucinating arguments, making incorrect calls, or misinterpreting results, hindering effective agent behavior.

SUP

Critic

Evaluates the outcome of actions against the planned steps or overall goal. It identifies errors or suboptimal performance and suggests corrections.

INSG

The One Big Idea

FLOW

How the Critic Works

The Critic follows a sequential process to evaluate actions and provide feedback, closing the loop in agentic AI architectures.

Receive Context

Receives action outcome, current state, original plan, and goal from the environment or other components.

Evaluate Performance

Compares the observed outcome against the expected plan steps or the ultimate goal.

Identify Discrepancies

Detects errors, suboptimal results, deviations from the plan, or missed opportunities.

Formulate Feedback

Generates insights explaining what went wrong or right, and suggests improvements.

Communicate Feedback

Sends evaluation report to the Planner or Actor for plan revision or action adjustment.

RISK

Risk: Flawed Feedback Loop

If the Critic's evaluation logic is poor or based on inaccurate information, it may provide unhelpful or misleading feedback, leading the agent to repeat mistakes or enter unproductive cycles.

EXMP

Flight Booking Example

An AI agent booked a flight. The Critic verifies if the booked dates, destination, and price match the user's initial request. If the flight is too expensive, it flags this for re-planning.

CMPR

Critic vs. Planner vs. Actor

The Critic has a distinct role from the Planner (who strategizes) and the Actor (who executes actions).

Dimension	Critic	Planner	Actor
Primary Role	Evaluates outcomes and provides feedback.	Formulates strategies and plans.	Executes actions based on plan.
Input	Observations, plans, goals.	Goals, environment state, critic feedback.	Action plans from Planner.
Output	Evaluation report, suggested revisions.	Sequence of actions (plan).	Actions in the environment.
Focus	Past performance and learning.	Future strategy and goal-setting.	Present execution.

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OPP

Components are Separate LLMs

A common misconception is that each component is a distinct LLM. Often, one LLM orchestrates all components, switching roles or using different prompts for each function.

OPP

Why people get this wrong

People often imagine separate 'brains' or distinct software services, each with its own LLM instance, similar to microservices or human team roles.

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SUP

The correct picture

In many agentic architectures, a single LLM orchestrates all components by switching roles and using different prompts and tools for each function.

SUP

Role Switching

The LLM receives different instructions (prompts) defining its current task, making it act as a planner, then an executor, then a reflector, etc.

SUP

Prompt Engineering

Carefully crafted prompts guide the LLM's behavior and access to external tools, making it functionally specialized for a specific component's step.

EXMP

Contrast

Misconception: A 'Planning LLM' sends tasks to an 'Execution LLM'. Correct: A single LLM first gets a 'Plan' prompt, then a 'Execute' prompt.

INSG

How to remember the correction

Think of a single versatile actor playing multiple roles in a play, changing costumes and scripts for each character. The actor is the LLM, costumes/scripts are the prompts/tools.

SUP

Test yourself

If an agentic AI has a 'Planner' and an 'Executor' component, does this mean two separate large language models are running simultaneously?

EXMP

Travel Planning Assistant

Planner breaks 'plan a trip' into 'find flights', 'book hotel'. Memory stores preferences. Tools search for flights/hotels. Critic reviews bookings, suggests adjustments.

TRDF

Complexity vs. Robustness

More components or sophisticated interactions increase an agent's capability and robustness, but also significantly increase system complexity and computational overhead.

INSG

Why Iterative Refinement

An iterative loop allows agents to recover from errors, adapt to new information, and tackle problems too complex for a single pass.

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SECT

Applications

Real-world scenarios where agentic AI architectures are employed.

EXMP

Complex Automation

Automating multi-step workflows like data analysis, report generation, or customer support interactions without constant human oversight.

EXMP

Interactive Assistants

Building advanced assistants that can understand context, use tools, and maintain coherent interactions over long sessions.

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SECT

Challenges & Trade-offs

Limitations, risks, and inherent tensions in designing agentic AI systems.

RISK

Reliability Issues

Agents can still 'hallucinate' or make logical errors, leading to incorrect plans or actions that are hard to debug.

TRDF

Autonomy vs. Safety

Increasing agent autonomy brings greater capability but also higher risk if the agent's goals diverge from human intent.

RISK

Resource Intensity

The iterative nature and frequent LLM calls can lead to high computational costs and slow execution times.

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SECT

Contrast

How agentic AI differs from other approaches to using large language models.

CMPR

Agentic vs. Direct Prompting

Agentic AI is distinct from simply sending a single prompt to an LLM.

Dimension	Agentic AI	Direct Prompting
Goal Complexity	Multi-step, complex	Single-step, simple
Execution	Iterative, autonomous	One-shot, manual
Error Handling	Self-corrects	Manual intervention
Autonomy	High	Low

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SECT

Extensions

Advanced concepts and future directions in agentic AI.

EXTN

Multi-Agent Systems

Orchestrating multiple specialized agents to collaborate on a larger problem, mirroring human team dynamics.

EXTN

Human-in-the-Loop AI

Designing agents that know when to ask for human input or intervention, combining AI efficiency with human judgment.

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SECT

Common Questions

Key questions to test your understanding of agentic AI architecture.

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QUIZ

What is the primary advantage of agentic architecture over simple LLM calls?

It enables autonomous, multi-step problem-solving beyond single-turn interactions.

SUP

Multi-step Reasoning

Agents can break down complex tasks, execute sub-tasks, and synthesize results, overcoming LLM limitations for chained reasoning.

SUP

Self-Correction

The observe-reflect loop allows agents to detect and recover from errors, improving reliability over time.

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QUIZ

How does 'memory' function in an agentic system?

Memory stores past interactions and observations, providing crucial context for future decisions and planning.

SUP

Short-term Context

The LLM's context window holds recent turns and current task details for immediate relevance.

SUP

Long-term Storage

External databases (vector stores) retrieve relevant historical information beyond the LLM's immediate context.

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QUIZ

Name one key challenge in deploying agentic AI systems.

Ensuring reliability and preventing unintended side effects due to autonomous decision-making.

SUP

Hallucinations and Errors

LLMs can generate plausible but incorrect information or plans, leading to flawed execution.

SUP

Cost and Latency

Frequent LLM calls and iterative loops can quickly become expensive and slow.

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SECT

Learning Path

A suggested sequence to deepen your understanding of Agentic AI Architecture.

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CHKL

Your first steps to understanding Agentic AI Architecture

Follow these steps to build a solid foundation.

1Grasp LLM fundamentals→2Learn prompt engineering→3Study tool use concepts→4Visualize the agentic loop→5Experiment with an agent framework

STEP

Grasp LLM fundamentals

Understand how Large Language Models process information and generate text.

STEP

Learn prompt engineering

Explore techniques for effectively guiding LLMs to desired outputs.

STEP

Study tool use concepts

Understand how LLMs can be integrated with external functions and APIs.

STEP

Visualize the agentic loop

Internalize the Plan-Act-Observe-Reflect cycle as the core mechanism.

STEP

Experiment with an agent framework

Build a basic agent using tools like LangChain or CrewAI to see it in action.

REL

Relationships

How this topic connects to the broader landscape

Part of	Artificial Intelligence	Broader field of intelligent systems
Depends on	Large Language Models	Core reasoning and generation engine
Made of	Planning Module	Decomposes goals into actionable steps
Alternative	Direct Prompt Engineering	Simpler, single-turn LLM interaction
Used in	Software Development	Automating coding, testing, debugging
Limitation	Reliability	Can produce errors or unintended outcomes