Learn: Agentic AI Architecture

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.

1

Plan

Break down the main goal into smaller, executable steps.

2

Act

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

3

Observe

Receive feedback or results from the executed action.

4

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.

1

Understand Goal & State

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

2

Decompose Goal

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

3

Select Tools/Actions

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

4

Sequence Steps

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

5

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.

1

Receive Context

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

2

Evaluate Performance

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

3

Identify Discrepancies

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

4

Formulate Feedback

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

5

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

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.

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

Context

Prerequisites

LLMs & Prompt Engineering

Tool Use

Core Mechanism

The One Big Idea

The Agentic Loop

Key Components

The One Big Idea

Core Functions of Each Component

Planner

The One Big Idea

How a Planner Works

Types of Planners

Single-Pass Planner

Iterative Planner

Hierarchical Planner

Example: Booking a Trip

Not a Separate Pre-programmed Module

Planning Depth vs. Adaptability

Memory

The One Big Idea

How it works

Short-Term (Context Buffer)

Long-Term (Knowledge Base)

Retrieval Mechanism

Memory vs. LLM Context Window

Key Types of Memory

Episodic Memory

Semantic Memory

Not a Single Brain

Remembering User Preferences

Capacity vs. Performance

Tools

The One Big Idea

How an AI uses Tools

Tool Description

Tool Selection & Argumentation

Tool Execution

Result Interpretation

Finding current weather

Types of Tools

Information Retrieval

Computation & Logic

External Action

Tools vs. Internal Knowledge

Misconception: Tools equal intelligence

Risks of Poor Tool Design

Critic

The One Big Idea

How the Critic Works

Risk: Flawed Feedback Loop

Flight Booking Example

Critic vs. Planner vs. Actor

Components are Separate LLMs

Why people get this wrong

The correct picture

Role Switching

Prompt Engineering

Contrast

How to remember the correction

Test yourself

Travel Planning Assistant

Complexity vs. Robustness

Why Iterative Refinement

Applications

Complex Automation

Interactive Assistants

Challenges & Trade-offs

Reliability Issues

Autonomy vs. Safety

Resource Intensity

Contrast

Agentic vs. Direct Prompting

Extensions

Multi-Agent Systems

Human-in-the-Loop AI

Common Questions

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