Agentic AI Patterns: The Autonomous Workforce

~20 min read5 quizzes

Agentic AI - Autonomous agents planning and executing complex workflows

The Reader's Dilemma

Dear Marilyn,Everyone is talking about "AI Agents" now. I understand chatbots and simple automation, but what makes an agent different? And how do I design systems that can actually leverage these autonomous AI workers without creating chaos?

Marilyn's Reply

The difference between a chatbot and an agent is like the difference between a calculator and a mathematician. A calculator performs operations you specify. A mathematician understands the problem, plans an approach, executes steps, and adjusts based on results.

Agentic AI represents a paradigm shift from reactive to proactive systems. Let's explore the patterns that make this possible.

The Spark: Understanding Agentic AI

What Makes an Agent "Agentic"?

An AI agent possesses four fundamental capabilities that distinguish it from traditional automation:

Capability	Description	Example
Perception	Ability to observe and interpret the environment	Reading emails, parsing documents, monitoring systems
Reasoning	Ability to analyze information and make decisions	Determining priority, identifying patterns, planning steps
Action	Ability to execute tasks and interact with systems	Calling APIs, writing code, sending messages
Learning	Ability to improve from experience and feedback	Refining approaches, remembering preferences, adapting strategies

Quick Check

What distinguishes an AI agent from a simple chatbot?

The ReAct Pattern: Reasoning + Acting

The ReAct (Reasoning and Acting) pattern is the foundation of most agentic systems. It interleaves thinking with doing:

# ReAct Loop Pseudocode
while task_not_complete:
    # THOUGHT: Reason about the current state
    thought = llm.think(f"""
        Task: {task}
        Observations: {observations}
        What should I do next and why?
    """)
    
    # ACTION: Choose and execute an action
    action = llm.decide_action(thought, available_tools)
    result = execute_action(action)
    
    # OBSERVE: Update understanding based on result
    observations.append(result)
    
    # REFLECT: Determine if task is complete
    if is_task_complete(observations):
        break

This pattern prevents the agent from blindly executing a predetermined plan. Instead, it continuously adapts based on what it observes.

Quick Check

In the ReAct pattern, what happens after an agent executes an action?

Tool Use: Extending Agent Capabilities

Agents become truly powerful when they can use tools. Tools are functions or APIs that extend what an agent can do:

Information Tools

Web search
Database queries
API calls
File reading

Action Tools

Code execution
Email sending
File writing
System commands

# Tool Definition Example
tools = [
    {
        "name": "search_web",
        "description": "Search the web for information",
        "parameters": {
            "query": "string - the search query"
        }
    },
    {
        "name": "execute_sql",
        "description": "Execute a SQL query on the database",
        "parameters": {
            "query": "string - the SQL query to execute"
        }
    },
    {
        "name": "send_email",
        "description": "Send an email to a recipient",
        "parameters": {
            "to": "string - recipient email",
            "subject": "string - email subject",
            "body": "string - email body"
        }
    }
]

Quick Check

Why are tools essential for agentic AI systems?

Multi-Agent Orchestration

Complex tasks often require multiple specialized agents working together. This is where orchestration patterns become crucial:

Supervisor Pattern

A "manager" agent delegates tasks to specialized worker agents and coordinates their outputs. Best for hierarchical workflows with clear task decomposition.

Swarm Pattern

Agents communicate peer-to-peer without central coordination. Best for emergent behavior and parallel exploration of solution spaces.

Pipeline Pattern

Each agent processes output from the previous agent in a chain. Best for sequential workflows where each step transforms the data.

Quick Check

Which orchestration pattern would be best for a code review system where code is analyzed, then tested, then documented?

Safety and Guardrails

Autonomous agents require careful safety considerations. Key guardrail patterns include:

Human-in-the-Loop: Require human approval for high-stakes actions
Sandboxing: Limit what tools and resources an agent can access
Rate Limiting: Prevent runaway loops or excessive resource consumption
Audit Logging: Record all agent decisions and actions for review
Kill Switches: Ability to immediately halt agent execution

Quick Check

What is the purpose of 'Human-in-the-Loop' in agentic AI systems?

The Reader's Dilemma

Marilyn's Reply

Agentic AI represents a paradigm shift from reactive to proactive systems. Let's explore the patterns that make this possible.

The Spark: Understanding Agentic AI

What Makes an Agent "Agentic"?

An AI agent possesses four fundamental capabilities that distinguish it from traditional automation:

Capability	Description	Example
Perception	Ability to observe and interpret the environment	Reading emails, parsing documents, monitoring systems
Reasoning	Ability to analyze information and make decisions	Determining priority, identifying patterns, planning steps
Action	Ability to execute tasks and interact with systems	Calling APIs, writing code, sending messages
Learning	Ability to improve from experience and feedback	Refining approaches, remembering preferences, adapting strategies

Quick Check

What distinguishes an AI agent from a simple chatbot?

The ReAct Pattern: Reasoning + Acting

The ReAct (Reasoning and Acting) pattern is the foundation of most agentic systems. It interleaves thinking with doing:

# ReAct Loop Pseudocode
while task_not_complete:
    # THOUGHT: Reason about the current state
    thought = llm.think(f"""
        Task: {task}
        Observations: {observations}
        What should I do next and why?
    """)
    
    # ACTION: Choose and execute an action
    action = llm.decide_action(thought, available_tools)
    result = execute_action(action)
    
    # OBSERVE: Update understanding based on result
    observations.append(result)
    
    # REFLECT: Determine if task is complete
    if is_task_complete(observations):
        break

This pattern prevents the agent from blindly executing a predetermined plan. Instead, it continuously adapts based on what it observes.

Quick Check

In the ReAct pattern, what happens after an agent executes an action?

Tool Use: Extending Agent Capabilities

Agents become truly powerful when they can use tools. Tools are functions or APIs that extend what an agent can do:

Information Tools

Web search
Database queries
API calls
File reading

Action Tools

Code execution
Email sending
File writing
System commands

# Tool Definition Example
tools = [
    {
        "name": "search_web",
        "description": "Search the web for information",
        "parameters": {
            "query": "string - the search query"
        }
    },
    {
        "name": "execute_sql",
        "description": "Execute a SQL query on the database",
        "parameters": {
            "query": "string - the SQL query to execute"
        }
    },
    {
        "name": "send_email",
        "description": "Send an email to a recipient",
        "parameters": {
            "to": "string - recipient email",
            "subject": "string - email subject",
            "body": "string - email body"
        }
    }
]

Quick Check

Why are tools essential for agentic AI systems?

Multi-Agent Orchestration

Complex tasks often require multiple specialized agents working together. This is where orchestration patterns become crucial:

Supervisor Pattern

A "manager" agent delegates tasks to specialized worker agents and coordinates their outputs. Best for hierarchical workflows with clear task decomposition.

Swarm Pattern

Agents communicate peer-to-peer without central coordination. Best for emergent behavior and parallel exploration of solution spaces.

Pipeline Pattern

Each agent processes output from the previous agent in a chain. Best for sequential workflows where each step transforms the data.

Quick Check

Which orchestration pattern would be best for a code review system where code is analyzed, then tested, then documented?

Safety and Guardrails

Autonomous agents require careful safety considerations. Key guardrail patterns include:

Human-in-the-Loop: Require human approval for high-stakes actions
Sandboxing: Limit what tools and resources an agent can access
Rate Limiting: Prevent runaway loops or excessive resource consumption
Audit Logging: Record all agent decisions and actions for review
Kill Switches: Ability to immediately halt agent execution

Quick Check

What is the purpose of 'Human-in-the-Loop' in agentic AI systems?