AI Agents & Workflows

What Are AI Agents?

AI agents are systems that can:

Plan - Break down goals into steps
Use Tools - Access external capabilities (search, code execution, APIs)
Remember - Maintain context across multiple steps
Execute - Take actions autonomously
Iterate - Learn from results and adjust

Unlike simple assistants that respond to single prompts, agents can work through complex, multi-step tasks with minimal human intervention.

Agent Architecture

A typical AI agent has four core components:

Planning

Breaking goals into actionable steps

Tools

External capabilities the agent can use

Memory

Storing and retrieving information

Execution

Taking actions and processing results

1. Planning

The agent decides what to do and in what order. Planning Strategies:

ReAct (Reasoning + Acting)

The agent alternates between reasoning and acting:

Thought: I need to find the latest stock price
Action: Search for "AAPL stock price"
Observation: $185.50 as of today
Thought: Now I need to compare with last month
Action: Search for "AAPL stock price last month"
Observation: $178.20 in November
Thought: I can now calculate the change
Action: Calculate (185.50 - 178.20) / 178.20 * 100
Observation: 4.1% increase
Thought: I have enough information to answer
Final Answer: Apple stock increased 4.1% from last month

Best for: Tasks requiring research and analysis

Chain-of-Thought

The agent thinks through the problem step-by-step before acting:

Goal: Write a competitive analysis report

Plan:
1. Identify top 5 competitors
2. Research each competitor's features
3. Create comparison table
4. Analyze strengths/weaknesses
5. Write executive summary
6. Compile final report

Now executing step 1...

Best for: Complex tasks with clear structure

Tree of Thoughts

The agent explores multiple possible paths and chooses the best:

Goal: Solve a complex problem

Branch 1: Approach A → Evaluate → Score: 7/10
Branch 2: Approach B → Evaluate → Score: 9/10
Branch 3: Approach C → Evaluate → Score: 5/10

Choose Branch 2 and continue...

Best for: Problems with multiple valid approaches

2. Tools (Function Calling)

Tools extend what the agent can do beyond text generation. Common Tool Categories:

Information Retrieval

Web Search - Google, Bing, DuckDuckGo
RAG/Knowledge Base - Query documents
APIs - Weather, stocks, news
Database Queries - SQL, NoSQL

Example: Agent searches web for current information

Computation

Code Execution - Python, JavaScript
Calculators - Math operations
Data Analysis - pandas, numpy

Example: Agent runs Python code to analyze data

Communication

Email - Send/read emails
Slack/Teams - Post messages
SMS - Send text messages

Example: Agent sends summary email after completing task

Content Creation

Image Generation - DALL-E, Midjourney
Document Creation - PDFs, presentations
Code Generation - Write and test code

Example: Agent creates charts from data

Automation

File Operations - Read, write, move files
API Calls - Interact with services
Workflow Triggers - Start other processes

Example: Agent saves results to Google Sheets

How Tool Use Works:

# Agent decides it needs to search
agent_thought = "I need current weather data"

# Agent calls the tool
tool_call = {
    "name": "web_search",
    "parameters": {
        "query": "San Francisco weather today"
    }
}

# Tool executes and returns result
tool_result = "Sunny, 68°F, light breeze"

# Agent uses result to continue
agent_response = "Based on the search, the weather in SF is sunny and 68°F..."

3. Memory

Agents need to remember information across steps. Types of Memory:

Short-Term Memory
Long-Term Memory
Episodic Memory
Semantic Memory

What: Current conversation/task contextHow: Stored in the prompt/context windowLimitations: Limited by context window sizeExample: Remembering the last 5 steps in current task

4. Execution

The agent runs its plan, using tools and memory. Execution Loop:

1. Receive goal
2. Create plan
3. For each step:
   a. Reason about what to do
   b. Select appropriate tool
   c. Execute tool
   d. Observe result
   e. Update memory
   f. Decide next action
4. Compile final result
5. Return to user

Error Handling:

Retry failed operations
Adjust plan if stuck
Ask user for clarification
Gracefully fail with explanation

Multi-Step Workflows and Chains

Agents can be combined into workflows:

Sequential Chains

One agent’s output becomes the next agent’s input:

Agent 1: Research → Agent 2: Analyze → Agent 3: Write Report

Example: Content creation pipeline

Research agent gathers information
Analysis agent identifies key points
Writing agent creates article
Editing agent polishes content

Parallel Chains

Multiple agents work simultaneously:

        ┌─ Agent A: Search Web
Goal ──┼─ Agent B: Query Database  ─→ Combine Results
        └─ Agent C: Analyze Files

Example: Competitive analysis

Agent A searches web for competitor info
Agent B queries internal database
Agent C analyzes uploaded documents
Results combined into report

Hierarchical Agents

A supervisor agent coordinates worker agents:

Supervisor Agent
    ├─ Research Agent
    ├─ Analysis Agent
    └─ Writing Agent

Example: Complex project

Supervisor breaks down project
Assigns tasks to specialized agents
Monitors progress
Combines outputs

Real-World Agent Examples

Research Agent

Goal: “Research the top 5 AI trends in 2025”Process:

Search web for “AI trends 2025”
Extract top sources
Read and summarize each
Identify common themes
Rank by importance
Compile report with citations

Tools: Web search, content extraction, summarization

Data Analysis Agent

Goal: “Analyze sales data and create insights report”Process:

Load CSV file
Clean and validate data
Calculate key metrics
Identify trends and anomalies
Create visualizations
Write insights report
Save results

Tools: Python/pandas, plotting libraries, file operations

Customer Support Agent

Goal: “Answer customer questions from knowledge base”Process:

Receive customer question
Search knowledge base (RAG)
If found: Generate answer with citations
If not found: Search web or escalate
Log interaction
Follow up if needed

Tools: RAG, web search, ticketing system API

Content Creation Agent

Goal: “Create social media posts for blog article”Process:

Read blog article
Extract key points
Generate Twitter thread
Generate LinkedIn post
Generate Instagram caption
Create images for each platform
Schedule posts

Tools: Content extraction, image generation, social media APIs

Automation Agent

Goal: “Monitor inbox and categorize emails”Process:

Check for new emails
Analyze each email
Categorize (urgent, info, spam, etc.)
Apply labels
Draft responses for urgent items
Send summary report

Tools: Email API, classification, response generation

Agent Frameworks and Tools

No-Code / Low-Code

Zapier AI

Build automation workflows with AI

Make (Integromat)

Visual workflow builder with AI

n8n

Open-source workflow automation

Voiceflow

Build conversational agents

Developer Frameworks

LangChain

Most popular agent framework (Python/JS)

AutoGPT

Autonomous GPT-4 agent

CrewAI

Multi-agent collaboration framework

AutoGen

Microsoft’s multi-agent framework

Building Your First Agent

Simple Agent with LangChain (Conceptual)

# 1. Define tools
tools = [
    WebSearchTool(),
    CalculatorTool(),
    FileWriterTool()
]

# 2. Create agent
agent = Agent(
    llm=ChatGPT(),
    tools=tools,
    memory=ConversationMemory()
)

# 3. Give it a goal
result = agent.run(
    "Research the population of the 5 largest cities in California, "
    "calculate the total, and save to a file"
)

What happens:

Agent searches for California cities
Extracts population data
Uses calculator to sum
Writes results to file
Returns confirmation

No-Code Agent with Zapier

Choose Trigger

“When new email arrives with subject containing ‘invoice’”

Add AI Step

“Extract invoice details (amount, date, vendor)”

Add Action

“Create row in Google Sheets”

Add Notification

“Send Slack message with summary”

Best Practices

1. Clear Goals

❌ Vague: “Help me with marketing” ✅ Specific: “Research top 3 competitors, analyze their pricing, and create a comparison table”

2. Appropriate Tools

Give agents only the tools they need:

Too few → Can’t complete task
Too many → Gets confused, wastes time

3. Safety Guardrails

Approval gates for critical actions (sending emails, making purchases)
Budget limits on API calls
Timeout limits to prevent infinite loops
Human oversight for important decisions

4. Error Handling

Agents should:

Retry failed operations (with limits)
Explain what went wrong
Ask for help when stuck
Fail gracefully

5. Monitoring

Track:

Success/failure rates
Cost per task
Time to completion
Tool usage patterns

Limitations and Challenges

Reliability

Agents can make mistakes
May take unexpected paths
Can get stuck in loops

Cost

Multiple LLM calls add up
Tool usage costs
Storage for memory

Complexity

Harder to debug than simple prompts
Unpredictable behavior
Requires more oversight

Security

Agents can access sensitive data
Need proper authentication
Risk of unintended actions

The Future of Agents

Current State (2025):

Agents work well for structured tasks
Require human oversight
Best for automation and research

Near Future:

More reliable and predictable
Better at complex reasoning
Seamless tool integration
Multi-agent collaboration

Long-term Vision:

Fully autonomous task completion
Proactive assistance
Learning from experience
Human-level planning

Curated Resources

LangChain Agents Tutorial

Build your first agent

AutoGPT Documentation

Learn autonomous agents

Agent Patterns

Anthropic’s guide to building agents

Multi-Agent Systems

CrewAI blog on agent collaboration

Next Steps

Choosing the Right Model

Learn how to select the best AI model for your agents and workflows

​AI Agents & Workflows

​What Are AI Agents?

​Agent Architecture

Planning

Tools

Memory

Execution

​1. Planning

​2. Tools (Function Calling)

​3. Memory

​4. Execution

​Multi-Step Workflows and Chains

​Sequential Chains

​Parallel Chains

​Hierarchical Agents

​Real-World Agent Examples

​Agent Frameworks and Tools

​No-Code / Low-Code