AI Agents & Workflows

Building AI systems that plan, use tools, and act autonomously

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.

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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

What: Current conversation/task context

How: Stored in the prompt/context window

Limitations: Limited by context window size

Example: Remembering the last 5 steps in current task

Long-Term Memory

What: Information persisted across sessions

How: Stored in vector databases or traditional databases

Limitations: Retrieval accuracy, storage costs

Example: Remembering user preferences, past conversations

Episodic Memory

What: Specific past experiences/events

How: Stored with timestamps and context

Limitations: Relevance decay over time

Example: “Last time you asked about this, we found…”

Semantic Memory

What: General knowledge and facts

How: RAG over knowledge base

Limitations: Needs to be kept up-to-date

Example: Company policies, product information

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

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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

1. Research agent gathers information
2. Analysis agent identifies key points
3. Writing agent creates article
4. 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

1. Agent A searches web for competitor info
2. Agent B queries internal database
3. Agent C analyzes uploaded documents
4. 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

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Real-World Agent Examples

Research Agent

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

Process:

1. Search web for “AI trends 2025”
2. Extract top sources
3. Read and summarize each
4. Identify common themes
5. Rank by importance
6. Compile report with citations

Tools: Web search, content extraction, summarization

Data Analysis Agent

Goal: “Analyze sales data and create insights report”

Process:

1. Load CSV file
2. Clean and validate data
3. Calculate key metrics
4. Identify trends and anomalies
5. Create visualizations
6. Write insights report
7. Save results

Tools: Python/pandas, plotting libraries, file operations

Customer Support Agent

Goal: “Answer customer questions from knowledge base”

Process:

1. Receive customer question
2. Search knowledge base (RAG)
3. If found: Generate answer with citations
4. If not found: Search web or escalate
5. Log interaction
6. Follow up if needed

Tools: RAG, web search, ticketing system API

Content Creation Agent

Goal: “Create social media posts for blog article”

Process:

1. Read blog article
2. Extract key points
3. Generate Twitter thread
4. Generate LinkedIn post
5. Generate Instagram caption
6. Create images for each platform
7. Schedule posts

Tools: Content extraction, image generation, social media APIs

Automation Agent

Goal: “Monitor inbox and categorize emails”

Process:

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

Tools: Email API, classification, response generation

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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

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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:

1. Agent searches for California cities
2. Extracts population data
3. Uses calculator to sum
4. Writes results to file
5. Returns confirmation

No-Code Agent with Zapier

1. Choose Trigger

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

2. Add AI Step

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

3. Add Action

   “Create row in Google Sheets”

4. Add Notification

   “Send Slack message with summary”

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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

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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

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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

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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

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Next Steps

Choosing the Right Model

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