AI Agents — A Software Engineer’s Overview

AI agents have got a lot of attentions recently. TechRepublic predicted that the use of AI agents will surge in 2025. Sam Altman from OpenAI echoes this in his blog post, saying “we may see the first AI agents ‘join the workforce’ and materially change the output of companies.”

In this article, we will provide an overview from a software engineer perspective to discuss what AI agents are, their types, and their common architectural design patterns. We will explore real-world applications and success stories, analyze their benefits, and examine the key challenges and opportunities in developing AI agents. We will also provide insights into when to use and when not to use AI agents, and discuss the best practices for building effective AI agents.

Table of Contents

• What is an AI Agent?
• What are the Types of AI Agents?
• What are the Common Architectural Design Patterns for AI Agents?
• What are Real-World Applications and Success Stories on AI Agents?
• What are the Benefits of AI Agents?
• What are the Key Challenges and Opportunities in Developing AI Agents?
• When to Use and When Not To Use AI Agents?
• What are the Best Practices for Building Effective AI Agents?
• What are the Key Take Always and Future Outlook on AI Agents?
• Appendix - Frequently Asked Questions (FAQs)

What is an AI Agent?

An artificial intelligence (AI) agent is a software system that builds upon AI models (e.g., large language models (LLMs)) and autonomously perceives and acts upon its environment to achieve specific goals. Key characteristics that define an AI agent include:

• Autonomy: Operates independently with minimal human intervention
• Goal-oriented: Works towards predefined objectives
• Environmental interaction: Senses and acts upon its surroundings
• Adaptability: Learns from experiences and improves performance
• Multi-capability: Handles various tasks from simple queries to complex problem-solving
• Tool utilization: Leverages available resources and APIs to extend functionality

Unlike traditional AI systems that execute predefined tasks, AI agents can make independent decisions, plan their actions, and adjust their strategies based on feedback and changing conditions.

What are the Types of AI Agents?

AI agents can be classified in several ways, each highlighting different aspects of their capabilities:

Based on Complexity

Type	Description	Example
Simple Reflex	Acts on current perception using predefined rules	FAQ chatbot with fixed responses
Model-Based Reflex	Uses a LLM model for better decisions	Chatbot with conversation history
Goal-Based	Evaluates actions based on goal achievement	Travel booking agent optimizing itineraries
Utility-Based	Considers value/utility of different outcomes	Product recommendation agent balancing user needs and profitability
Learning	Improves through experience and feedback	Game-playing agent learning from wins/losses

Based on Collaboration

Category	Types	Examples
Structural	Hierarchical Agents	Factory management system with supervisor and worker agents
GUI	GUI Automation	AutoGLM booking reservations through web interfaces
Roles	Development	ChatDev collaborative software development agents

Key Distinctions

• Workflows vs. Agents: Predefined sequences vs. autonomous decision-makers
• Agentic vs. Non-Agentic: Tool-using, learning capable vs. fixed-response systems

This categorization continues to evolve as new AI agent architectures and capabilities emerge.

What are the Common Architectural Design Patterns for AI Agents?

Core Architectural Components

An AI agent's architecture consists of four fundamental components: model, tools, memory, and planning, shown in the diagram below:

Figure: AI Agent Architecture

1. Model

Model acts as the central decision-maker for agent processes. It can be one or multiple language models (LMs) that are capable of understanding and generating text, images, or other data types. It can be fine-tuned to specific tasks and domains, enhancing its performance and accuracy and can be refined through examples of capabilities and tool usage.

2. Tools

Tools are the agent's interface to external systems. They include system APIs, functions, and data stores that enable the agent to interact with environments, databases, and other resources, even other AI agents. The tools include two major components, tool definition and tool execution. The tool definition describes the tool's name, description, and input schema, while the tool execution specifies how to use the tool to accomplish a specific task. The following JSON provides an example of tool definition for a database query tool:

tools = {
  "name": "Database Query Tool",
  "description": "Tool for querying a database",
  "input_schema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "SQL query to execute"
      }
    },
    "required": ["query"]
  }
}

Based on the tool's definition, the AI model can understand what the tool is, when to use it, and what input parameters it requires.
The following Python code provides an example of tool execution for the database query tool:

def execute_tool(query):
    if isinstance(query, str):
        # Execute the SQL query
        try:
          result = execute_sql_query(query)
          return result
        except Exception as e:
          return {"error": str(e)}
    else:
        raise ValueError("Unknown input: {}".format(query))

With the tool's definition and implementation in place, we can let the language model (e.g., Anthropic Claude 3.5 Haiku) know how to use the tool to accomplish a specific task. For example, the model can use the database query tool to retrieve information from a database and generate a response to a user query.

from anthropic import Anthropic
import os

client = Anthropic(api_key=os.environ.get('ANTHEPIC_API_KEY'))
MODEL_NAME = "claude-3-5-haiku-20241022"

user_message = "What are the top 5 most popular products?"

response = client.messages.create(
        model=MODEL_NAME,
        max_tokens=4096,
        messages=[{"role": "user", "content": user_message}],
        tools=tools,
    )

3. Memory

Memory is the agent's knowledge base, storing information for future reference and decision-making. It includes two major types, long-term memory and short-term memory. Usually, the long-term memory can be implemented using external databases, knowledge graphs, or retrieval augmented generation (RAG) systems. The short-term memory is a temporary storage space for information that is relevant to the current task or interaction. Memory enables agents to retain context across interactions, learn from past experiences, and adapt to changing environments. It can be used for storing user preferences, historical data, and task-specific information and provides necessary relevant context information for the model to understand the user requests, make right decisons, and generate appropriate responses.

4. Reasoning and Planning

Reasoning and planning present a (cyclical) process managing the agent's operations. It includes information processing, reasoning, action decision-making, memory, and state management. In order to achieve appropriate reasoning and planning results, the agent can be built by following some reasoning and logic frameworks, such as ReAct, CoT, or Tree-of-Thoughts, each offering unique capabilities and design patterns.

With these components in place, AI agents can perform complex tasks, make informed decisions, and adapt to changing conditions, enhancing their autonomy and problem-solving capabilities. For example, consider a software development AI agent. The workflow might look like the following diagram:

Figure: High-level flow of a coding agent (Source: Anthropic.com)

• A coding agent receives a user request/query to build a new feature.
• The agent uses its model to clarify, refine, and eventually understand the detailed requirements.
• It then leverages tools like files, code repositories, documentation, and APIs to gather relevant information.
• The agent plans the coding process, breaking it down into smaller tasks and sub-tasks.
• It executes the coding tasks, using the model to generate code snippets and the tools to access external resources.
• It also tests the code and provides feedback to the user.

Key Design Patterns

Generally, four common design patterns enhance AI agent capabilities:

1. Reflection

This design pattern focuses on self-evaluation and improvement. It enables agents to refine their output iteratively, enhancing performance over time. Examples include code review and optimization, where agents analyze their own work and make adjustments to improve quality.

Figure: Agentic Design Patterns - Reflection (Source: deeplearning.ai)

2. Tool Use

This design pattern emphasizes the agent's ability to leverage external resources to enhance its capabilities. By integrating with databases, APIs, and other tools, agents can access a wealth of information and functionality, expanding their problem-solving abilities. An example of this is a chatbot that uses external knowledge bases or web search tools to provide accurate answers to user queries.

Figure: Agentic Design Patterns - Tool Use (Source: deeplearning.ai)

3. Planning & Sequencing

This design pattern is to use a large language model (LLM) to autonomously decide on what sequence of steps to execute to accomplish a large task. It usually needs to creates multi-step execution plans, breaks down complex tasks, and sequences actions to achieve goals. For example, a travel booking agent might plan an itinerary by selecting flights, hotels, and activities based on user preferences.

Figure: Agentic Design Patterns - Planning (Source: deeplearning.ai)

4. Multi-Agent Systems

This design pattern focuses on collaboration between multiple agents to achieve complex tasks. By dividing responsibilities and coordinating actions, agents can work together to solve problems more efficiently. Examples include factory management systems with supervisor and worker agents, or collaborative software development systems with agents specializing in different tasks.

Figure: Agentic Design Patterns - Multi-Agent Collaboration (Source: deeplearning.ai)

What are Real-World Applications and Success Stories on AI Agents?

AI agents are delivering significant ROI across various industries. Here's how leading companies are implementing them:

Customer Service & Support

• 24/7 Availability: High-volume inquiry handling.
• Personalized Support: Data-driven interaction customization.
• Proactive Solutions: Issue prevention and early intervention.

For example,

Company	Use Case	Key Features
ServiceNow	24/7 AI Agent Platform	Enhanced productivity, reduced response times
Cosentino	Digital Workforce	Streamlined customer service operations
Deutsche Telekom	"askT" Internal Support	Automated employee inquiries for 80,000+ staff

Enterprise Applications

• Sales & Marketing
  • Lead qualification and nurturing
  • Campaign automation and analysis
• Process Automation
  • IT systems monitoring and resolution
  • Supply chain optimization
  • Financial analysis and reporting

For example,

Company	Use Case	Key Features
Johnson & Johnson	Drug Discovery	Autonomous chemical synthesis optimization
Moody's	Financial Analysis	Multi-agent system with 35 specialized agents
eBay	Software Development	Code generation and marketing automation

Organizations implementing AI agents strategically are seeing improvements in efficiency, cost reduction, and service quality. The key is selecting use cases that match organizational capabilities and business objectives.

What are the Benefits of AI Agents?

AI agents offer numerous advantages that can significantly enhance productivity and efficiency in various tasks. Here’s why they are essential:

• Autonomous Operation: AI agents can handle multi-step tasks with minimal human intervention. Unlike chatbots that need constant interaction, AI agents can proactively plan, reason, and take actions across different systems, freeing up your time for more critical tasks.
• Specific and Personalized Solutions: AI agents can provide tailored solutions based on individual needs and preferences. By analyzing user data and behavior, they can deliver personalized recommendations, advice, and support, leading to higher customer satisfaction and engagement.
• Skill Enhancement: AI agents empower individuals to achieve specialist-level results without prior expertise. Imagine being able to build software or create high-quality marketing content without needing technical or design skills. AI agents make this possible.
• Enhanced Decision-Making: AI agents excel at gathering data from multiple sources, analyzing it, and delivering results faster than manual methods. This leads to data-driven insights, optimized processes, and quick problem resolution, helping you make better decisions.
• 24/7 Availability: AI agents can operate around the clock, providing continuous service and support. This ensures prompt responses to customer inquiries, regardless of time zones or business hours, leading to improved customer satisfaction and personalized experiences.

These benefits combine to create significant operational improvements, cost savings, and enhanced service quality, making AI agents a valuable asset in any organization.

What are the Key Challenges and Opportunities in Developing AI Agents?

Building effective AI agents comes with several challenges, but also presents exciting opportunities. Let's explore the main considerations in simple terms:

People and Skills

• Finding Expert Talent: Just like you need experienced chefs to run a high-end restaurant, building AI agents requires specialists who understand complex technologies.
• Training and Development: Teams need continuous learning to keep up with rapidly evolving AI technologies.

Technical Challenges

• System Integration: Think of connecting LEGO blocks - AI agents need to fit smoothly with existing business systems.
• Computing Power: Running AI agents is like running multiple high-end gaming computers simultaneously - it requires significant computing resources.

Data and Knowledge

• Quality Information: AI agents need clean, accurate data to learn from - like students need good textbooks.
• Keeping Current: Information must be regularly updated to ensure AI agents give accurate advice.

Trust and Reliability

• Consistent Performance: AI agents must work reliably in real-world situations, like a dependable employee.
• Human Oversight: Finding the right balance between AI automation and human supervision.
• Building Trust: Making AI agents transparent and understandable to users.

Ethical Considerations

• Fairness: Ensuring AI agents make unbiased decisions and treat all users equally.
• Job Impact: Carefully managing how AI agents affect existing workforce roles.

Business Impact

• Investment: Like any major business initiative, implementing AI agents requires significant initial funding.
• Measuring Success: Tracking concrete benefits like improved efficiency, cost savings, and customer satisfaction.

Keys to Success

To overcome these challenges, organizations should:

1. Start small and scale gradually - Focus on specific use cases with clear benefits, then expand as you gain experience.
2. Invest in quality data and expertise - Like planting a garden, good seeds and skilled gardeners lead to a bountiful harvest.
3. Focus on user needs and experience - Design AI agents with the end-user in mind, ensuring they provide real value.
4. Maintain strong ethical guidelines - Build AI agents that respect privacy, fairness, and transparency.
5. Monitor performance and adapt continuously - Measure how well AI agents are working and make adjustments as needed.

Think of implementing AI agents like building a new department - it requires careful planning, the right resources, and ongoing management to succeed.

When to Use and When Not To Use AI Agents?

Think of AI agents like hiring a new employee - they're great for some tasks but might be overkill for others. Here's a practical guide:

When to Use AI Agents ✅

Scenario	Example	Why It Works
Complex, Unpredictable Tasks, Open-ended Problems	Customer support handling unique requests	Each case might need different steps and solutions
Multi-Step Processes	Travel booking that requires comparing options, checking availability, and coordinating details	Needs both conversation and action capabilities
Learning-Based Tasks	Personalized product recommendations that improve over time	Can learn from user feedback and adapt
Tasks Needing Flexibility	Software development assistance	Can handle varying requirements and approaches

When to Avoid AI Agents ❌

Scenario	Better Alternative	Why It Won't Work
Simple, Predictable Tasks	Regular automation scripts	Like using a sledgehammer to hang a picture - unnecessarily complex
Speed-Critical Operations	Direct API calls	AI agents add processing time and complexity
Fixed Workflows	Traditional programming	When steps are always the same, traditional code is more reliable

Important Tips 💡

1. Start Simple: Don't use an AI agent if a basic script will do
2. Consider Costs: Factor in both computing resources and development time
3. Test Thoroughly: Always test AI agents in a safe environment first
4. Monitor Performance: Keep track of how well the agent is doing its job

Think of it this way: Use AI agents when you need a smart, adaptable assistant who can handle complex tasks, but stick with simpler tools for routine, predictable work.

What are the Best Practices for Building Effective AI Agents?

Start with simplicity and increase complexity only when needed. Consider the tradeoffs between latency, cost, and task performance when deciding to implement agentic AI systems.

1. Foundation Work 🏗️

Data & Evaluation

• Ground Truth Collection: Gather high-quality, real-world data for benchmarking. The higher the quality, the better the agent's performance.
• Evaluation Framework: Define clear success metrics. Use a mix of automated and human evaluation for comprehensive assessment.
• Human Oversight: Incorporate expert feedback for nuanced assessment. Human judgment is crucial for evaluating complex tasks.

Scope & Design

• Clear Boundaries: Define agent capabilities and limitations. Avoid over-promising or under-delivering. Start with a narrow scope and expand gradually.
• Interaction Design: Specify input/output formats. Ensure seamless integration with existing systems. Consider user experience and accessibility.
• User Experience: Plan agent personality and communication style. Tailor responses to user preferences and context. Strive for natural, engaging interactions.

2. Technical Implementation 🔧

Architecture

• Keep Simple: Maintain simplicity in your agent's design. Start with a basic architecture and expand as needed. Avoid over-engineering. Focus on core functionality.
• Modular Design: Build small, focused agents instead of monolithic systems. Encapsulate functionality for easy maintenance and scalability. Use microservices architecture.
• Tool or Framework Selection: Choose appropriate frameworks. Optimize for speed, accuracy, and resource efficiency. Consider trade-offs between simplicity and flexibility. Use pre-trained models for quick start.
• Platform Integration: Consider managed solutions like AWS Bedrock Agent or Google's Vertex AI. Leverage cloud services for scalability and reliability. Use containerization for portability.

Learning Approaches

1. In-context learning (runtime adaptation). For example, a chatbot that learns from user feedback during a conversation.
2. Retrieval-based learning (external memory access). For example, a recommendation agent that retrieves information from a knowledge base.
3. Fine-tuning based learning (pre-trained optimization). For example, a language model (e.g., OpenAI Codex) fine-tuned for the software development tasks.

3. Security & Reliability 🔒

Security Measures

• Authorization: Implement flexible IAM permissions. Use role-based access control. Limit data access based on user roles. Implement secure data handling practices.
• Encryption: Use customer-managed keys. Encrypt data at rest and in transit. Implement secure key management practices.
• Action Confirmation: Add safeguards for critical operations. Implement multi-step verification for high-risk actions. Implement rate limiting and throttling.

Monitoring

• Logging: Comprehensive observability. Log all agent interactions and decisions. Use structured logging for easy analysis. Monitor agent health, performance and system resource usage.
• Testing: Automated evaluation frameworks. Use unit tests, integration tests, and end-to-end tests. Implement continuous integration and deployment pipelines.
• Quality Control: Content filtering and guardrails. Implement content moderation for user-generated content. Use profanity filters and sentiment analysis. Implement content policies and guidelines.

4. Scaling Strategy 📈

Gradual Deployment

1. Internal testing. Start with a small group of users or internal team members. Gather feedback and iterate.
2. Limited external release. Expand to a larger user base or select customers. Monitor performance and gather feedback.
3. Full production rollout. Scale to all users or customers. Monitor performance and user feedback. Implement continuous improvement.

Reusability

• Build reusable action catalogs. Create libraries of common actions and workflows. Use templates for common tasks. Implement version control for action catalogs. Use action chaining for complex workflows.
• Standardize knowledge bases. Create structured data repositories. Use knowledge graphs for data organization. Implement data versioning and access control. Use data pipelines for data processing.
• Create common guardrails. Implement content moderation policies. Use rule-based systems for content filtering. Implement user feedback mechanisms.

What are the Key Take Always and Future Outlook on AI Agents?

AI agents represent a significant evolution in software automation, offering powerful capabilities for complex task execution. Here are the key takeaways:

Key Points

• AI agents excel at handling unpredictable, multi-step tasks. They can learn, adapt, and make autonomous decisions.
• Success requires balancing complexity with practical needs. Start small and scale gradually.
• Proper architecture and design patterns are crucial. Focus on modular, maintainable design.

Future Outlook

The field of AI agents continues to evolve rapidly. Watch for:

• Enhanced autonomy capabilities. AI agents will become more proactive and self-sufficient.
• Improved collaboration between agents. Multi-agent systems will tackle complex tasks together.
• More sophisticated reasoning systems. AI agents will exhibit advanced problem-solving and decision-making skills.

Remember: Choose AI agents when the complexity of the task justifies the implementation effort. For simpler tasks, traditional automation methods may be more appropriate.

Appendix - Frequently Asked Questions (FAQs)

AI Agents vs. AI Models

While AI agents and AI models (particularly Large Language Models or LLMs) share foundations in machine learning and artificial intelligence, they serve distinct purposes. The key differences between AI models and AI agents can be simply summarized as follows:

Aspect	AI Models	AI Agents
Core Function	Execute specific tasks (text generation, translation, image recognition)	Orchestrate complex workflows and achieve broader goals
Decision Making	Reactive: respond to direct inputs	Proactive: plan and execute multi-step actions
Tools & Integration	Limited to training data	Can use APIs, databases, and external tools
Learning	Static after training	Continuous learning from experiences
Applications	Pattern recognition, predictions	Autonomous processes, workflow automation

For example, consider a customer seeking support:

• An AI model-powered chatbot would simply answer questions using its training data, limited to basic responses
• An AI agent could take comprehensive action by:
  • Accessing the customer's history
  • Diagnosing the specific problem
  • Suggesting targeted solutions
  • Taking concrete actions (e.g., processing refunds or scheduling technician visits)

AI Agents vs. Chatbots

While both AI agents and chatbots use artificial intelligence, they serve fundamentally different purposes:

Aspect	Chatbots	AI Agents
Primary Purpose	Conversation & Information	Action & Task Completion
Interaction Style	Reactive to user inputs	Proactive and autonomous
Task Handling	Single-step, predefined tasks	Multi-step, complex workflows
Tool Integration	Limited data access	Full system integration
Decision Making	Rule-based responses	Advanced reasoning (ReAct, CoT)
Learning Capability	Static or basic adaptation	Continuous improvement
Autonomy Level	Requires constant user input	Self-directed operation

Examples:

• Chatbot: Answering FAQs, providing product information
• AI Agent: Managing entire customer service cases, including diagnosis, solution implementation, and follow-up

The key distinction lies in autonomy and capability scope - chatbots are conversational interfaces, while AI agents are autonomous problem solvers.

AI Agents vs. Agentic AI

While the terms "AI agent" and "agentic AI" are often used interchangeably, they represent different aspects of autonomous AI systems:

AI Agents are focused on specific implementation and architecture:

• Individual autonomous programs with defined capabilities
• Built with specific components (Model, Tools, Orchestration)
• Designed for particular use cases or domains

Agentic AI represents a broader paradigm shift:

• A philosophy of AI system design emphasizing autonomy
• Focus on general-purpose problem-solving abilities
• Emphasis on system-level intelligence and adaptation

Key Distinctions:

• Scope: AI agents are concrete implementations; Agentic AI is an architectural approach
• Purpose: AI agents solve specific tasks; Agentic AI aims for broader autonomy
• Implementation: AI agents use current technologies; Agentic AI drives future development

This relationship is similar to how an individual web service (AI agent) relates to cloud computing (Agentic AI) - one is a specific implementation, the other a broader paradigm.

Agentic AI vs. Traditional AI

The emergence of agentic AI represents a fundamental shift in how AI systems operate and interact with the world. This comparison highlights the key differences:

Aspect	Traditional AI	Agentic AI
Behavior	Reactive: responds to specific inputs	Proactive: anticipates needs and takes initiative
Scope	Narrow: excels at specific tasks	General: adapts to various situations
Tool Usage	Basic: follows predetermined patterns	Advanced: strategic tool selection and mastery
Learning	Static: trained for specific tasks	Dynamic: continuous adaptation and improvement

Future Impact Areas from Agentic AI:

1. Business Operations: Autonomous workflow optimization and decision-making
2. Personal Services: Context-aware assistance in healthcare, education, and daily life
3. Innovation: Enabling non-experts to achieve specialist-level outcomes

This shift from traditional AI to agentic AI parallels the evolution from basic automation to truly intelligent systems that can partner with humans in solving complex challenges.

Comments

[Vinayak Mishra]

I'm new to this and trying to learn more about agents, thanks for posting this! Also, I came across a post on Agent as a Judge - can be helpful!

[Ivan Chen]

Thanks for sharing, @ai_joddd