As AI applications advance, enterprises increasingly rely on AI agents to enhance their operations. These intelligent systems can reason through problems, connect to external tools and systems, and perform complex tasks autonomously. However, complexity increases as applications scale and incorporate multiple specialized agents, requiring careful orchestration and management. Building effective AI agents demands a solid understanding of AI principles, familiarity with available tools and frameworks, and a well-planned development strategy.
This step-by-step guide provides everything you need to know about AI agent development. You’ll learn the fundamentals of building AI agents, how these systems work, the key development frameworks to consider, and how the Fiddler AI Observability and Security Platform supports the responsible deployment of AI agents.
Agent Development Fundamentals
Agent development involves creating intelligent, goal-oriented systems to reason, learn, and interact with users or other agents. These systems autonomously perform tasks and interact with external tools to achieve specific objectives.
Modern agent development often uses orchestration tools like Agent Development Kits (ADKs) to simplify composing multiple specialized agents. These tools coordinate and manage interactions between different agents, usually referred to as an agent engine, enabling seamless communication and collaboration.
Core components involved in agent development:
- Reasoning Capabilities: The ability of an AI agent to think through problems and make decisions autonomously.
- Integration with External Tools and APIs: Connecting the agent to various data sources, tools, and systems to gather information and take actions.
- Learning from Prior Actions: AI agents can learn from past interactions and feedback, developing adaptive behavior that improves performance and decision-making over time.
- Performance Testing and Evaluation: Continuous monitoring ensures the agent meets the required safety, quality, and task-specific objectives.
- Building with Agentic Frameworks: Developers often use modern agentic frameworks like Langraph and Amazon Bedrock to build, scale, and deploy autonomous agents. These frameworks provide powerful abstractions, built-in integrations, and orchestration capabilities that accelerate development and support multi-agent collaboration in enterprise environments.
Step-by-Step to Building an AI Agent
Building an AI agent requires a structured approach to ensure it functions effectively, safely, and responsibly.
Here are the key steps to follow:
1. Define the Agent’s Objective and Operational Context
Start by clearly defining the problem your agent will solve and the environment in which it will operate. For example, if your goal is to automate customer support, the agent must be designed to understand customer queries and provide accurate responses.
2. Gather, Clean, and Structure Data
Data forms the foundation of AI agent development. Ensure your data is high-quality, relevant, and well-structured to support the agent’s learning and decision-making. This step involves cleaning raw data, filling in missing values, and organizing it for easy access and interpretation.
3. Assemble a Cross-Functional Team
Building AI agents requires collaboration among diverse stakeholders. A cross-functional team should include:
- Data scientists to design and implement machine learning models.
- Machine learning engineers integrate AI algorithms into the agent’s architecture.
- Domain experts who understand the specific use case and business requirements.
- Product managers and stakeholders to ensure the agent meets business objectives and aligns with customer needs.
4. Choose the Right Technology Stack
Selecting the right tools and frameworks depends on your use case and the complexity of the agent. Consider options such as:
- Frameworks for agent-powered applications: These enable language processing, reasoning, and communication capabilities.
- Tools for multi-agent systems: These simplify the development of systems with multiple interacting agents.
- Collaboration tools: These help teams coordinate AI workflows and integrate AI-driven processes.
- Proprietary frameworks: Custom-built solutions designed to meet specialized enterprise needs.
5. Design the Agent Architecture
Define the architecture of the agent by outlining its core components:
- Memory: Stores context and past interactions to enable effective learning and adaptation.
- Decision Logic: The reasoning system that guides the agent’s decision-making process, allowing it to plan and execute tasks autonomously.
- Tool Execution and Access: Integrates with external tools and systems, such as databases, APIs, and LLMs, to expand the agent’s capabilities and ensure seamless application integration.
6. Develop and Test the Agent
With the design in place, begin development by building incrementally and testing each feature as it’s developed. Automated testing ensures the agent functions as intended and meets quality standards.
7. Deploy and Monitor in Production
Once the agent is ready, deploy it to production. Continuous monitoring is essential to build production-ready agents that maintain performance and reliability.
AI observability platforms like Fiddler allow tracking real-time behavior, identifying issues, and improving agent performance.
Agent Development Frameworks
When developing AI agents, enterprises can choose between building them from scratch or using existing frameworks. Each approach has its advantages and trade-offs, depending on the complexity of the use case and available resources. A thoughtful approach is essential to build effective agents that align with your business objectives and technical requirements.
Building an AI Agent from Scratch
Developing an AI agent from scratch offers complete control over its architecture, logic, and data governance. This approach is ideal for enterprises with specific requirements and dedicated engineering teams. However, it can be resource-intensive and time-consuming, especially when building agentic systems that must interact seamlessly with other AI agents and tools.
Utilizing Existing Orchestration Frameworks
For many organizations, using existing orchestration frameworks accelerates development. These frameworks provide ready-to-use components that simplify building AI agents and reduce the need for extensive custom coding. This option is well-suited for businesses that want to deploy AI agents quickly without the complexity of starting from scratch.
Benefits of Building AI Agents
Building AI agents offers numerous advantages, including:
- Accelerated Automation: AI agents can automate repetitive tasks, reducing human involvement and freeing up resources for higher-value activities.
- Intelligent Decision-Making: By leveraging machine learning and deep learning techniques, AI agents can make more informed, data-driven decisions that enhance overall effectiveness.
- Improved Scalability: AI agents can handle increasing tasks without requiring additional human resources, making them ideal for scaling business operations and enabling complex coordination between agents to support scalable applications.
- Enhanced Customer Experience: AI agents can improve customer interactions through personalized, responsive services, while workflow agents can orchestrate interactions across various touchpoints to deliver seamless experiences.
When to Use AI Agents: Key Use Cases
AI agents are highly effective in a variety of enterprise applications, including:
- Customer Support Automation: AI agents can automate customer support functions like chatbots that efficiently handle inquiries and resolve issues.
- Data Processing and Analysis: AI agents help process and analyze large datasets, generating real-time insights to support decision-making.
- IT Operations: AI agents excel in tasks like anomaly detection, triage, and other IT management functions, helping teams maintain system reliability.
- Sales and Marketing: AI agents can provide personalized recommendations, automate customer segmentation, and support targeted marketing campaigns.
Empowering Enterprise Innovation with AI Agents
AI agents have rapidly become essential drivers of innovation and efficiency in today’s enterprise landscape. They automate processes, enhance decision-making, and enable scalability, helping businesses stay competitive in an increasingly AI-driven world. However, building these agents successfully requires the right tools, frameworks, and observability solutions to ensure they operate reliably and responsibly within a complex technology ecosystem.
In real-world applications, many systems rely on multiple agents working on different tasks within a single application. These agents collaborate to deliver complex solutions, enabling organizations to achieve end-to-end automation and high performance at scale.
Enterprises can confidently deploy AI agents with Fiddler’s Agentic Observability, which offers comprehensive monitoring and agentic AI security features. This observability layer ensures real-time performance tracking, transparency, accountability, and effectiveness so businesses can fully leverage the power of AI.
Learn how Fiddler’s Trust Service enhances AI agent monitoring and security, empowering you to deploy AI agents confidently in today’s dynamic enterprise landscape.
Frequently Asked Questions About Building AI Agents
1. What are the different types of AI agents?
AI agents vary widely based on their design and purpose. Common types include:
- Reactive agents that respond to specific stimuli without memory.
- Deliberative agents use reasoning and planning to make decisions.
- Learning agents that improve performance over time by analyzing past interactions.
- Hybrid agents that combine multiple capabilities to handle complex tasks effectively.
2. Can I build an AI agent on my own?
While experienced developers can build a simple AI agent independently, creating a robust, enterprise-grade AI agent typically requires a team of experts, including data scientists, ML engineers, and domain specialists. Leveraging existing frameworks and platforms like Fiddler can streamline development and deployment, making building your AI agent easier and responsibly.
3. How do I choose the right algorithm for my AI agent?
Selecting the right algorithm depends on your agent’s objectives, the nature of your data, and the tasks' complexity. For example, generative tasks could use deep learning models like transformers. To find the best fit, it is recommended to consult with data scientists and test different models.
4. What are the key steps in training an AI agent?
Training an AI agent typically involves:
- Data collection and preprocessing: Gathering high-quality, relevant data and cleaning it for use.
- Model selection: Choosing an appropriate machine learning model.
- Training: Feeding data into the model to adjust its parameters.
- Validation: Testing the agent on unseen data to evaluate performance.
- Iteration: Refine the model based on performance and retrain it as needed.
5. What is the cost of creating a complex AI agent?
The cost of building an AI agent can vary widely, depending on factors like complexity, data requirements, team expertise, and infrastructure. A simple AI chatbot may cost a few thousand dollars. At the same time, a sophisticated, multi-agent system with extensive integrations and governance could require significant investment, often reaching hundreds of thousands of dollars.
6. What kind of data does an AI agent need to function?
An AI agent needs high-quality, relevant data tailored to its intended tasks. For example:
- Structured data (e.g., spreadsheets, databases) for tasks like classification or analysis.
- Unstructured data (e.g., text, images) for language generation or image recognition tasks.
- Real-time data streams for applications that require immediate responses or continuous learning.
