Building AI agents: architecture, tools and best practices

Artificial intelligence is no longer just about models that predict or classify. Today, AI agents are transforming how software behaves by allowing systems to reason, plan and act autonomously. From customer support bots that solve complex requests to internal agents that automate operations, AI agents are becoming a core focus of AI software development.

What are AI agents and why they matter

An AI agent is a system that can perceive information, make decisions and take actions toward a goal. Unlike traditional automation, AI agents can adapt to new inputs and operate across multiple steps. This makes them ideal for complex workflows such as customer experience, finance operations or product personalization. For businesses, AI agents unlock faster execution, reduced costs and better user experiences.

Core architecture of AI agents

A solid architecture is critical in AI software development when building AI agents. Most modern systems share these layers:

• Perception layer: Connects the agent to data sources such as APIs, databases, documents or user inputs.

• Reasoning and planning layer: This is where large language models or hybrid AI logic analyze context, break down tasks and plan actions.

• Action layer: Executes tasks by calling tools, triggering workflows or interacting with external systems.

• Memory and feedback layer: Stores short-term and long-term context, enabling learning and continuous improvement.

  
  

Well-designed architectures emphasize modularity and observability. This is a key principle followed by any experienced AI software development company.

Tools powering modern AI agents

The rise of AI agents has been accelerated by powerful frameworks and platforms. Popular tools include:

• OpenAI and Anthropic APIs for reasoning and language understanding

• LangChain and LlamaIndex for orchestration and tool calling

• Vector databases such as Pinecone or Weaviate for semantic memory

• Cloud platforms like AWS and Azure for scalable AI software development services

Choosing the right stack is not just a technical decision. It requires AI consulting to align tools with business goals, compliance needs and long-term scalability.

Best practices for building AI agents

Successful AI agent projects follow clear best practices:

• Start with narrow, high-impact use cases

• Design with human oversight and fallback mechanisms

• Prioritize data quality and security from day one

• Continuously evaluate performance with real user feedback

Challenges and risks to consider

AI agents introduce new challenges in governance, ethics and reliability. Poorly designed agents can hallucinate, act unpredictably or expose sensitive data. This is why many organizations rely on specialized AI software development companies to ensure safe deployment and ongoing optimization.

How BySix helps companies build AI agents

At BySix, AI agents are built with a strong focus on architecture, security and business value. As an experienced AI software development company, BySix combines engineering excellence with practical AI consulting to help organizations design, deploy and scale intelligent agents.

From strategy to implementation, BySix supports end-to-end AI software development services, helping teams move from experimentation to production-ready AI agents. If you want to explore how AI agents can create real impact in your business, discover more insights on the BySix blog or reach out to the BySix team to start the conversation.