AI Agents: how to design autonomous systems with LLMs

What are AI Agents and why do they matter?

An AI agent is more than a chatbot. It's a system that combines:

• a language model (LLM) for reasoning and logic
• external tools (APIs, databases, emails, custom functions)
• detailed instructions to guide its behavior

This synergy allows the agent not just to respond, but to act—analyzing documents, processing refunds, generating reports, automating complex conversations, and more.

When does it make sense to use an AI Agent?

According to OpenAI, AI agents are especially useful when:

• decisions are complex, nuanced, or involve exceptions
• rules change frequently and are hard to maintain using traditional logic
• data is unstructured: emails, PDFs, transcripts

Practical example: fraud detection in payments. A rule-based engine checks boxes, but an AI agent can evaluate weak signals, historical context, and anomalies more effectively.

How to design an effective AI Agent

An AI agent is built on three pillars:

1. language model (LLM): drives reasoning and task execution
2. tools: APIs, custom functions, databases, email services for action
3. instructions: structured prompts with rules, exceptions, messages, and flows

Best practices for design:

• start with the best model available, then optimize for cost and performance
• write clear, step-by-step instructions anticipating edge cases and ambiguity
• explicitly define when and how each tool should be used.

Architectures: Single-Agent or Multi-Agent?

Choosing between a single agent or a network of specialized agents depends on task complexity:

• single-agent: easier to build and maintain, great for linear workflows
• multi-agent: ideal for varied and specialized tasks. Two common patterns:
  • manager: a central agent manages others as tools
  • decentralized: agents transfer control based on context

Examples of multi-agent implementations are available on GitHub.

Safety and reliability in production

Bringing an agent into production requires strong guardrails. Key measures include:

• filters against harmful or off-topic prompts
• recognition of sensitive data to prevent exposure
• validations before critical actions (e.g. refunds, submissions)
• escalation to a human operator in case of uncertainty or errors

Conclusion and next steps

AI agents represent a concrete step toward cognitive automation. If your processes involve unstructured data, dynamic rules, or high variability, now is the perfect time to start exploring the potential of this technology.

To operate effectively, agents need to interact with specialized tools. myBiros is a solution designed for document processing, and thanks to its APIs, it can be seamlessly integrated into agent-based systems.

Want to learn more? Get in touch. We’re here to help you take the first step.

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