A Practical Guide to Building AI Agents for your Startup

The world of technology is changing rapidly, from artificial intelligence (AI) to cybersecurity. Of the many AI variants, one that is taking the world by storm is large language models (LLMs). LLMs are built to handle complex and multi-step tasks, and through advances in reasoning, multimodality and tool use, LLM systems have a new category: agents.

As more and more businesses look to leverage new technologies, having an agent can be highly beneficial. These agents can perform tasks that would free up time for founders to focus on important tasks such as finding investors.

In this guide, we look at how you can build your own AI agent and more.

What Is an Agent?

In the most basic terms, agents are systems that independently accomplish tasks on your behalf. LLM agents are advanced AI systems designed for creating complex text-based workflows that need sequential reasoning. These agents can think ahead, remember past conversations, and use various tools to adjust their responses based on the situation and style needed.

Agents typically consist of four components:

• Agent/brain – This is a language model/large action model that processes and understands language based on trained data.
• Planning – Through planning agents can reason, breakdown complicated tasks into smaller, manageable parts and develop specific plans for each part.
• Memory – Memory helps agents handle complex LLM tasks using a record of what has been done before.
• Tool use – These are the different resources that help agents connect with external environments to perform certain tasks.

What can agents do for you?

Across various industries, LLM agents deliver benefits that align with your business goals in many ways, including:

Efficiency and automation: Agents can take care of any repetitive tasks you might have, handling large volumes of inquiries or data processing in a matter of seconds. This means your employees spend less time on mundane tasks and more on strategic initiatives across the business.

Elevated decision support: Agents can analyse large amounts of datasets and pull insights and patterns you might overlook. This can help reduce errors, improves risk assessment and strengthens data-driven decision making in areas like finance, supply chain and other industries.

Customisation and customer experience: LLM agents can produce hyper-personalised interactions by customising responses based on customer or employee preferences. Agents can have natural, context-aware conversations and help you deliver tailored experiences that drive loyalty and business growth.

Scaling and consistency: If you are looking to scale your business, you cannot afford any delays. By leveraging off LLM agents you can scale easily, manage large amounts of requests at once while maintaining accuracy and reliability.

Innovate: By integrating LLM agents you can have tech such as 24/7 chatbot support and intelligent automation. This can improve your overall operations and leaves you with the capacity to innovate further.

Building your own LLM Agents

Creating cost-effective, tailored agents can be simpler than you think. According to OpenAI, these are the steps you should take when building your own LLM agent.

Step 1: Select your models

Every model has its own strengths, weaknesses and trade-offs related to task complexity, latency and cost. You might want to consider a variety of models for different tasks in the workflow.

You need to remember that not every task needs the smartest model. For example, a task like retrieval or intent classification can be handled by smaller, faster models, while harder tasks like decisions on finance may benefit from a more capable model. It’s all about knowing what you want your model to do.

In summary, the principles for choosing your model are easy: set up evaluations to establish the performance baseline, focus on meeting your accuracy target with available models and optimise for cost and latency by replacing larger models with smaller models where possible.

Step 2: The defining tools

The tools you will choose will extend the capabilities of your agent by using application processing interfaces (APIs) for underlying applications or systems. For a legacy system without APIs, agents can rely on computer-use models to interact directly with those applications and systems through web and application UIs – basically it can interact with elements within the application like a human would.

Each tool that you use should have a standardised definition – this will allow it to be flexible and have many-to-many relationships between tools and agents.

So, to build your agent you will need three different types of tools:

· Data tool – to enable your agents to retrieve context and the information they need to execute workflows.

· Action tool – to allow your agents to interact with systems and take actions such as adding new info, updating records or sending messages.

· Orchestration – to enable your agents to serve as tools for other agents.

Step 3: Configuring your instructions

High-quality instructions are extremely critical for agents because they reduce ambiguity and improve the decision-making of the agent – making for a much smoother workflow executing and fewer errors.

Some of the best practices for agent instructions are:

Use existing documents: When creating routines, consider using existing operating procedures, support scripts, or policy documents to create LLM-friendly routines.

Prompt your agents to breakdown tasks: By providing smaller, clearer steps it can help your agents minimise ambiguity and helps the model follow instructions better.

Clearly define actions: Ensure that every step in your routine coincides with a specific action or output. Being specific about the action (and even the wording of a user-facing message) leaves less room for errors in interpretation.

Capture edge cases: Real-world interactions can create decision points such as how to proceed when a user provides incomplete information or asks an unexpected question. Having a robust routine means anticipating common variations and includes instructions on how to handle them with conditional steps or branches if a required piece of information is missing.

Step 4: Orchestration

When it comes to LLM agents, orchestration refers to the processing of coordinating and managing multiple AI agents, each with its own task or function. When all the fundamental components are in place, your orchestration pattern enables your agent to execute workflows effectively.

Orchestration patterns fall into two categories:

Single-agent systems: This is when a single model is equipped with appropriate tools and instructions and executes workflows in a loop. In this case, a single agent can handle multiple tasks by periodically adding tools, keeping complexity manageable and simplifying evaluation and maintenance.

Multi-agent systems: In this system workflow execution is distributed across multiple coordinating agents. The agent coordinates multiple specialised agents via tool calls who then hand-off the tasks to each other based on their specialisations.

Safety and Security: Implementing Guardrails

With any technology you build or use, you have to have considerations in place to manage data privacy risks such as system leaks or reputational risks - these are called Guardrails -  a set of safety controls that monitor and dictate interaction with an LLM application.

Types of guardrails

There are different types of guardrails in LLM, including:

Input validation and sanitisation: This guardrail serves as the first line of defence in AI safety. These guards ensure that any data fed into your model is safe and appropriate and in the correct form.

Syntax and format checks: These guards are there to maintain system integrity. They verify that the input adheres to the expected format and structure.

Content filtering: This guardrail focuses on removing sensitive or inappropriate content before it reaches the model. It detects and removes identifiable information to avoid potential privacy issues and filtering.

Jailbreak attempt detection: These are the guards that prevent security breaches and keep your business out of any bad news headlines.

Output monitoring and filtering: These guards fall into two categories: preventing damage and ensuring maximum performance.

Dynamic: This guard involves using your own data to augment existing guards. This allows your guard to evolve based on your system’s specific needs and usage patterns.

Some guardrails to consider for your model:

·  Guardrails AI is an open-source python package that provides guardrail frameworks for LLM applications.

· Reliable AI Markup Language (RAIL) is language-agnostic and human-readable format for specifying specific and corrective actions for LLM outputs.

·  NVIDIA NeMo-Guardrails is another open-source toolkit that provides programmatic guardrails to LLM systems.

With these steps and best practices, you can build AI agents that cut costs, boost efficiency and empower your startup to innovate without compromise.