Teaching AI Agents Skills

AI Agents leverage large language models (LLMs) known for their advanced linguistic capabilities. Beyond this, you can enrich their functionality by embedding knowledge into their context. Another effective way to expand their capabilities is by equipping them with tools, enabling the agent to perform specific actions.

Adding Skills to AI Agents

Some still apply the approach of classic conversation design with more deterministic dialogue flows when working with AI Agents, which sometimes feels like a scripted interrogation. However, when working with generative and conversational AI Agents, a more effective method is to assign specific skills or actions to the agent. This allows the agent to autonomously decide how and when to use these skills during interactions.

For example, let’s create a simple AI Agent with basic calculator skills: addition, subtraction, multiplication, and division. The AI Agent can autonomously identify the need for a skill and trigger the appropriate action during a conversation:

• User: What is 10 + 10?
• AI Agent identifies the "addition" skill and calculates the result.
• User: What is (10 × 10 ÷ 10) + 5 - 10?
• AI Agent breaks the complex formula into smaller steps, calling the appropriate actions in sequence to compute the result.

By enabling the agent to resolve complex operations dynamically, we unlock greater flexibility in task handling. However, it comes with a tradeoff: reduced control over the agent's actions. Much like working with humans, you must decide whether your use case benefits more from micro-control or from empowering a smarter, more autonomous agent.

Large Language Models can be made more predictable by reducing randomness, controlled through parameters like temperature. Additionally, you can enforce tool usage by requiring tool calls. However, both approaches come with the tradeoff of making conversations feel less human-like.

Applying Skills in Real-World Scenarios

This approach isn’t limited to simple calculations. It can be extended to more conversational use cases, such as a banking assistant.

The agent is equipped with the following skills:

• Retrieving account balances.
• Fetching exchange rates.
• Validating IBANs (bank account numbers).

A conversation could look like:

• User: What is my current account balance in Euros?
• AI Agent detects that the IBAN is missing and prompts the user to provide it. Once supplied, it validates the IBAN and retrieves the account balance.
• User: I meant in Dollars.
• AI Agent fetches the exchange rate and converts the balance to USD.

For combined queries:

• User: What is my current account balance in Dollars for account DE123412341234?
• AI Agent validates the IBAN, retrieves the balance, fetches the exchange rate, and calculates the final amount in USD in one step.

The agent autonomously decides which tools to call and in what order to fulfill the request.

Enhancing the Agent with Prompt Engineering

To ensure the agent behaves appropriately, some degree of prompt engineering is necessary. This involves providing the agent with a clear set of guidelines or "rules of behavior" tailored to its role. For instance, the banking agent might need:

• Instructions for recognizing missing information and prompting the user.
• Guidelines for prioritizing tool calls when multiple tasks are involved.

Unlocking Advanced Functionality

By merging the linguistic power of LLMs with tool-based functionalities, AI Agents move beyond simple conversations to deliver actionable, context-aware results. Whether performing calculations, assisting with banking tasks, or managing specialized use cases, this approach enables agents to handle complex operations seamlessly:

• Flexibility: The agent dynamically decides which tools or combinations of tools to use.
• Scalability: Adding new skills (e.g., loan calculations or fraud detection) becomes straightforward.
• User-Friendliness: Users can make complex requests in natural language, and the agent handles the rest.

While skill-based AI Agents offer autonomy and versatility, they come with the tradeoff of reduced control. To mitigate risks and strike a balance between control and flexibility, a hybrid approach can be effective: combine autonomous AI Agents where needed with deterministic, classic conversation design to ensure both adaptability and reliability.