Building a Telco AI Agent Orchestration Layer with SWARM

What is SWARM?

SWARM is an experimental framework designed to manage complex interactions between multiple AI agents. Its key features include:

1. Agent-Based Architecture: SWARM allows you to create specialized agents, each with its own set of instructions and available functions (tools).

2. Dynamic Interaction Loop: The framework manages a continuous loop of agent interactions, function calls, and potential handoffs between a system of agents.

3. Stateless Design: SWARM is stateless between calls, offering transparency and fine-grained control over the orchestration process.

4. Direct Function Calling: Agents can directly call Python functions, enhancing their capabilities and integration with existing systems.

5. Context Management: SWARM provides context variables for maintaining state across agent interactions.

6. Flexible Handoffs: Agents can dynamically switch control to other specialized agents as needed.

7. Real-Time Interaction: The framework supports streaming responses for immediate feedback.

8. Model Agnostic: SWARM works with any OpenAI-compatible client, including models hosted on platforms like Hugging Face TGI or vLLM.

Why SWARM?

As an AI practitioner, I’ve come to value simplicity and rapid implementation. My philosophy is that if you can bring an abstract concept to life in just a few hours of focused deepwork, it’s worth exploring.

We often get caught up in complex frameworks and lengthy setups. But what if the most impactful ideas could be tested quickly, using nothing more than a simple colab notebook?

This approach challenges us to strip away unnecessary complexities. It’s about finding tools and methods that let us move swiftly from concept to prototype. By embracing this mindset, we open doors to innovation and make AI development more accessible.

Enough of the philosophy. Let’s do the real work.

Building a Telco Orchestration Layer

Let’s walk through an example of how to build an orchestration layer for a telco company using SWARM. The complete code is available here:

https://github.com/goodrahstar/orchestration_layer/blob/main/Orchestration%20layer%20using%20SWARM.ipynb

1. Define Your Agents

In our telco orchestration layer, we created a specialized agent orchestration layer to handle diverse tasks. Each agent is equipped with specific functions and knowledge, allowing them to efficiently address their designated areas of expertise.

• Triage Agent: Determines which agent is best suited to answer a user’s query.
• OLT Agent: Handles queries related to Optical Line Terminal (OLT) data.
• CRM Agent: Retrieves customer information from the CRM system.
• ACS Agent: Manages queries related to Auto Configuration Server (ACS) data.
• Solution Agent: Integrates with a knowledge graph to solve customer problems.

2. Implement Data Connectors (Tools)

For each agent, let’s define the tools they can use.

For example, here are the tools to load the data from different sources. These tools can be replaced into different kinds of tools such as text-to-SQL, API calls, or reading data from log files:

I have added an additional tool to interface with a knowledge graph that queries the Hugging Face space. This allows our multi-agentic system to tap into a vast repository of structured information, enhancing their ability to process and understand complex information:

Learn how I build Knowledge Graphs – here is my webinar tutorial on using them to empower LLMs, as well as LLM-powered AI agents:

3. Defining Agent functions

Now let’s define the agent functions. These functions will be invoked using the agents we’ll define in a subsequent step.

They’ll be responsible for calling the previously defined tools in the background and executing the necessary actions like data retrieval or content generation:

4. Implement Agents

At the heart of SWARM’s orchestration layer are the specialized agents, each designed to handle specific tasks within the system.

In our telco example, we defined several key agents:

1. The Triage Agent which acts as the initial point of contact and directs queries to appropriate specialists;
2. The OLT Agent responsible for handling Optical Line Terminal data;
3. The CRM Agent which retrieves and manages customer information;
4. The ACS Agent dealing with Auto Configuration Server data;
5. The Solution Agent which integrates with a knowledge graph to solve complex customer issues.

By clearly defining each agent’s role and capabilities, we create a modular and efficient system that can handle a wide range of customer queries and technical tasks:

5. Define Handoff Logic

SWARM’s power lies in its ability to transfer control between agents seamlessly. We implemented this through simple transfer functions like transfer_to_olt() and transfer_to_crm().

These functions enable dynamic routing of queries, allowing agentic AI systems to hand off tasks when they encounter questions outside their expertise. For example, the CRM agent can pass network status queries to the OLT agent.

This mechanism ensures that complex inquiries are addressed by the most appropriate specialists, resulting in comprehensive and accurate responses to user queries:

6. Let’s EXECUTE!!!

Using run_demo_loop() will execute an infinite while loop for the agent to run and do its amazing job:

Results

This implementation perfectly illustrates the orchestration layer’s capabilities. The system demonstrates its ability to:

1. Identify and activate the most suitable agent for each query
2. Determine and call appropriate functions or tools in the background
3. Retrieve relevant information from various data sources
4. Maintain context throughout the conversation
5. Seamlessly assist the user with their query

For instance, when asked about customer data, the system activates the CRM agent, retrieving detailed information about the customer.

It then seamlessly transitions to providing ACS data when requested, maintaining the context of the previous interaction.

The system’s ability to understand context is further demonstrated when it correctly interprets “he” as referring to the previously mentioned customer, David.

The SWARM framework offers a powerful and flexible approach to building AI orchestration layers.

Conclusion

By breaking down complex tasks into specialized agents and managing their interactions, you can create sophisticated AI systems capable of handling diverse queries and scenarios.

As demonstrated in our telco example, SWARM can be effectively applied to real-world use cases, providing a robust foundation for building intelligent, multi-agentic systems.

The complete code is available here:

https://github.com/goodrahstar/orchestration_layer/blob/main/Orchestration%20layer%20using%20SWARM.ipynb

About us: Neurons Lab

Neurons Lab delivers AI transformation services to guide enterprises into the new era of AI. Our approach covers the complete AI spectrum, combining leadership alignment with technology integration to deliver measurable outcomes.

As an AWS Advanced Partner and GenAI competency holder, we have successfully delivered tailored AI solutions to over 100 clients, including Fortune 500 companies and governmental organizations.