Multi Agentic AI Systems

A few weeks ago, I wrote about the growing relevance of Agentic AI. Let me add, the next big potential lies in the rise of multi-agentic AI systems. These systems are built on three fundamental elements: Agents, Tasks, and Crews. Together, these building blocks form the foundation of how multi-agentic AI automates processes and drives efficiency.

Multi-Agentic AI systems bring together multiple agents to complete the objective. It’s an agent team, performing, delegating, and collaborating on tasks like humans but at a scale and speed beyond human capability.

For e.g., traditionally, lead generation and prioritization involve manual data collection (or, to an extent, automated but within a specific scope) and analysis. Now, AI agents can automate this process together. AI agent gathers data from online sources and internal documents, compares details, scores the results, and categorizes leads into specific priorities.

To be effective, an agent must be good or utilized correctly in the below six areas (at least):
1. Specialization, i.e., defined role (e.g., data miner, research analyst, decision-making advisor, or automation orchestrator) to bring unique value
2. Right tools at both the agent and task levels
3. Task delegation to other agents for suited/specific operations
4. Smart Caching or cross-agentic caching. For example, if one agent uses a tool with some parameter and another agent uses the same tool with the same parameter, it can use a caching layer and have no unnecessary API calls
5. Collaborate seamlessly, sharing insights and results with other agents in real-time
6. Learn and adapt through memory, improving performance over time

Multi-agent AI systems can work in sequence, parallel, or hierarchically. Also, they can use various tools, from internet search web scraping to database connections, RAG searches, and others. The good part is that the tool can be assigned at the agent or task levels, but the task level tool can override the agent level tool, providing better control and flexibility.

Frameworks like CrewAI and others facilitate these multi-agent interactions by enabling dynamic integration of tools and even allowing the use or switch between different Large Language Models (LLMs) for various tasks through frameworks like LangChain to ensure that the system uses the most suitable LLM for each scenario. (m skipping the limitations).

What sets multi-agentic AI apart is its ability to scale and collaborate like human teams (faster and more efficiently). By distributing tasks and leveraging advanced tools, these systems can respond to dynamic environments with outstanding agility.

Agentic systems are continuously evolving. As I often say, technology evolves over time, along with how we use it – I encourage everyone to give it a try.