A Framework to Implementing Multi-Agent Agentic AI

Prepare your enterprise to invest in multi-agent agentic AI systems.

April 9, 2025 | By Ramesh Koovelimadhom

Multi-agent agentic AI systems represent a significant advancement in AI, enabling complex task management through collaborative autonomous agents.

While enterprises require a custom approach to development and deployment (or in other words, there’s no one-size-fits-all approach), the Google Cloud Platform (GCP) offers robust tools for efficient and successful multi-agent system implementation.

In this article, we’ll outline the framework for technical insights and solutions when implementing multi-agent agentic AI.

Understanding Multiple Agent Systems in Agentic AI

Agentic AI refers to autonomous systems capable of planning, reasoning and executing tasks to achieve specific goals.

Multi-agent systems extend this concept by orchestrating multiple specialized agents working collaboratively. These multi-agent AI systems provide incredible flexibility compared with old-school hard coding.

Core Components and Technical Challenges

A typical multi-agent system architecture consists of five key elements:

1. Specialized agents: individual AI agents with specific functions and capabilities
2. Shared memory: a repository for communication and knowledge sharing between agents
3. Orchestration layer: coordinates agent activities and workflow
4. Data storage and retrieval layer: manages information access and storage
5. Service layer: delivers AI capabilities across platforms

With any new technology or system, there are technical challenges to consider. Here are five common challenges enterprises should solve before implementation.

Agent Communication and Orchestration

One of the most complex components involves creating effective communication channels between agents. The GCP Community forum highlights a specific technical issue where developers struggle with multidirectional communication: “I can route from A to B, but then B cannot go back to A after it has completed.”

This particularly affects workflows that require bidirectional communication between parent and child agents. Addressing “loop detected” errors ensures the system’s ability to implement communication patterns successfully.

Complexity in Agent Workflow Design

Designing effective multi-agent workflows also presents considerable challenges that require consideration.

The loop-back example described above is a limitation that constrains the design of sophisticated multi-agent architectures, where agents need to report back to orchestrating agents.

This situation emphasizes the importance of strategizing and clearly planning out workflows before implementation.

Scalability and Performance Bottlenecks

As multi-agent systems grow in complexity, scalability becomes a critical concern. While individual agents might operate efficiently, orchestrating numerous agents across an enterprise introduces significant performance challenges.

Traditional approaches to scaling AI often rely on cloud-based processing power, but this model may be shifting. Public cloud providers have built their business models around offering scalable computing, massive storage and centralized data processing—services agentic AI systems need far less of.

Interoperability Challenges

Multiple agent systems frequently interact with diverse data sources, APIs and other systems.

GCP’s Codelabs demonstrate approaches to building tools that connect agents to databases and other services, so implementing these connections must be diligently planned.

Data Privacy and Security

Agentic AI systems often require access to sensitive data, raising significant security and compliance concerns.

As agents interact with databases, APIs and user information, security becomes more complex, and it becomes even more critical to maintain appropriate security controls.

Solutions and Best Practices for Multi-Agent System Implementation

To address communication challenges between agents, developers are testing several approaches:

1. Status indicators: implementing state management to track agent progress and facilitate communication
2. Event-driven architecture: using events to trigger agent responses rather than direct agent-to-agent calls
3. Shared memory patterns: creating centralized knowledge repositories accessible to all agents

Google Cloud offers specific tools to address multi-agent challenges:

1. Vertex AI Agent Builder: enables no-code creation of conversational AI agents, allowing easy orchestration of multiple agents
2. Vertex AI Agent Engine: provides powerful orchestration and customization capabilities for complex agent systems
3. Cloud Run functions: offers serverless infrastructure for deploying individual agents

Google AI has introduced PlanGEN, a specialized multi-agent framework featuring three collaborative agents:

1. Constraint Agent: identifies problem-specific information
2. Verification Agent: assesses the quality of proposed plans
3. Selection Agent: determines the most suitable inference algorithm based on problem complexity

Addressing Emerging Challenges

As agentic AI evolves, there’s a trend leaning toward reduced dependency on cloud resources. Organizations are exploring edge deployment models where agents can operate with minimal cloud connectivity, potentially reducing costs and latency while improving privacy.

To address performance and resource utilization challenges, hybrid approaches combining different AI technologies are emerging:

1. Integration with reinforcement learning: enhancing agent autonomy through trial-and-error learning
2. Graph neural networks: enabling complex relationship modeling and real-time decision-making
3. Neuro-symbolic AI: combining neural pattern recognition with symbolic reasoning for explainable decisions

Questions for Enterprise Discussions on GCP Multi-Agent AI Solutions

As enterprises consider these new approaches, it’s important to ask the right questions.

Here are 10 questions to answer before implementing multi-agent systems to determine your enterprises’ overall strategy and preparation:

1. What specific business processes would benefit most from multi-agent automation?
2. How will you measure ROI for your multi-agent system implementation?
3. What is your strategy for scaling from pilot to enterprise-wide deployment?
4. Will your architecture leverage specialized or more generalized agents for different tasks?
5. What data sources will your agents need to access, and how will you secure those connections?
6. How will your multi-agent system integrate with existing enterprise applications?
7. What approach will you take to ensure data privacy compliance across your agent ecosystem? (And how will you handle sensitive data that agents may process during operations?)
8. How will you monitor agent performance and identify bottlenecks in complex workflows?
9. What guardrails will you implement to prevent misuse of your AI agents?
10. How will you monitor and debug complex multi-agent interactions when issues arise?

Embracing Multi-Agent Agentic AI

While implementing multi-agent agentic AI solutions on Google Cloud is complex, with the right planning and the right implementation process, your enterprise can reap the benefits of advanced AI automation.

The core considerations outlined in this article will help you develop effective strategies for agent communication, orchestration, scalability and security.

As Google continues to develop specialized tools like Vertex AI Agent Builder and frameworks like PlanGEN, these solutions will become increasingly accessible to enterprises. The key to successful implementation? Careful planning, the right expertise and a proven partner.