CANVAS

RagaAI Canvas Deploy & Manage Multi-Agent Systems

The future of AI is collaborative. Multi-Agent Systems (MAS) are revolutionizing how we build intelligent applications by orchestrating a team of specialized AI agents to work together.

Benefits of multi-agent systems

Here's why Multi-Agent Systems are a game-changer.

Scalability via Collaboration

Imagine tackling a massive project by dividing it into smaller, manageable tasks. That's the power of MAS. Multiple agents, each with a specialized skill set (e.g., data extraction, analysis, or response generation), can work in parallel to achieve a larger goal. This distributed approach enables systems to handle increasing complexity and workloads with ease.

Diagram of an agent orchestrator connecting user input to multiple agents with shared memory, tools, and LLMs, showing how collaboration enables scalability.

Performance dashboard for MAS chatbot showing graph of success rates and pie chart of errors versus successful responses, illustrating quality gains from specialized agents.

Quality via Specialization

Instead of relying on a single, general-purpose AI, MAS allows you to leverage "expert agents" for specific tasks. This specialization minimizes errors and significantly boosts the quality of results, outperforming single-model approaches. Think of it as having a team of highly skilled specialists instead of a generalist.

Resilience via Fallbacks

Things don't always go as planned. With MAS, if one agent fails or provides incorrect information, the system can dynamically reroute the task to another agent or implement a fallback strategy. This built-in redundancy ensures system reliability and robustness.

Agent orchestration diagram showing fallback routing from a failed agent to backup agents, ensuring reliability and robustness in AI workflows.

Illustration of modular AI agents represented as icons in a sandboxed system, highlighting flexibility to update or replace components without disrupting the system.

Adaptive via Modularity

MAS offers unparalleled flexibility. Individual agents are "sandboxed," making it easier to update or replace them without disrupting the entire system. This modularity simplifies maintenance, promotes innovation, and allows your AI to adapt quickly to evolving needs.

Example of a Multi-Agent System

Patient Concierge

Consider a "concierge" AI in a hospital setting. This friendly AI interacts with patients and delegates tasks to a team of specialized helper agents:

One agent checks symptoms.
Another agent books doctor's appointments.
A third agent answers insurance queries.

These helper agents seamlessly connect with the hospital's calendar, patient records, and insurance systems, enabling instant task completion and a smooth, efficient experience.

Consider a "concierge" AI in a hospital setting. This friendly AI interacts with patients and delegates tasks to a team of specialized helper agents:

One agent checks symptoms.
Another agent books doctor's appointments.
A third agent answers insurance queries.

These helper agents seamlessly connect with the hospital's calendar, patient records, and insurance systems, enabling instant task completion and a smooth, efficient experience.

Consider a "concierge" AI in a hospital setting. This friendly AI interacts with patients and delegates tasks to a team of specialized helper agents:

One agent checks symptoms.
Another agent books doctor's appointments.
A third agent answers insurance queries.

These helper agents seamlessly connect with the hospital's calendar, patient records, and insurance systems, enabling instant task completion and a smooth, efficient experience.

Designing Multi-Agent Systems

Building effective Multi-Agent Systems involves a comprehensive lifecycle:

Build

Choose a suitable framework and design your agent pipeline.

Build

Choose a suitable framework and design your agent pipeline.

Build

Choose a suitable framework and design your agent pipeline.

Evaluate

Rigorously test for potential issues such as goal misalignments, planning failures, tool integration errors, and PII leakages.

Evaluate

Rigorously test for potential issues such as goal misalignments, planning failures, tool integration errors, and PII leakages.

Evaluate

Rigorously test for potential issues such as goal misalignments, planning failures, tool integration errors, and PII leakages.

Deploy & Manage

Implement robust versioning and rollback mechanisms, ensure scalability to handle increasing volumes, establish state persistence, and manage authentication.

Deploy & Manage

Implement robust versioning and rollback mechanisms, ensure scalability to handle increasing volumes, establish state persistence, and manage authentication.

Deploy & Manage

Implement robust versioning and rollback mechanisms, ensure scalability to handle increasing volumes, establish state persistence, and manage authentication.

Monitor

Implement real-time safety checks, create status dashboards, and set up alerts and drill-downs. Also, ensure stringent authentication management.

Monitor

Implement real-time safety checks, create status dashboards, and set up alerts and drill-downs. Also, ensure stringent authentication management.

Monitor

Implement real-time safety checks, create status dashboards, and set up alerts and drill-downs. Also, ensure stringent authentication management.

Deploying agents through RagaAI Canvas

RagaAI Catalyst is a sophisticated platform optimized for AI observability, monitoring and evaluation, improving your development journey.

Workflow diagram showing RagaAI Canvas flow from code to container to cluster deployment with Git-driven CI/CD, Catalyst observability, and integration with AWS, Azure, and Google Cloud.

Code → Container → Cluster – We wrap Canvas apps in OCI images, then ship them as Cloud SaaS, self‑hosted data plane, or standalone Docker

Git‑Driven CI/CD – Pull‑requests trigger automated evaluations, quick rollbacks

Observability Baked‑In – Every run is auto‑traced to Catalyst; logs, metrics, and spans feed custom dashboards and agent graphs

Security & Residency Control – Choose fully‑managed SaaS or deploy inside your VPC

Introducing Canvas

Canvas simplifies the complexities of building and managing Multi-Agent Systems. Canvas provides state-of-the-art lifecycle management with:

Version management and safe rollbacks to previous builds.

Persistent memory to remember knowledge, secrets, and preferences.

Sandboxed testing environments and canary deployments.

Scalability to handle increasing traffic.

Get Started

Join 5,000+ companies growing with RagaAI

Evaluate all stages of Agentic AI workflows and deploy with confidence.

Build Better Agents

Get Started

Join 5,000+ companies growing with RagaAI

Evaluate all stages of Agentic AI workflows and deploy with confidence.

Build Better Agents

Get Started

Join 5,000+ companies growing with RagaAI

Evaluate all stages of Agentic AI workflows and deploy with confidence.

Build Better Agents