When should I use a multi-agent system instead of a single agent?

Multi-agent systems shine when your workflow requires diverse expertise that is hard to encode in a single prompt — for example, a process that needs research, analysis, writing, and review. They are also valuable when tasks can be parallelized (multiple agents working simultaneously), when you need clear separation of concerns for maintainability, or when different steps require different LLMs or tools. For simple, linear workflows, a single agent is usually simpler and more cost-effective.

What is the difference between supervisor and crew patterns?

In the supervisor pattern, a central orchestrator agent receives tasks, decomposes them, delegates to worker agents, and synthesizes results. It provides centralized control and is easier to debug. In the crew pattern (as in CrewAI), agents have defined roles and collaborate more autonomously — passing work between each other based on their role definitions. Crews are more flexible but harder to control. We recommend supervisor patterns for structured workflows and crew patterns for creative, exploratory tasks.

How much do multi-agent systems cost compared to single agents?

Multi-agent systems have higher LLM costs because multiple agents make separate API calls. A 4-agent system might cost 3-5x more per task execution than a single agent. However, the total cost of ownership is often lower because multi-agent systems complete complex tasks that would otherwise require human labor. We optimize costs through intelligent model routing (expensive models for critical agents, cheap models for simple ones) and caching shared context.

How do you debug issues in multi-agent systems?

We implement comprehensive observability from day one. Every agent call, tool execution, and inter-agent message is traced and logged via LangSmith or custom monitoring. We build visualization dashboards that show the full execution graph — which agent was called, what it decided, and what it returned. When failures occur, you can replay the exact execution path and identify the specific agent and step that caused the issue.

How do multi-agent systems scale?

Multi-agent systems scale both vertically (adding more capable models to existing agents) and horizontally (adding more specialized agents for new capabilities). We architect systems with modular agent definitions, so adding a new specialist is straightforward. For high-throughput scenarios, we implement parallel agent execution, queue-based task distribution, and auto-scaling of agent instances based on load.

Should I use LangGraph or CrewAI for my multi-agent system?

LangGraph is our default recommendation for production systems — it provides fine-grained control over agent state, supports conditional branching, checkpointing, and human-in-the-loop workflows. CrewAI is excellent for rapid prototyping and role-based team setups where agents have clear, predefined roles. For complex enterprise workflows with strict reliability requirements, LangGraph's graph-based orchestration gives you the control and observability you need.

What happens when one agent in the system fails?

We implement multiple failure handling strategies: per-agent retry with exponential backoff, fallback agents that activate when primary agents fail, checkpoint-based recovery that resumes from the last successful state, graceful degradation that completes partial workflows, and dead letter queues for tasks that cannot be completed. Critical paths have redundant agents, and the supervisor is always notified of failures for intelligent re-routing.

Can agents in the system use different LLMs?

Absolutely, and this is one of the key advantages of multi-agent architecture. A research agent might use GPT-4o for complex reasoning, a classification agent might use a fine-tuned Llama model for speed and cost, and a writing agent might use Claude for nuanced content generation. Each agent gets the optimal model for its specific task, optimizing both performance and cost across the system.

Multi-Agent System

Multi-Agent System Development Services

Build orchestrated multi-agent systems where specialized AI agents collaborate, delegate, and communicate to solve complex workflows. We design supervisor patterns, agent-to-agent protocols, shared memory, and failure recovery — turning single agents into coordinated AI teams.

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

Multi-Agent System Capabilities

Orchestrate teams of specialized AI agents that collaborate on complex workflows

Supervisor & Delegation Patterns

Design supervisor agents that analyze incoming tasks, decompose them into subtasks, and delegate to specialized worker agents. The supervisor monitors progress, handles failures, and synthesizes results — enabling complex multi-step workflows that no single agent could handle.

Agent-to-Agent Communication

Build structured communication protocols between agents using message passing, shared blackboards, or event-driven architectures. Agents can request help, share findings, ask clarifying questions, and coordinate their work through well-defined interfaces.

Shared Memory & Context

Implement shared knowledge stores that all agents can read and write to — including conversation history, task state, retrieved documents, and intermediate results. Agents maintain both private working memory and shared team context for coherent collaboration.

LangGraph & CrewAI Orchestration

Build multi-agent systems using production-grade frameworks. LangGraph provides graph-based orchestration with checkpointing and state management. CrewAI offers role-based agent teams with built-in delegation. We select the right framework for your complexity level.

Tool Sharing & Specialization

Assign specialized toolkits to each agent — a research agent with web search tools, a data agent with SQL tools, a writing agent with formatting tools. Agents use their specialized capabilities and share results, creating a team of experts rather than one generalist.

Failure Handling & Retry Logic

Production multi-agent systems need robust error recovery. We implement per-agent retry policies, fallback agents for critical paths, checkpoint-based recovery, timeout handling, and dead letter queues for failed tasks — ensuring your system degrades gracefully instead of failing completely.

Our Multi-Agent System Development Process

A proven 5-step process from workflow mapping to production multi-agent deployment

Discovery & Architecture

We map your complex workflows, identify where multiple specialized agents add value over a single agent, define agent roles and responsibilities, design the orchestration topology (supervisor, peer-to-peer, or hierarchical), and select the framework (LangGraph or CrewAI).

Prompt Engineering & Prototyping

Define system prompts for each agent role, design the communication protocol between agents, build tool interfaces for each specialist, and deliver a working multi-agent prototype you can test with real workflow scenarios.

Integration & Development

Build the full orchestration layer with state management, implement shared memory stores, connect each agent's specialized tools to your systems, add human-in-the-loop checkpoints for critical decisions, and build the monitoring infrastructure.

Testing & Evaluation

Test individual agents in isolation and the full system end-to-end. Evaluate task decomposition accuracy, delegation effectiveness, inter-agent communication quality, failure recovery, and overall workflow completion rates across diverse scenarios.

Deployment & Monitoring

Production deployment with per-agent dashboards, orchestration flow visualization, cost tracking by agent, latency monitoring, and task completion analytics. 30-day post-launch support with continuous optimization of agent coordination.

Multi-Agent System Pricing

Transparent pricing for multi-agent orchestration projects

Starter Agent

INR 50,000starting

Simple 2-3 agent system with basic orchestration

2-3 specialized agents
Basic supervisor pattern
Shared context via state
Single workflow automation
Basic error handling
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

"Edesy built a 6-agent system for our content pipeline — a research agent, writer agent, editor agent, SEO agent, image agent, and publisher agent. What used to take our team 4 hours per article now runs in 12 minutes with human review only at the final stage. Content output increased 8x."

Head of Content

Content Operations at Digital Media Company

"Our multi-agent system handles end-to-end deal analysis — a data extraction agent pulls financials, a market research agent gathers comps, an analysis agent builds models, and a report agent generates the investment memo. A deal that took 2 analysts 3 days now completes in 45 minutes."

Managing Director

Investment at Private Equity Firm

"The supervisor agent pattern was a game-changer for our customer operations. A routing agent classifies incoming requests and delegates to specialized agents for billing, technical support, account changes, and escalations. Each agent has its own tools and knowledge base. Resolution rate hit 91% without human intervention."

COO

Operations at Telecom Provider

Multi-Agent System

Multi-Agent System Development Services

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

Trusted by businesses worldwide

ShopifyAmazonStripeSlackNotionVercel

Multi-Agent System Capabilities

Orchestrate teams of specialized AI agents that collaborate on complex workflows

Supervisor & Delegation Patterns

Agent-to-Agent Communication

Shared Memory & Context

LangGraph & CrewAI Orchestration

Tool Sharing & Specialization

Failure Handling & Retry Logic

Max Agents Per System

96.8%

Task Completion Rate

<5s

Orchestration Latency

35+

Complex Workflows Automated

Our Multi-Agent System Development Process

A proven 5-step process from workflow mapping to production multi-agent deployment

Discovery & Architecture

Prompt Engineering & Prototyping

Integration & Development

Testing & Evaluation

Deployment & Monitoring

Multi-Agent System Pricing

Transparent pricing for multi-agent orchestration projects

Starter Agent

INR 50,000starting

Simple 2-3 agent system with basic orchestration

2-3 specialized agents
Basic supervisor pattern
Shared context via state
Single workflow automation
Basic error handling
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

Head of Content

Content Operations at Digital Media Company

Managing Director

Investment at Private Equity Firm

COO

Operations at Telecom Provider

Frequently Asked Questions

Explore More

Resources to help you evaluate and implement

OpenAI GPT Agent LangChain Agent RAG Agent Fine-Tuned LLM Agent

Ready to Build Your Multi-Agent System?

Get a free consultation and detailed project estimate. Our multi-agent specialists will help you design the right orchestration architecture for your complex workflows.

Schedule a Call