Multi-Agent System

Multi-agent systems (MAS) are architectures in which multiple specialized AI agents work together in a coordinated manner to solve complex tasks. Instead of a single agent handling everything, specialized agents divide the work: a frontend agent, a backend agent, a testing agent, a security agent. An orchestrator coordinates the entire process and delegates subtasks to the appropriate specialists. ### Why Multi-Agent Systems? The importance of multi-agent systems is growing rapidly. Industry analysts report strong interest in multi-agent systems in recent years. AI agents are expected to gain increasing importance in enterprise applications. The reasons lie in the limitations of single agents: - Individual agents hit context limits with complex, extensive tasks - Specialization increases quality: a frontend agent knows Vue or React far better than a generalist - Parallelization significantly accelerates development because multiple agents can simultaneously work on different subproblems - Error checking by independent agents increases the reliability of the overall result ### Architecture of a Multi-Agent System A typical multi-agent system consists of several coordinated layers: **Orchestrator Agent:** Receives the original task, analyzes it, breaks it into subtasks, and delegates these to the responsible sub-agents. The orchestrator knows the capabilities of all sub-agents and manages dependencies between tasks. **Specialized Sub-Agents:** Each agent takes on a clearly defined area of responsibility, such as frontend implementation, backend logic, testing, security analysis, or DevOps configuration. **Communication Protocol:** Structured messages between agents ensure that results and context are passed on correctly. Without defined contracts, inconsistencies arise. **Shared Memory:** Common files, databases, or memory files allow agents to access the work results of other agents without passing the entire context. **Feedback Loops:** Agents can review and validate the results of other agents before they flow into the overall result. ### Multi-Agent Systems in Software Development In modern software development, multi-agent systems are increasingly used for demanding development tasks: **Claude Code with Sub-Agents:** Claude Code supports native task delegation to specialized sub-agents that work in parallel or sequentially on subtasks. **Parallel Feature Development:** Frontend and backend agents work simultaneously on a feature, coordinated through shared interface contracts. **Automated Quality Assurance:** A security agent reviews the implementation agent's code for vulnerabilities without requiring a human to manually trigger this step. **Integration Validation:** After parallel development by multiple agents, an integration agent checks the consistency of the results. **Example Workflow:** 1. Issue or requirement arrives at the orchestrator 2. Orchestrator analyzes and delegates in parallel to frontend agent and backend agent 3. Both agents return results 4. Integration agent checks consistency 5. Testing agent runs automated tests 6. Final report goes back to the orchestrator ### Challenges Multi-agent systems are not a universal solution. With their complexity, the requirements also increase: - Coordination overhead grows with the number of agents involved - Errors can propagate across agent boundaries if contracts are not cleanly defined - Context sharing between agents requires careful architectural decisions - Costs increase proportionally with the number of parallel LLM calls - Debugging becomes more complex because error causes can lie in different agents A clean architecture with clearly defined contracts between agents is therefore crucial for project success. ### elasticbrains and Multi-Agent Systems At elasticbrains, we have been using multi-agent architectures in production since 2024 - embedded in over 100 delivered digital projects and 5,000+ person-days of experience since August 2015. This practical foundation underpins our approach to AI-driven development. Specifically, we work with: **Specialized Agent Teams:** Frontend, backend, testing, and security agents work in parallel on development tasks, coordinated by an orchestrator. **Orchestrator Patterns with Claude Code Sub-Agents:** We leverage the native sub-agent capabilities of Claude Code and extend them through structured task delegation. **Agent Packages:** Structured task delegation with defined contracts between agents ensures that parallel work remains consistent and does not lead to inconsistencies. **Enterprise AI Platforms:** Coordinated services such as speech recognition, NER analysis, and PII detection work together as a multi-agent system, with each service taking on a specialized role. ### Agentic Coding Workshop Multi-agent setups are a central topic in the [Agentic Coding Workshop](/workshops/agentic-coding). In the workshop, developers and technical teams learn how to set up orchestrator patterns, define agent contracts, and build parallel development workflows with Claude Code sub-agents.