--- type: "LandingPage" title: "Multi-Agent Systems: AI Agents as a Team" description: "Multi-agent systems: specialised AI agents that solve complex tasks as a team, with orchestration, shared memory, MCP tools and human-in-the-loop approvals." resource: "https://www.contextstudios.ai/multi-agent-systems" language: "en" tags: ["multi-agent systems", "multi-agent AI", "AI agent teams", "agent orchestration", "LangGraph", "CrewAI", "agentic workflow", "autonomous agents", "human-in-the-loop", "multi-agent framework"] generated: by: "process:contextstudios-md/1" at: "2026-10-08T19:44:34.558Z" status: "stable" --- # Multi-Agent Systems: AI Agents as a Team Multi-agent systems distribute complex tasks across specialised AI agents that work together like a team: researching, analysing, writing and checking each other's work. Context Studios, an AI-native development studio in Berlin, builds such agent teams with orchestration, tool access via MCP and human approval at critical points. Multi-agent systems consist of several specialised AI agents that divide a task among themselves, communicate with each other and coordinate their results. Each agent has a role, its own tools and knowledge sources; an orchestrator or supervisor controls the flow and involves people in important decisions. Entity: Multi-Agent Systems Specialisation: Agent orchestration, tool chains, communication protocols Technologies: LangGraph, CrewAI, AutoGen, Claude Agent SDK, MCP Target group: Companies with complex, multi-step automation needs Project duration: Typically 6–16 weeks depending on agent complexity Compliance: GDPR, EU AI Act, human-in-the-loop, audit trails ## What makes up a multi-agent system? Specialised agents working as a team, for tasks that overwhelm a single model ### Agent roles & specialisation We design agents with clear roles, such as research, analysis, writing, review or coding, each optimised for its field and using its own tools and knowledge sources. ### Communication & coordination Efficient communication patterns between agents: structured message passing, shared memory and supervisor-driven workflows with clear rules for resolving conflicts. ### Orchestration strategies Supervisor-worker patterns for controlled processes, peer-to-peer for creative tasks or hierarchical structures for enterprise processes, depending on the requirements. ### Tool chain integration Each agent gets access to suitable tools such as web search, databases, code execution or email, standardised via the Model Context Protocol (MCP). ### Shared memory Persistent knowledge stores with vector databases for semantic search and knowledge graphs for relationships are available to all agents. ### Security & human-in-the-loop Approval levels for critical actions, automatic escalation to the people responsible and complete audit trails for every step, for safe automation in the enterprise. ## How is an agent team built? ### Initial call A free 30-minute video call with Michael Kerkhoff. We get to know your project, assess where AI adds value and give you a first estimate of feasibility, effort and timeframe. ### Proposal & planning A detailed feature breakdown, a technical architecture plan and a written proposal covering scope, schedule and a fixed price. ### AI-accelerated development Agile development with weekly demos and production-ready code backed by automated tests. Goal: a working MVP in about 4 weeks. ### Launch & operation Production deployment with complete documentation and handover. 30 days of free bug fixing from final delivery; maintenance and further development by agreement. ## Frequently asked questions about AI agent teams Q: What is the advantage over a single AI agent? A: Single agents reach their limits on complex, multi-step tasks, in terms of context window, specialisation and reliability. An agent team spreads the work across specialists who work in parallel and check each other. For complex tasks this usually leads to better and more traceable results. Q: How do the agents communicate with each other? A: Depending on the task, via structured message passing for direct assignments, shared memory based on vector databases, event-driven communication via message queues, or supervisor-driven workflows for controlled processes. The Model Context Protocol standardises the use of tools and data sources. Q: How much does it cost to develop a multi-agent system? A: Costs depend on the number and complexity of agents, the connected systems, requirements for memory and approvals and the testing effort; a team of a few agents for a clearly defined process is much smaller than an organisation-wide platform. Fixed price after scoping, proposal within 48 hours. Q: How do you prevent agents from getting stuck in endless loops? A: With several safeguards: maximum iterations per workflow, deadlock detection with automatic termination, budget limits for token usage and time limits as circuit breakers. A supervisor agent monitors the overall flow and escalates anomalies to a person instead of carrying on indefinitely. Q: Can people intervene in the agent workflow? A: Yes, human-in-the-loop is a core feature. You define which actions run automatically and which require approval, such as sending emails, approving contracts or budget decisions. The workflow then pauses, notifies the person responsible by email or chat and continues automatically after the decision. Q: Which frameworks do you use? A: Mainly LangGraph for stateful agent workflows and CrewAI for role-based teams. In Microsoft environments we use Semantic Kernel and AutoGen, and for Claude-based agent teams the Claude Agent SDK with native MCP support. The choice depends on your infrastructure and use case. Q: How do agent systems scale as load increases? A: Agents run as independent services that can be scaled horizontally. Message queues decouple communication, and container orchestration with Kubernetes adjusts capacity automatically to the load. That way many workflows can run in parallel without individual agents becoming a bottleneck for the whole system. Q: Can different LLMs be used for different agents? A: Yes, that often makes sense: an analysis agent might use Claude for thorough reasoning, a code agent GPT for fast generation and a summarisation agent a smaller model such as Mistral for cost-efficient text processing. This diversity optimises quality and cost at the same time and reduces dependence on one provider. Q: How do we test a multi-agent system? A: On three levels: unit tests for each agent, integration tests for communication between agents and end-to-end tests for complete workflows. In addition, chaos tests deliberately provoke errors in individual agents to check the robustness of the overall system before it goes into production. Q: How do multi-agent systems differ from classic workflow automation? A: Classic automation follows fixed rules and if-then logic. Agent teams make their own decisions within defined limits, react to unforeseen situations and improve through feedback. They are particularly suited to tasks that require judgement, understanding of context and bringing together many sources. ## Ready for intelligent agent teams? Talk to us for 30 minutes about the process an agent team should take over for you. ## Technology stack for agent teams ## Use cases by industry ## Agent teams: example projects Examples we can build for you ## Agent teams: consulting in Berlin