I’m trying to figure out the best way to use Claude Code, Claude models and API tokens to build a multi-agent system that can manage different functions of a company. The idea is to move beyond using AI agents for individual tasks and build something closer to an actual AI workforce, where different agents have dedicated roles, responsibilities, memory, tools and the ability to collaborate with each other. For example, imagine having: CEO Agent: Oversees the entire operation, sets priorities, delegates work and monitors progress. Marketing Agent: Handles content strategy, campaigns, research and distribution. Product Agent: Manages product development, feature planning and roadmaps. Development Agents: Work on coding, debugging, testing and deployments. QA Agent: Independently tests deliverables, identifies issues and sends them back for corrections. Documentation Agent: Maintains documentation, knowledge bases and internal processes. Operations Agent: Tracks ongoing work, dependencies, reports and team coordination. Ideally, these agents would operate with their own context, memory and responsibilities while communicating through a central orchestration layer. I’m particularly interested in using Claude Code as the primary coding and execution environment, but I’m still trying to understand the most efficient architecture for this. A few things I’d love to get advice on: Token economics: How are you managing Claude token consumption when running multiple agents? Is it practical to use Opus for complex reasoning, Sonnet for execution and Haiku for repetitive tasks? Agent orchestration: Is Claude Code with subagents sufficient, or should I look at something like OpenClaw, Paperclip, or another orchestration framework? Memory and context: What’s the best approach to persistent memory across agents? Should each agent maintain its own memory, or should there be a shared company-wide knowledge base? Long-running workflows: How do you handle agents that need to work continuously, monitor tasks, pick up unfinished work and coordinate with other agents? Cost optimisation: How do you prevent multiple agents from repeatedly reading the same files, consuming unnecessary context and burning through tokens? Human approvals: How do you structure approval workflows so agents can independently research, plan, code and test, while requiring human approval before important external or production actions? Real-world experience: Has anyone actually built something like an autonomous AI workforce that can handle meaningful business operations, rather than just running a few independent coding agents? I’m not necessarily looking for another framework recommendation. I’d love to hear from people who have experimented with this at scale and understand what works, what doesn’t, and where the actual bottlenecks are. My end goal: Build a relatively lean AI-powered organisation where a small number of humans can oversee multiple specialised agents working together, with clear accountability, shared knowledge and controlled token consumption. Would love to hear about your architectures, setups, token budgets and lessons from real-world implementations. I have tried Paperclip, OpenBot submitted by /u/AdityaR_Sharma
Originally posted by u/AdityaR_Sharma on r/ClaudeCode