Multi-Agent Systems Where Specialised Agents Collaborate

A single agent starts to strain when a task requires genuinely different skills at different stages, for example one step needs deep web research while another needs structured data extraction and a third needs synthesis into a client-ready format. Splitting these into specialised sub-agents under an orchestrator, rather than one agent trying to do everything, generally produces more reliable results and is easier to debug when something goes wrong.

Through an orchestration layer that decides when to hand off to which sub-agent, how to pass context between them, and how to handle errors when a sub-agent fails or returns low-confidence output. This orchestration logic is where most of the engineering effort in a multi-agent system lives, more than in any individual sub-agent's prompt.

Research pipelines that pull from multiple data sources and synthesise a brief, competitive intelligence gathering, due diligence workflows, and data enrichment at scale. These all share a pattern: multiple distinct sub-tasks that each benefit from a specialised agent, combined into a single coherent output by an orchestrator.

Complex multi-agent systems with custom RAG, fine-tuning, and production infrastructure typically take 10 to 16 weeks. We always start with a 2-week discovery sprint to map the sub-tasks, define the orchestration logic, and ship a working proof-of-concept for the core hand-off pattern before building the full system.