Role-Based Agent Delegation

Assemble agents with distinct roles, goals, and tools and delegate work between them like a team, following the AutoGen and CrewAI frameworks.

An emerging pattern for multi-agent architecture: define agents with specific roles and delegate work to them like a team assigned to a goal, rather than one agent doing everything.

AutoGen's simplest usage has two roles: an Assistant — a standard conversational LLM agent with background tool use — and a UserProxy, which stands in for the human user. The UserProxy is system-messaged to work with the Assistant toward the human's stated goal, acting as a corrective force (keeping the Assistant on track, offering recommendations) and eventually declaring the goal accomplished. An Assistant–UserProxy pair is itself a small LLM-based workflow. AutoGen also offers a group chat manager: a workflow coordinator holding several role-specific conversational agents (each with its own system message and tools) that delegates incoming requests to whichever agent it judges appropriate — a role-labeled instance of LLM-driven workflow routing.

CrewAI fills a similar niche: assemble "crews" of agents, each with its own role, goal, backstory, and tool subset from the shared tool inventory, given tasks to resolve toward an overall goal. Agents can be arranged into processes:

  • Sequential — like a pipeline.
  • Hierarchical — a director agent routes work, similar to AutoGen's group chat manager.
  • Consensual — agents collaborate to determine how work gets done.

Both frameworks trade the predictability of a fixed pipeline for delegation flexibility, so the same simpler-is-better guidance applies: prefer role delegation only where a fixed AI pipeline orchestration genuinely cannot express the needed coordination.

The Assistant/UserProxy pair also shows up as the last resort on the task quality escalation ladder: for a complex, open-ended workflow task that resists being pinned down as a single templated prompt, an expert conversational agent equipped with the needed tools, paired with a user-proxy agent pushing it toward the goal, can succeed where a single-shot prompt cannot — an expert agent alone often won't act without a conversational partner. Treat this as experimental relative to the more deterministic steps earlier on that ladder.

Sources

  • Prompt Engineering for LLMs (John Berryman), ch. 9