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Dynamic Expert Recruitment

aka Recruiter Agent, Run-Time Team Assembly, Adaptive Role Generation

category: multi-agent · status: experimental

Intent

Generate the agent team — role descriptions and instances — at run time based on the specific task, then adjust team composition between iterations based on evaluation feedback.

Context

The task arrives without a known type; a fixed pool of pre-assigned roles either over-provisions (expensive, noisy) or under-covers (missing the right specialist).

Problem

Hard-coded role lists are brittle: the right team for "draft a regulatory filing" is not the right team for "refactor a Python module"; both tasks share an entry point.

Forces

• Pre-specified roles are stable but mis-fit;
• Run-time generation costs an extra LLM call before any work begins;
• Adaptive composition risks instability: the team that solves step 1 may not solve step 5.

Solution

Add a recruiter agent (or a meta-agent committee: planner + agent observer + plan observer). Stage 1 — Drafting: recruiter receives the goal, generates role descriptions matched to that goal, instantiates the team and an execution plan. Stage 2 — Execution: the team works. Stage 3 — Evaluation: a reviewer scores progress; if unsatisfactory, the recruiter adjusts the team (add, remove, replace roles) and the next iteration runs. The recruiter is the only meta-agent that mutates team composition.

Structure

goal -> Recruiter -> [role descriptions] -> instantiated agents -> joint execution -> Evaluator -> feedback -> Recruiter (adjust team) -> ...

Constrains

Consequences

Benefits

• Team matches the task instead of the task being squeezed into a fixed team.
• Adaptive composition closes the gap as the task evolves.
• Recruiter prompt is the only place the meta-policy lives.

Liabilities

• Recruiter quality is the bottleneck; a bad recruiter produces bad teams.
• Run-time team generation is non-deterministic; reproducibility suffers.
• Adjustment between iterations can churn (replace too aggressively).

Known Uses

• AgentVerse — Recruiter agent generates expert descriptions per goal; team composition adjusted across iterations.
• AutoAgents — Drafting stage with three meta-agents (Planner, Agent Observer, Plan Observer) synthesises the team.

Related Patterns

• complements → supervisor
• generalises → role-assignment — Role assignment is the design-time special case.
• alternative-to → mixture-of-experts-routing — MoE routes to a fixed expert pool; this constructs the experts.
• complements → orchestrator-workers
• uses → evaluator-optimizer — Evaluation step drives team adjustment.

References

• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors — Chen et al. (2023) paper
• AutoAgents: A Framework for Automatic Agent Generation — Chen et al. (2023) paper