Dynamic Expert Recruitment

Agentic AI Multi-Agent

A recruiter agent generates the team composition at runtime based on the specific task — and adjusts that composition between iterations based on evaluation feedback.

Dynamic Expert Recruitment replaces hard-coded role lists with a runtime recruiter: a meta-agent receives the goal, generates role descriptions matched to that specific task, instantiates the team and execution plan, runs the task, evaluates progress, and adjusts team composition (add, remove, replace roles) before the next iteration — so the team always matches the task rather than the task being squeezed into a fixed crew.

Intent & Description

🎯 Intent

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

📋 Context

A multi-agent platform accepts a wide range of tasks through one entry point — drafting a regulatory filing, refactoring a Python module, planning a marketing campaign. The right team of specialists varies sharply from one task to the next. The platform cannot know the task type in advance and cannot afford to keep one large fixed crew always running.

💡 Solution

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

Real-world Use Case

Hard-coded role lists are brittle because the right team varies wildly across task types.
A recruiter agent can generate role descriptions and instantiate the team from the goal.
Evaluation feedback can drive team composition adjustments between iterations.

Source

View Original Source →

📌 TL;DR

Generate the team from the task at runtime, evaluate, adjust, repeat — the recruiter is the only agent that mutates team composition, and it does so based on evidence.

Advantages

Team matches the task instead of the task being forced into a fixed team.
Adaptive composition closes the gap as the task evolves mid-execution.
The recruiter prompt is the only place the meta-policy lives — one place to audit and improve.

Disadvantages

Recruiter quality is the bottleneck — a bad recruiter produces bad teams every time.
Runtime team generation is non-deterministic; reproducibility and debugging suffer.
Adjustment between iterations can churn — replacing roles too aggressively wastes work.

113 of 322