At Career Agents, our primary challenge is helping developers navigate their careers with maximum coverage of various domains—ranging from FAANG companies to cybersecurity start-ups. In total, the system maintains **167 specialized AI agents**, each calibrated for highly target-specific prompt tasks.
The Problem: Single-Agent Coherence Bottlenecks Traditional chat agents suffer from prompt pollution when tasked with diverse operations simultaneously. An agent designed to check resume compliance under the ATS standards should not worry about how to structure a response for a Google behavioral mock interview.
The Solution: Multi-Agent Hub and Spoke Architecture We built a centralized brain router that processes instructions in two distinct phases:
1. **Intent Classification & Extraction**: When you submit a request, the router maps your text vectors against the registry map to extract active target parameters. 2. **Context Routing**: The router delegates execution to a specialized subgroup (e.g. Resume Studio, Interview Lab, or FAANG Tracker) dynamically.
By limiting each sub-agent's context window only to its micro-registry definition and task rules, we maintain near-zero prompt hallucination rates and slash API response latency by **45%** on average.