Episode 3 | The Hidden Cost of AI Agents (And How to Keep It in Check)

In this episode, Nick and Kat get into one of the most asked — and least understood — questions in recruitment right now: how much does running AI actually cost, and why does it keep going up?

Nick breaks down how AI agents actually work in plain English — think of an agent as a digital employee who shows up at a set time (literally triggered by a timer/alarm), picks up the right tools (LinkedIn, Apollo, your CRM), and gets to work. No distraction, no scattiness — but also no human nuance, unless you train it in.

The real conversation starter? Context windows and token costs. The more data you feed an LLM to process, the more it costs — and with agents running daily, that data compounds fast. Monday's four pages of LinkedIn messages becomes seven by Tuesday, ten by Wednesday... and your bill quietly snowballs with it.

Nick's solution: a structured database that acts as a memory layer, feeding the agent only the specific, relevant data it needs — not the whole book, just the right chapter. This can cut processing costs by 50–60% and keeps your GDPR exposure in check too.

They also touch on the limitations of existing recruitment CRMs (spoiler: most aren't built for this), and why chasing the next shiny agent tool without understanding the cost architecture could hurt more than help.

Key takeaway: AI agents are powerful — but they're not set-and-forget. Understanding tokens, context, and data flow isn't just for the technically-minded. It's now a recruitment business literacy issue.

Like our page and connect with us!

LinkedIn: linkedin.com/company/re-boot-ai-and-recruitment/

Nick: https://www.linkedin.com/in/ai-agents-for-recruitment/

Kat: https://www.linkedin.com/in/katkingshott/