Here's 5 recruiting agents I use daily at Abnormal AI.

🔍 Outbound Sourcer: Thinks like a top recruiter and a top candidate at the same time. Reads the profile, infers what motivates them, writes outreach that actually lands. Not templates. Actual personalization at scale.
📝 Intake Agent: Stops bad reqs before they open. Pushes back on vague job descriptions and forces clarity on what "good" actually looks like. Less interview noise. Fewer late-stage surprises.
🎯 Debrief Synthesizer: Turns messy panel feedback into a clear hiring decision. Flags conflicting signals, catches bias patterns, and makes a recommendation. Hire, no hire, and why.
🦄 Recruiting Ops Automator: Finds where your process is quietly breaking. Time-in-stage, funnel drop-off, and offer slowdowns — surfaced automatically, no manual report needed.
🏆 Talent Brand Agent: Keeps your voice consistent across every candidate touchpoint. Same positioning, same tone, no drift. It also includes employee stories for the different job families.

The numbers across two quarters: 88% offer acceptance with a 46.6% InMail response rate — nearly 2x benchmark, up 22.6% since October.

That's what recruiting on systems looks like. Most teams have the same tools.

The gap is whether you're building or waiting.

We’ve built an AI operating stack that’s helping our recruiting firm move faster without adding more admin overhead.

Current stack:

- AI sourcing workflow to identify candidate leads
- Data enrichment workflow to improve contact and company context
- LLM + MCP connection to read/write directly in our CRM
- AI roll-up workflow to prep outreach-ready records
- Email + texting workflows for faster candidate response
- Social media content agent and scheduler for consistent daily posting and client development

What this does for us:

- Frees recruiters from low-value admin work
- Keeps CRM data cleaner and more current
- Speeds up candidate identification and outreach
- Improves consistency in market visibility and brand presence

We’re doing this with a ~400,000-person database, and it’s made a major impact on execution speed.

If you want the exact framework, comment SYSTEM and I’ll send it.

Recruiters! I'm 50/50 terrified/in love with AI. But is it making us terrible recruiters? 👇


Anthropic tested 52 junior engineers learning a new Python library. The group using AI scored 17% lower on comprehension.

The ones who asked AI "why does this work?" scored above 65%.
The ones who just asked it to write the code scored below 40%.

METR found that developers using AI took 19% longer to complete tasks. They expected a 24% speedup. After experiencing the slowdown, they still believed AI had made them faster by 20%.

They got slower. They thought they got faster *Luke raises his eyebrows in suspicion*

Wharton tested 1,372 people across 9,593 trials. Acceptance of incorrect AI answers hit 79.8% because people just... stopped checking. They also felt more confident because they had checked AI.

SO...The million dollar question is, would any of this "cognitive surrender" spill over into recruiting?

How about your sourcing instincts? AI runs the search. You stop building market knowledge. When the results are rubbish, you can't tell because you've lost the ability to judge what awesome looks like.

Your outreach craft. AI drafts your messages. You stop practising the skill of writing something a real person would actually reply to. Reply rates drop and you blame the market.

Your candidate assessment. AI screens CVs. You stop developing the wee internal alarm that fires when something doesn't add up.

The research also shows AI as a tutor accelerates learning. Students in AI-powered learning environments scored 54% higher. So this isn't about ditching the tools. It's about whether you're using them to think better or to avoid thinking altogether.

Two things you can do this week:

1️⃣ When you use AI, ask it to explain it's reasoning before you accept the output. "Why did you rank this candidate higher?" forces you to engage critically.

2️⃣ Spot-check one AI output per day against your own judgement. If you consistently can't tell when it's wrong, that's atrophy.
What's one recruiting skill you've noticed getting weaker since you started using AI?


I'm a constant AI user, its embedde dinto every part of my working life so I'm not saying "Boooo AI" I'm challenging us to properly discern where AI makes us baetter and where AI. makes us worse.

I'd LOVE a massive multi-sided chinwag on this one, do you agree? how do you choose what goes to AI and what stays with you squidgy organic things between your ears? Tell me in the comments!


Hi 👋 I'm Luke. I empower recruiters with data. Want to get data-driven for free? Link in my profile for my weekly newsletter.

Recruiters, when was the last time you looked at what your sales team is using?

Last week I spoke at Talent Crunch Berlin about using Clay for recruiting. Most recruiters stick to LinkedIn Recruiter. Nothing wrong with that. But once your sourcing process works manually, there's a lot you can automate.

I presented a live workflow for a fictive case: a Berlin-based Series B looking for an AI Engineer. Not just technically strong, someone who also acts as an evangelist. Conference talks, open source, community presence.

Static profile data won't tell you that. So we used Clay to:
1. Pull the candidate's last LinkedIn posts to see how active they are on social media
2. Find their GitHub account automatically
3. Rate the profile based on contributions, repositories & stars
4. Feed everything into a scoring agent
5. Push top-scored candidates into LaGrowthMachine for outreach

It makes the sourcing & qualification process a lot more efficient.

Great meeting Willem Wijnans & Dita Biško and good seeing some familiar faces again. Thanks to Andreea Lungulescu , Ashby and the team for the orga & to Taxfix for hosting this event.

Want the workflow? Just reach out.

Apparently, I'm supposed to be worried about my job...



At least that's what my feed keeps telling me. Every other post: "In 12 months, your job won't exist." And look, I'm not going to pretend automation isn't real.

Boolean searches? Automated.

Candidate lists? Generated in seconds. Personalized outreach at scale? AI does it before breakfast.

But here's what nobody's talking about.

The best sourcers I know aren't running from AI — they're running with it. Because here's the reality: AI is incredible at the mechanical stuff. The searching. The pattern matching. The data crunching.

But it completely falls apart at the human stuff.

Try getting AI to build genuine trust in a 15-minute call. Navigate the politics of a hiring manager who "knows what they want" (but doesn't). Sense when a candidate is actually open vs. just being polite. Handle the chaos of real-world recruiting in a large organization.

It can't.

And here's the part people miss: you still need to know how sourcing actually works to make AI work for you.

A bad prompt from someone who doesn't understand the craft? You get garbage.

A smart setup from someone who knows the game? You get magic.

The role isn't disappearing. It's upgrading. The future sourcer isn't just a LinkedIn detective — they're part strategist, part relationship builder, part AI orchestrator.

They let the tech handle the noise.

And they focus on what actually matters: conversations, experience, and the messy human side of hiring.

So will AI replace sourcers? Sure. The ones stuck in 2019.

But the ones evolving? The ones blending tech-savvy with people skills?

They're just getting started. 🚀

#sourcing #recruitment #GenAI

I have spent nearly three decades in talent acquisition, and one thing has always stayed true.

Recruiting works best when the systems behind it work.
Clear intake. Strong sourcing strategy. Clean workflows. These are the pieces that make the work feel doable instead of chaotic.

In healthcare especially, I have seen how easy it is for recruiters and sourcers to feel overwhelmed when roles are complex, pipelines are thin, and tools do not always keep up. I have also seen how the right process can change everything.

This is the space I love working in.
Intake. Sourcing strategy. Outreach. The small systems that create big wins.

I am also a strong believer in using AI to support the work we do. Not to replace recruiters, but to help us think more clearly, write more effectively, and move faster when it matters most. I use AI every day to build sourcing plans, draft outreach, clean up job postings, and bring structure to the parts of recruiting that often feel messy.

If you care about:
 building better sourcing systems
 improving intake
 modernizing how we approach hard-to-fill roles
 using AI without feeling overwhelmed
 making the day-to-day work feel lighter

You are in the right place.

I will be sharing more of what I have learned over the years. Practical strategies, prompts, sourcing ideas, and lessons that come from real experience in the trenches of healthcare recruiting.

If this resonates, I would love to connect and learn from your work too. Here is to building better systems together.

I Built a Full Hiring Automation System And It Changes Everything for Recruiters

Most organisations are still hiring the way they did 10 years ago manually sourcing profiles, reading every resume, sending copy-paste messages, and scheduling endless intro calls.

I designed a system that flips that entirely.

Here's how an end-to-end AI-powered hiring automation pipeline works — and why your recruiting team needs it NOW 👇

🔍 STEP 1 — Smart Sourcing (300–400 Profiles Per Role, Per Day)
I built the system to auto-generate Boolean search queries based on must-have skills, job titles, and dealbreakers — then pull 300–400 matching profiles daily from LinkedIn and Naukri directly into your ATS/CRM. No manual hunting. No missed talent.

🧠 STEP 2 — AI-Powered Screening (2-Layer Filter)
Every resume is parsed into structured data — skills, experience, companies, education, and seniority level — then scored using a two-layer approach I designed:
✅ Layer 1: Rules-based filtering (removes candidates missing must-haves instantly)
✅ Layer 2: AI Scorecard (GPT-4o / Claude) — evaluates skill match, project relevance, career stability and flags risk signals like job-hopping or skill gaps, with written reasoning and quoted evidence from the resume.
Recruiters only see candidates who are actually qualified.

✉️ STEP 3 — Personalized Outreach at Scale
Forget generic InMails. I integrated AI to draft hyper-personalized messages referencing each candidate's specific skills, current role, and standout projects — then run multi-step follow-up sequences automatically. Reply classification (Interested / Not Now / Wrong Person) is handled by AI so your team spends zero time sorting inboxes.

🎤 STEP 4 — Async AI Interviews (Before a Recruiter Ever Joins)
I added an async interview layer where interested candidates receive a chat or video interview link they complete on their own time. The system transcribes responses, scores competencies, flags weak answers, and pushes a full recommendation (Proceed / Hold / Reject) into the ATS — before a single recruiter minute is spent.

🛠️ The Tech Stack I Used
ATS/CRM: Greenhouse / Lever
Workflow Orchestration: n8n → Airflow
Data: Postgres + S3 + Pinecone (Vector DB)
AI Layer: GPT-4o / Claude + embeddings
Parsing: Affinda, Sovren, DaXtra
Outreach: Lemlist, Reply.io, Apollo
AI Interviews: HireVue or custom-built with Twilio + Whisper + LLM


The result? Recruiters stop doing administrative work and start doing what they're actually great at — building relationships and making great hiring decisions.

This isn't the future of hiring. It's happening right now.

🎥 Watch the full video breakdown above to see how each piece connects.

If you're in HR, Talent Acquisition, or building recruiting tech, drop a comment or DM me. Happy to share the full system design.

♻️ Repost if you think every recruiting team should see this.