AI email agents for BDR teams

AI Agents rewrote how we win clients in 2025. After 250+ hours of research, here's what I found: They took over many GTM use-cases 👇 - Segmenting/qualifying leads on autopilot. - Conduct deep prospect research. - Personalize outreach at scale. - Build prospecting workflows. ... and more. Agents typically follow this logic: A. Figure out actions to be taken. B. Perform set of actions automously. C. Generate outputs + describe what they did. The best ones focus on verticalized use-cases: 1. Agent builders ↳ They help you craft custom agentic workflows. Examples: - Relevance AI - Relay.app - n8n - Dify - Taskade - wordware (YC S24) The best way I found to utilise these is to review manual, time-consuming, processes and automate them through the above platforms. 2. Scraping Agents ↳ Help you extract online data at scale. - Clay's AI Agent - Browserbase - Gumloop - ZenRows - Firecrawl - Apify The best way I found to use them is to think: "what's a data point that, if was true about my target, would mean they're a perfect fit for my offering?". Get creative with it... these agents help you get hard-to-find data. 3. MCP Servers ↳ Enable AI Agents to take real-world actions by connecting to your tool stack. E.g: You connect your LLM (e.g: Claude) to your Slack, Notion, Calendar, your Gmail, Stripe & even your Sales Engagement tools. You can now perform tasks like: - Rescheduling and cancelling meetings by prompting Claude. - Monitor your mailbox by asking Claude if you missed any emails. But also... Build entire prospecting campaigns by connecting your LLM to Clay, Instantly & other tools... without having to be in the interfaces of the tools. This means controlling all your work through one chat interface. MCPs are the glue between your LLM & your tech stack: Examples include: - Docker, Inc - Pipedream - Composio - Zapier 4. AI SDRs ↳ They automate cold email & LinkedIn prospecting. - Artisan - Valley - 11x - AiSDR - Topo (YC W24) - Jason AI by Reply Everyone criticises them, but the ones I've seen at play (Artisan, Valley) are generating very decent outputs. Still, make sure to review these manually. 5. GTM Co-Pilots ↳ Help you run human-crafted, complex GTM workflows. - Instantly.ai - Unify - Copy.ai - Bardeen - Common Room This category is broad, as all have a different approach. But, as an example, Instantly.ai's new copilot allows you to build entire prospecting campaigns (including building lists, writing sequences, and automating sending) within their platform by writing a few prompts. 6. Sales Agents ↳ Gather insights from your CRM / Call transcripts to help you close deals. Examples include: - Attention - Attio - Gong - Momentum.io 7. Research Agents ↳ They help you perform deep account research at scale. - Claygent - Linkup - Airtop - Exa - Tavily - Perplexity 8. List building Agents ↳ Help you build prospect lists. - Instantly.ai - Openmart - Exa P.S: Which platform did I miss? 👇

The traditional SDR role is dead. Not in 10 years. But today. Here's why:   Most teams are using the old model.   Hire junior reps to blast cold emails. Book demos. Burn out in 12 months.   It’s slow. Expensive. And it doesn’t scale.   But a shift is happening:   AI isn’t just making SDRs faster.   It’s replacing them.   The best GTM teams don’t hire more reps. They build systems that do what reps can’t:   - Spot high-intent leads on LinkedIn - Enrich contact data in seconds - Personalize every message - Reach out minutes after the signal - Nurture across LinkedIn, email, and SMS   We’ve built that exact system at Atticus. Here’s what it looks like:   1️⃣ Signals + Lead Scoring   → Tools: Valley, Clay, RB2B → Auto-detect profile views, site visits, competitor follows → Score leads against our ICP in real time   This gives us a live queue of warm leads, before most teams even know who to contact.   2️⃣ Enrichment + Research   → Tools: Clay, Prospio → Pull job titles, emails, hiring activity, and funding rounds → Auto-generate talking points and likely pain points   No guessing. Just hyper-relevant context, pulled and packaged instantly.   3️⃣ Outreach + Routing   → Tools: Valley, Smartlead, HeyReach → Send personalized DMs, cold emails, and social touches → Route hot leads to the AE or founder with zero friction   The system does the work. Our team only steps in once a conversation’s started.   4️⃣ Human-Like Follow-Up   → Tools: Custom AI agents trained on past conversations → Handle replies, ask qualifying questions, and direct to demos → Handover to AE via DM, email, or SMS when ready   Every reply gets a response. Every signal gets actioned.   No leads slip through the cracks.   The results?   → Replaced 2 full-time SDRs → Booked 30–50 SQLs/month → £0 in ad spend → 3–5x reply rates compared to manual outreach   The future of outbound isn’t headcount.   It’s systems. P.S Peep the screenshot below on a recent outbound campaign - 33% response rate - 688 messages, 54 leads and 9 meetings booked. I believe the acceptance rate was lower because a value prop was given in the first message. We currently aren't able to empty connect on software.

I see a lot of skepticism around AI SDR agents… and I totally get it! The AI SDR agents value propositions flooding the market right now are: ➡️ All-in-one tools that help you find leads through Apollo (or other). ➡️ Automation tools like Smartlead or Instantly, letting you send out high volumes emails. ➡️ Using AI in the funnel only for copywriting. ➡️ And, no matter the industry, it's the same AI agent for all the customers! That’s great, yes… but NOT enough!!! When we launched Topo (YC W24), we aimed to fully mimic the end-to-end human behavior of the best SDRs we know (👋 Valentin, Robin). The goal? 🎯 To replicate human behavior that follows a clear outbound playbook, thus eliminating the need for an army of SDRs. BUT, without compromising on quality work!!!! After onboarding dozens of clients over the past few months, we realized our performance wasn’t consistent across all industries. So, we made a big decision 🚨 We're rolling out expert agents 🤓, pre-trained in four industries: 💻 Selling to Developers or IT (devtools, cyber, gaming, AI, IT tools...) 🤝 Selling to HR (HRIS, ATS, LMS, hirings, trainings, benefits...) 💰 Selling to business profiles (sales tools, marketing tools...) 🛍️ Selling to e-commerce operators These agents: - Understand the personas 💡 - Detect industry-specific signals 🔍 - Have access to industry-specific data sources 🧑💻 - Learn from successful campaigns across our clients 📈 Applying AI agents to lead generation CAN'T just mimic an intern’s behavior... it must replicate that of a senior, top-performing industry expert! 😎 If you want to learn more about those pre-trained sales agents, my agenda is in the 1st comment 👇

A LinkedIn post with the most comments ever (17,697) was one about AI agents. The hype is real but the question is… How do you avoid the smoke-and-mirrors and find the right AI Agent for outbound? 💎 𝗦𝘁𝗲𝗽 1: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 The success of your initiative depends on the data that the AI agents use, such as: 1st, 3rd party data like signals, your CRM data, product usage data, etc. If the data isn’t accurate, AI agents won’t be effective, or worse, they could damage your brand & reputation. As the saying goes, garbage in, garbage out. To assess the vendors’ data accuracy: 1. Identify 1-3 specific campaigns you want to start immediately with the AI agent. The results will be more relevant and real for your team. And once you pick a vendor, you can turn them on immediately, reducing the time to impact. 2. Provide each vendor with the same list of accounts and contacts so that you can compare apples to apples. For example:  - List of Customer accounts - List of Target accounts - List of Customer contacts - ICP criteria - Persona criteria 💎 𝗦𝘁𝗲𝗽 2: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗔𝗜 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 Ask each vendor to provide you with 20-30 sample emails for the campaigns to review. Even though messaging is subjective, you can assess for the following: - Does the AI articulate the pain points I'm solving, the persona-based messaging, and any industry-specific messaging well? - Are the emails truly personalized? “Bad AI” personalization is simple scraping output, like, if [this] then [that] without considering the holistic context around that account or buyer. That’s why they sound robotic or completely irrelevant. “Good AI” personalization: - Combines all the data points (signals) of what is happening at that account and buyers and any existing or past relationships you have with them - Knows which data points (signals) are more important - Can reference back to its previous messaging to the buyers so that it sounds natural & human-like 💎 𝗦𝘁𝗲𝗽 3: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘂𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 One of the key factors for any new initiative is strong user adoption. For AI initiatives, usability isn’t only for the end-users (i.e. SDRs and AEs) but also for the business users & admins (i.e. SDR/marketing/sales managers and Ops) Depending on the level of AI and technical skills of your existing team, look for solutions that most of your teams can use, maintain, and scale. Overly technical solutions could cause: - Challenges in hiring & backfilling the technical resource - A bottleneck for the existing technical resource - Your team spending time tooling & bug fixing instead of focusing on the business outcomes ------- Follow this checklist and you'll probably save yourself & your team a lot of time and headache.

AI agents just replaced an entire SDR team and most sales leaders are still debating whether to hire more reps The math isn't even close anymore. I'm watching companies deploy AI agents that research prospects, write personalized outreach, handle initial responses, and book qualified meetings. All day. Every day. Without coffee breaks or commission negotiations. Meanwhile, sales leaders are posting job openings for 20-person SDR teams. Here's what's happening right now AI agents cost $2K monthly and generate 200+ personalized touchpoints daily Traditional SDR costs $80K+ annually and manages 15-20 conversations weekly AI agents book 40-60 qualified meetings per month Average SDR books 2-4 meetings monthly Your competition isn't hiring more SDRs. They're automating volume prospecting and repositioning their humans for high-value activities. The new SDR role looks completely different → AI strategy manager → Complex conversation specialist → Key account relationship builder → Quality control for automated outreach These aren't basic chatbots sending LinkedIn spam. AI agents understand context, industry nuances, and buyer psychology better than most junior reps. The companies waiting for perfect AI solutions will find themselves competing against teams that automated 70% of sales development six months ago. This isn't coming next year. It's happening now while you're interviewing candidates. Ready to future-proof your sales development approach? Check out The Innovative Seller for the framework on evolving your team before the market forces the change — ♻️ Repost this if you're seeing the shift Follow for more AI and sales insights