AI SDR: 10 agents to assess before automating

AI SDR describes several product categories. A full agent may apply an ICP, research accounts, enrich contacts, draft contextual messages, sequence outreach, handle initial replies and book meetings. A more conservative product assists an SDR with research and drafts. Inbound agents focus on website visitors and follow-up. Those are different jobs. Define one success unit before evaluating: qualified reply, held meeting, opportunity created or correctly enriched account—not emails sent. Then define data sources, exclusions, account ownership and the exact CRM output. AI scales the quality of the underlying process; it cannot repair a vague value proposition or unreliable proof.

Artisan/Ava is worth evaluating for integrated outbound, with prospecting, enrichment, campaigns and escalation controls. 11x/Alice and Julian is a candidate for mature RevOps teams that want a broad autonomous worker model. RegieOne fits established SDR teams that need sourcing, enrichment, sequencing and call preparation alongside reps. Reply/Jason AI is compelling when playbooks, knowledge bases and approval queues are central to governance. AiSDR is a focused campaign-oriented option to test against your own segment. Salesforge/Agent Frank offers copilot and autopilot modes, making a narrow supervised pilot practical. Qualified/Piper is principally an inbound option for teams converting website demand and email follow-up. Landbase belongs on a shortlist when data quality, targeting and enrichment are the bottleneck. Rox is relevant when account prioritization and GTM execution matter more than sending volume. Apollo is a pragmatic choice for teams that prefer a human-controlled prospecting workflow over a fully autonomous agent. This is a functional selection, not a universal ranking; capabilities and commercial terms must be rechecked in a live evaluation.

Run the same test with every vendor. First, give them the same ICP and exclusions and judge twenty proposed accounts for fit and duplicates. Second, ask for the source behind five personalization claims; unverifiable research should not be sent. Third, blind-review French or English sequences for clarity, restraint and ease of reply. Fourth, test approval mode, escalation, pause controls and audit history. Fifth, create and update records in the CRM, including an opt-out and a handoff, to confirm account ownership is protected. Sixth, ask operational questions about suppression lists, deletion, retention and user access rather than seeking vague compliance assurances. Seventh, calculate the total operating cost: platform, data, sending infrastructure, setup and supervision. A four-to-six-week single-segment pilot is more reliable than a demo promise.

Start with one offer, one segment, one account owner and a tight knowledge base of verified claims, approved use cases and forbidden wording. Use human approval for early drafts and replies. Label every proposal as accepted, tone-edited, substance-edited or rejected; this identifies whether the issue is targeting, research, offer or copy. Automate only repeated, low-risk situations after review. Establish hard exclusions for active opportunities, customers, partners and do-not-contact records. Set a contact cap per segment and pause when repeated negative feedback appears. Finally, hold a short weekly review between marketing, SDR and RevOps to inspect conversations, misclassified accounts and missing CRM fields. An AI SDR earns wider autonomy through demonstrated relevance and clean handoffs.

For mature outbound with a stable ICP, compare Artisan, 11x, Reply/Jason, AiSDR and Agent Frank on controls, research quality and CRM output. For a mature SDR organization, RegieOne, Rox or Apollo may be more rational because they reduce preparation and prioritization work while humans retain strategic conversations. For an inbound engine already supplied by marketing, Qualified/Piper deserves special attention; its job is fast engagement and routing at moments of intent. Landbase is worth assessing when account data quality is the first constraint. Choose the agent that removes your current bottleneck, prove it on a constrained pilot, then expand only what improves qualified conversations.