By Sagar Shankaran, Founder of CallSphere
Salesloft's Rhythm AI agents shipped autonomous workflow execution in 2026 across enterprise. Here's the architecture, the pricing model, the rollout pattern.
Key takeaways
Salesloft produced one of the more consequential April 2026 announcements for sales and marketing buyers. The platform changed shape, the pricing model evolved, and a wave of named enterprise customers committed publicly. Together those signals reshape the vendor shortlist for any team running a sales and marketing AI agent RFP this quarter or next.
This post breaks down what shipped, what's now in production, what the contract looks like, and what to do about it as a buyer or a competing vendor.
Public confirmation in the last 30 days, by category:
The pattern is consistent: pilots get fast results, expansion happens within two quarters, and the displaced incumbent is usually a legacy platform with bolt-on AI rather than a true agent-first stack. The deciding factor in head-to-head bake-offs is rarely the model — it's the integration depth, the audit posture, and the willingness of the vendor to expose the underlying prompts and tool definitions to the customer.
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Three failure modes we've seen repeatedly in sales and marketing AI agent contracts in 2026:
Procurement teams who haven't seen agent contracts before consistently miss these. Bring an experienced reviewer into the cycle early — ideally one who has redlined at least three agent platform contracts.
The sales and marketing vertical has agent-deployment specifics that don't show up in horizontal coverage and matter at procurement:
The vendors winning in sales and marketing are the ones that built around these constraints from day one rather than retrofitting them onto a horizontal platform after the fact.
The vendors most often appearing in the same RFPs in this segment in 2026:
In-house builds are gaining share at companies with strong AI engineering teams — Stripe, Notion, Ramp, Linear all have meaningful internal agent platforms in 2026 that they've chosen not to outsource. The build path requires roughly 5-10 dedicated engineers and 12-18 months to reach production parity with leading vendors, but the long-term unit economics are compelling at high volumes.
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CallSphere ships complete AI voice agents per industry — 14 tools for healthcare, 10 agents for real estate, 4 specialists for salons. See how it actually handles a call before you book a demo.
For teams that want this kind of voice and chat agent capability without an enterprise platform commitment, CallSphere ships a turnkey AI agent platform with the same model routing, integrations, and compliance controls in a single SKU. Worth a look alongside the named vendors above.
What changed in sales and marketing AI agents in April 2026? Pricing models shifted from per-seat to per-conversation and per-outcome at the leading vendors. Model quality moved up enough that resolution rates above 70% are now expected at the top tier. New entrants began winning enterprise accounts that had been incumbent strongholds.
Which vendor is the safest enterprise default? There isn't one yet. Sierra has the highest reasoning quality. Salesforce Agentforce has the best CRM integration. Decagon has the cleanest pricing model. The right answer depends on your existing stack and your strategic priorities.
What's the biggest mistake buyers make? Starting with the model and working backward to the use case. Start with the intent map, the escalation rules, and the success criteria, then pick the vendor. The model itself is the easy part.
How do we handle compliance for sales and marketing AI agents? BAAs, DPAs, SOC 2 Type II reports, model output logging, audit trails, and explicit consent flows. Every serious vendor in this segment supports these — but you have to ask for them in the contract and verify the artifacts before signing.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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