AgencyBloc is the market leader, 6,500+ agencies and the #1 Recommended platform. You just put AI into the product with AgencyBloc Intelligence. The role says you want the demand engine agent-powered too. Here is that engine, built for AgencyBloc, plus the one place a smaller challenger is quietly out-marketing the market leader.
A teardown of where the demand engine has headroom, the multi-agent stack the role describes built for AgencyBloc, and the first 90 days. Public signals only, before day one.
G2 now names NextAgency Best Life & Health Insurance Agency Management Software. They run a comparison page against you that ranks on page one, and use it to tell buyers AgencyBloc costs roughly 30 percent more per seat month to month and almost 55 percent more annually.
Capterra, G2, SelectHub and Fuzen all rank “AgencyBloc alternatives” pages. On review scores the challenger sits ahead on both marketplaces, 4.8 and 4.9 against 4.7, and on support 9.9 against 9.0.
None of that is a product problem. It is a distribution one, and the page a competitor wrote is currently the thing an AI reads when a benefits agency asks which system to buy. Check it in a minute: search your own category and count how many of the first ten results you control.
The product leads the category and the customer proof is excellent. The headroom is in how the demand engine runs and where buyers now form opinions. Four observable gaps, each one a lever demand generation owns, and each one closes with the same fix: an agent-powered demand engine.
AgencyBloc owns the install base and the "#1 Recommended" claim. But ask ChatGPT, Claude, or Perplexity "best health insurance agency software" or "AgencyBloc vs NextAgency" and a smaller challenger is winning the narrative: NextAgency holds the G2 "Best Life & Health AMS" position and has built the comparison-content moat (framed as cheaper, better-supported) that the engines synthesize. The leader has the customers; the challenger has the surface where buyers now decide.
Win back the comparison and AI-answer real estate. It's the fastest high-leverage demand win on the board, and an agentic content + CRO stack produces it at scale. Scorecard below.
The role itself splits the work: high-velocity VSB inbound and Key Account ABM for uplines, IMO/FMOs, and carriers. Those are different buyers, different cycles, different plays. Run through one undifferentiated funnel, the high-velocity motion gets over-engineered and the high-value accounts get under-served, and pipeline leaks from both ends.
Two engines, one operator: a self-serve-speed inbound motion and a precision ABM motion for Key Accounts, each instrumented to its own funnel and revenue target.
The platform spans AMS+, Commissions+, Engage+, and Quote+, yet most agencies land on one. Cross sell is named as a core motion for a reason: expanding an existing, trusting customer is the lowest-CAC revenue there is, and 6,500 accounts is a large, warm, under-mined base.
An agent-run lifecycle motion that watches product usage and renewal signals and triggers the right cross-sell play to the right account at the right moment.
The way this is usually measured gives it away. The ask is "measurable gains in throughput, iteration speed, and creative variant volume" and a "CRO agent running continuously." Those are the words of a team that knows execution is currently throttled by human bandwidth. A human-paced engine can't test enough, ship enough variants, or watch enough accounts to win a fast category.
Put agents on research, audience selection, variant generation, sequencing, attribution, and CRO, human-in-the-loop on what matters. This is Layer 02, and it's what I build.
AgencyBloc Intelligence brings AI to how agencies work. The same conviction hasn't reached how AgencyBloc grows yet. Every gap here, the lost AI answer, the two motions, the un-mined base, the human-paced cadence, closes when the demand engine becomes as agent-powered as the product is becoming.
Layer 02 is that engine: the multi-agent stack the role describes, designed for AgencyBloc.
Not an assertion, the evidence. AgencyBloc versus NextAgency across the surfaces an AI engine pulls from when a benefits or Medicare agency asks it what to buy. AgencyBloc wins where it has scale; the challenger wins where buyers now form their shortlist.
| Where buyers & AI engines look | AgencyBloc | NextAgency | What it means |
|---|---|---|---|
| Market share / install base | Leader (6,500+) | Smaller | AgencyBloc owns scale and the "#1 Recommended" claim. The brand asset is real. |
| Review-site ratings | 4.8 / 4.7 | 4.9 / 4.8 | Tight, but the challenger edges it, and leads on support score (9.9 vs 9.0). AI engines read those deltas. |
| G2 category position | Strong | "Best Life & Health AMS" | The challenger holds the category-leader badge the engines cite first. |
| "X vs Y" comparison content | One page | Owns the matchup | NextAgency has multiple pages framing the comparison on its terms. Ask AI "AgencyBloc vs NextAgency" and the challenger's framing answers. |
| The open AI answer | Under its weight | Punches above | For "best health insurance AMS," the market leader is not the default answer. That is a marketing gap, not a product one. |
The gap is not someone who has heard of AI. It is someone who has designed and operated multi-agent stacks across campaigns, ABM, attribution, and CRO, human-in-the-loop. Here are the four stacks, built for AgencyBloc's two motions. This is not a concept for me; it's what AnalyzeLens already does.
Research, audience selection, asset variants, sequencing, and in-flight optimization across paid, content, email, and events, mapped to segment and funnel stage.
For Key Accounts (uplines, IMO/FMO, carriers): account research, fit + intent monitoring, enrichment, prioritization, and sequencing input handed to sales.
Weekly full-funnel reporting, channel and campaign ROI, conversion by stage, and pacing against pipeline targets, in execs' hands without a manual scramble.
Running continuously across forms, pages, flows, and conversion paths, every test measured against pipeline, not vanity clicks.
How I'd sequence it: prove the gaps with data, take back the answer the challenger is winning, then stand up the agent stacks across both motions, working in lockstep with Sales, RevOps, Product Marketing, and Customer Success.
A demand function lives or dies on cross-functional alignment. What I'd trade with each partner.
| Partner | What I need from them | What I give them |
|---|---|---|
| Mike Lamb (CEO) | The growth mandate and air cover to run agent-first | A demand engine that turns market leadership into the AI-answer lead too |
| Sales / Key Accounts | Account priorities and feedback on opp quality | Scored, enriched, prioritized accounts and a clean lead-to-opp handoff |
| Revenue Operations | CRM, attribution plumbing, data hygiene | Weekly full-funnel truth: ROI, conversion by stage, pacing to target |
| Product Marketing (the sister hire) | Positioning and the value story by segment | Demand surfaces that put that story in front of the right buyer |
| Customer Success | Usage and renewal signals | Cross-sell plays that expand accounts without adding friction |
Most agent projects do not fail on model quality. They stall because context is lost between every run so the thing never compounds, and because nobody can prove it caused anything, so at the next budget review it quietly loses.
OfficeSpace Software, BlueConic, Zyter, Kalivia, iSmile Dental Software, Defiant Health, GroundIQ and PracticeIQ. Before that, founding marketing hire at Cohere Health through the run from $5M to past $170M ARR.
Anyone can ship an agent that reports success. Here is one of mine reporting it did nothing. 440 people messaged, 53 booked. 60 held back at random and never contacted, 11 booked anyway.
So it caused zero bookings rather than the 53 it could have claimed. It said so on its own screen and stopped recommending that campaign.
An agent that answers a broker's question about coverage, pricing or a carrier is making a claim on AgencyBloc's behalf. In a regulated distribution category that is a different risk class from a marketing chatbot being vague.
So nothing is recorded above a hint without the source it came from, that rule sits in the database rather than in a prompt, and what stopped being true expires on a schedule matching what kind of fact it is. A commission schedule from two plan years ago should not still be in its head. A contractual term should.
It is also what lets one operator run agents for several accounts without any of them reading another. Working demonstration here, including one that tries to break its own isolation and fails.
Multi agent campaign, ABM, attribution and CRO stacks, human in the loop, in B2B SaaS healthcare and benefits. This is not a capability I would grow into. It is what I run now, on top of a decade of building demand engines from zero.
Before you close this
I built it because the findings were sitting there and nobody had put them in one place. Everything on it is dated and checkable, and if any of it is wrong I would rather hear it from you.
If it is useful, the next thirty minutes are more useful than the page.
Book 30 minutesBring one number you do not trust. I will tell you whether an agent can fix it, what it would take, and what it would cost. If the answer is that you do not need me, I will say that instead.
Rehan / Agentic GTM systems, built, shipped, in production / analyzelens.com