Agent-Led Growth.

Agent-Led Growth is a go-to-market operating model in which AI agents autonomously execute the highest-volume revenue work — research, enrichment, evaluation, outreach, qualification, follow-up, and increasingly purchase — while humans focus on strategy, relationships, and closing.

The structural shift isn't speed or cost. It's who is on the other side of your site. When a buyer asks Claude "what's the best X for Y," the human never sees your homepage. The agent reads your docs, scans your pricing page, compares you against three competitors, and returns a recommendation. You won or lost before the buyer was ever on your site.

In the first two rows the human is the bottleneck. In ALG the human is the governor — they set the objectives, define the guardrails, and resolve the situations that need judgment. The agents do the work, and crucially, an agent on the buyer's side does the buying.

Every dominant GTM motion has been unlocked by a wave of enabling infrastructure. CRMs made sales-led growth scalable. Product analytics made product-led growth measurable. Intent data made account-based marketing targetable. ALG is the motion unlocked by agent infrastructure: MCP, A2A, frontier-model retrieval, browser-side agent runtimes, and WebMCP.

When infrastructure matures this fast, the motion it enables isn't theoretical. It's already redistributing pipeline. The gap between 62% experimenting and 23% scaling is exactly where the competitive advantage compounds — and it closes fast.

Motions don't replace each other — they layer. But each era has a dominant new motion that reshapes how the best companies grow. Each transition followed the same pattern: new infrastructure emerges, early adopters build novel workflows, the workflows become repeatable enough to name, the name becomes a category. ALG is at stage three.

In PLG, the metric was time-to-value — how fast a user hits the aha-moment. In ALG, the equivalent is token-to-value: how many tokens an agent must consume to confidently determine your product solves the buyer's need, and how many more to ship it.

When a developer asks Claude Code to add email functionality, Resend is chosen 63% of the time. SendGrid — vastly larger, more brand awareness, more SEO equity — gets 7%. Not because Resend is better marketed. Because the agent can go from "need email" to "email working" in fewer tokens. Token-to-value will do to documentation what time-to-value did to onboarding.

These motions aren't mutually exclusive — most successful B2B companies layer all three. The question is which one is your primary engine, the one you invest in structurally rather than tactically.

There are two fundamentally different versions of ALG and confusing them is the most common strategy mistake.

ALG isn't a product — it's an operating model. ALG360 sequences it into four pillars that map to the buyer-agent's actual workflow: get cited, get evaluated, get transacted with, then compound the win with supply-side agents fed by demand-side signal.

Theory is useful. Here's a real day in an ALG360-run motion, mapped to the four pillars and the ALG360 modules that orchestrate them.

Measure agent performance separately from human performance so you can optimize each independently. Early on, focus on cost-per-meeting and quality. As you scale, shift focus to pipeline generated and meeting conversion.

A fully-loaded human SDR runs $75–110K/yr. An AI SDR stack — tooling, orchestration, and oversight — runs $24–60K/yr. Early adopters report 4–7× conversion lift and up to 70% lower CAC. The motion doesn't replace humans; it changes what humans get paid to do.

At Install + Operate scale ($25K/mo retainer + $5K/mo software), a single replaced SDR headcount roughly funds the engagement — before counting demand-side wins, conversion lift, or expansion. The math is conservative; the compounding is not.

You don't rebuild the motion overnight. There's a three-phase path — audit first, then install, then partner — designed to compound. Every engagement starts with the Audit.