TL;DR
- Agent-Led Growth (ALG) is the GTM motion where AI agents — Claude, ChatGPT, Perplexity, Cursor, Operator — do the research, evaluation, and trial on the buyer's behalf. The human stays in the loop to set the goal and approve; they no longer do the work.
- This is not 'AI in sales.' That's supply-side — agents helping you sell. ALG is demand-side — agents doing the buying. Confusing the two is the most common strategy mistake.
- The infrastructure is in production: MCP went 100K → 8M+ downloads in 6 months; Chrome 146 ships WebMCP; 57% of B2B uses AI sales agents. Most of your top-of-funnel traffic in 2026 will not be human.
- ALG360's framework: Introduce → Evaluate → Execute — one pillar for each job the agent now does on the buyer's behalf. Get named on the shortlist. Win the agent's evaluation. Let the agent transact with you, not around you.
- Early movers report 4–7× more conversions and up to 70% lower CAC. Recommendation moats compound with every model generation — by the time they're obvious, they're locked in.
Ch. 01Definition
What ALG is — and what it isn't.
Agent-Led Growth is the GTM operating model for a buying journey where the actor changed, not the journey itself. The human used to do the research, read the docs, build the feature matrix, and run the trial. Now an AI agent does all of that on the buyer's behalf. The human stays in the loop to set the goal and approve the outcome — but is no longer the one doing the work.
That changes who is on the other side of your site. When a buyer asks Claude "what's the best X for Y," the human never opens your homepage. The agent reads your docs, scans your pricing page, compares you against three competitors, and hands the human a recommendation. You won or lost before the buyer was ever on your site.
ALG is not "AI in sales." That's supply-side — agents helping you sell more efficiently (AI SDRs, content engines, pipeline automation). ALG is demand-side — agents doing the buying on the buyer's behalf. Supply-side ALG improves the economics of your current funnel. Demand-side ALG changes whose funnel it is. Confusing the two is the most common strategy mistake.
In the first two rows the human is the bottleneck. In ALG the human is the governor — they set the objective, define the guardrails, and approve the outcome. The agent does the research, evaluation, and trial. Crucially, an agent on the buyer's side is now the one comparing you to your competitors — and increasingly the one initiating the transaction.
Ch. 02Inflection
Why now: the infrastructure is production-ready.
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.
Ch. 03History
The evolution of GTM motions.
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.
- Enabled by
- CRM (Salesforce)
- North-star metric
- Quota attainment
- What changed
- Scalable sales team management
- Enabled by
- Mixpanel, Amplitude
- North-star metric
- Activation rate
- What changed
- Product as acquisition channel
- Enabled by
- 6sense, Bombora
- North-star metric
- Engagement score
- What changed
- Precision targeting of buying committees
- Enabled by
- MCP, A2A, LLMs, WebMCP
- North-star metric
- Token-to-value
- What changed
- Autonomous GTM, agent as buyer
Ch. 04The new metric
Token-to-value: the new north star.
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.
"Lowest token-to-value wins. Documentation is the new homepage; the homepage is decoration."
Ch. 05Comparison
ALG vs PLG vs SLG.
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.
- Engine
- Product experience
- Entry
- Free trial / freemium
- TTFV
- Minutes
- CAC
- $10–50
- Best for
- SMBs, devs, prosumers
- Scales by
- Viral loops, word-of-mouth
- Key metric
- Activation, viral coefficient
- Risk
- Conversion plateau upmarket
- Engine
- Sales team
- Entry
- Demo / sales call
- TTFV
- Days to weeks
- CAC
- $200–500+
- Best for
- Enterprise, complex, high-ACV
- Scales by
- Hiring more reps
- Key metric
- Quota attainment, cycle length
- Risk
- High CAC, hiring dependency
- Engine
- AI agents on both sides
- Entry
- Agent-evaluated recommendation
- TTFV
- Hours
- CAC
- $25–150
- Best for
- Post-PMF B2B, data-rich ICPs
- Scales by
- Deploying more agent instances
- Key metric
- Token-to-value, agent pipeline
- Risk
- Quality control, brand voice
Ch. 06Two sides of the loop
Supply-side vs demand-side ALG.
There are two fundamentally different versions of ALG and confusing them is the most common strategy mistake.
Agents working for the seller.
AI SDRs research accounts, AI content engines spin targeted content, automated pipeline management scores and follows up. Compelling economics — 4–7× conversions, up to 70% lower CAC — and the easiest place to start.
- Who deploys
- The seller
- Changes
- Funnel efficiency
- How to win
- Better data, sequences, agents
- Key metric
- Meetings booked, pipeline generated
- Maturity
- Production-ready
Agents working for the buyer.
A procurement team asks an agent to evaluate CRMs. A developer asks Claude Code to add payments. A marketer asks an AI to build a competitive analysis. The agent makes — or decisively shapes — the buying decision.
- Who deploys
- The buyer
- Changes
- Market structure
- How to win
- Better docs, simpler integration, transparent pricing
- Key metric
- Token-to-value, agent selection rate
- Maturity
- Early but accelerating
"Supply-side ALG improves the economics of your current funnel. Demand-side ALG changes whose funnel it is."
Ch. 07The framework
The ALG360 operating model: Introduce → Evaluate → Execute.
ALG isn't a product — it's an operating model. ALG360 sequences it into three pillars that map to the buyer-agent's actual workflow: get introduced to the agent, win its evaluation, then let it execute against you instead of around you.
Introduce
When the agent retrieves a shortlist for the buyer, you're on it.
Outcome: Named by Claude, ChatGPT, Gemini & Perplexity when the agent retrieves vendors.
- →Agent Readiness Scan — what the agent finds when it retrieves your category
- →Training-data, citation and tool-registry presence in the sources agents trust
- →Machine-readable product, pricing and capability data the agent can grab in one fetch
Evaluate
Win the agent's preliminary recommendation.
Outcome: Lowest token-to-value in your competitive set.
- →Evidence library of agent-readable docs, pricing & comparisons
- →Trust signals, proof points and pricing metadata in one source of truth
- →Continuous token-to-value scoring as competitors ship
Execute
Let the agent transact on the buyer's behalf — not route around you.
Outcome: Agent-initiated trials, provisioning, and checkout.
- →MCP / API handoff so agents can act on the buyer's behalf
- →Agent-native trial endpoints with zero-friction provisioning
- →Agent-confirmable purchase flows wired to your stack
Ch. 08In production
What agents actually do, all day.
Theory is useful. Here's a real day in an ALG360-instrumented motion, mapped to the three pillars and the ALG360 capabilities that run them.
- ·Weekly retrieval sweep across Claude, ChatGPT, Gemini and Perplexity
- ·Citation, schema and MCP-discovery tracking on hundreds of buyer prompts
- ·Machine-readable surfaces (schema, .well-known, structured pricing) shipped on a queue tied to retrieval gaps
- ·Human SEO/AEO referred to Teknicks — out of scope for ALG360
- ·Agent-readable docs, pricing and comparison surfaces
- ·Trust signals and proof points in one source of truth
- ·Continuous token-to-value scoring as competitors ship
- ·Agent-native trial endpoints with zero-friction provisioning
- ·WebMCP instrumentation on the top conversion paths
- ·Agent-confirmable checkout wired to your billing stack
- 07:02Signal
Agent Readiness Scan flags an account researching your category in Perplexity and ChatGPT this week.
- 07:03Evaluation
Buyer's agent pulls your evidence library — docs, pricing, comparison page — and stacks you against two competitors.
- 07:04Shortlist
Your name comes out of the model with a recommendation. The human never opens your homepage.
- 07:05Provision
Agent hits the trial endpoint, gets a working sandbox in seconds, and brings the buyer back a live demo.
- 07:06Confirm
Two-step checkout: agent proposes the cart, human one-taps to confirm. Pipeline attributed to the agent-driven motion.
- Day 2Expand
Usage signal triggers an expansion prompt. The agent surfaces the next plan; your team owns the relationship.
Human time per account: ~5 minutes of review. Agent time: ~2 minutes of execution. A traditional SDR spends 30–45 minutes on the same loop.
Ch. 09Measurement
Metrics that matter (and which module owns 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.
How many tokens an agent needs to recommend you
Across Claude/ChatGPT/Gemini/Perplexity
Pipeline attributable to agent-initiated motion
vs. $200–400 for a human SDR
Cold agent-driven outbound
Of conversations → booked meetings
Of MCP-instrumented paths an agent can finish
Human review of agent-generated artifacts
Ch. 10The math
The economics of an agent-led motion.
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.
On the Growth tier, a single replaced SDR headcount roughly funds the entire ALG360 subscription — before counting introduce-side wins, conversion lift, or expansion. The math is conservative; the compounding is not.
Ch. 11Anti-patterns
Six ways to do ALG wrong.
Bolting AI onto a broken GTM system
If your ICP is undefined and your pipeline stages don't reflect reality, agents will automate the chaos.
What ALG360 does: The Agent Readiness Scan maps the system before any agent touches it — free on the Starter tier.
Treating agents as standalone tools
An AI SDR tool in isolation isn't ALG — it's a silo with a chatbot.
What ALG360 does: ALG360 stitches Introduce, Evaluate and Execute into one signal layer.
No human-agent interface design
Without clear escalation, you get either too much oversight (defeating the point) or too little (damaging the brand).
What ALG360 does: Execute ships the governance and consent layer, not just the bots.
Optimizing for volume over quality
A 2% error rate at 500 messages/day is 10 brand-damaging interactions every day.
What ALG360 does: Quality score is a tier-1 metric in the ALG360 product.
Ignoring the recommendation moat
If you only ship outbound agents, you compete on outbound efficiency forever.
What ALG360 does: Introduce + Evaluate build the recommendation moat first — Execute compounds it.
Measuring activity instead of outcomes
'The agent sent 2,000 emails this week' isn't a metric. '14 qualified meetings at $35 each' is.
What ALG360 does: ALG360 reports outcomes by default; activity is diagnostic only.
Ch. 12The path
How to get started.
You don't rebuild the motion overnight. ALG360 is software you adopt in three steps — scan, ship, wire — designed to compound. Every team starts with the free Agent Readiness Scan.
Run the Agent Readiness Scan
Sign up and run the scan on your domain. You'll see exactly where you're being missed in Claude, ChatGPT, Gemini and Perplexity — and the highest-leverage fixes to ship first.
Ship Introduce + Evaluate
Track agent retrieval weekly, build your agent-readable evidence library, and watch token-to-value drop across your competitive set. Continuous scoring as your competitors ship.
Wire Execute
Turn on MCP / API handoff, agent-native trial endpoints, and agent-confirmable checkout. The buyer's agent transacts with you instead of around you — and the recommendation moat compounds.
Ch. FAQFrequently asked
The questions every team asks.
Trusted by post-PMF B2B teams
Get picked by the buyer's agent.
Start with the free Agent Readiness Scan. You'll see exactly where you're being missed in the model, where token-to-value is bleeding, and the highest-leverage motion to ship first.
