The Agentic AI Deep Dive You've Been Waiting For

Hello! For the next month or so, this newsletter will run as a focused series. I’m taking Andrew Ng’s Agentic AI course with the goal of creating a practical PMM workflow you can actually use. Each week, I’ll recap what I learned and build toward a complete agentic workflow for product marketing. If you don’t have time for the course but want the takeaways, this series is for you.

This is why I moved to NYC.

Sunday morning, I grabbed coffee with Maja Voje, a marketing legend whose GTM method is used by 9,500+ companies. She’d just flown in from Europe for a Miro event and her first-ever trip to New York… and still made time to meet.

What struck me most wasn’t just her track record; it was her mindset. While a lot of folks chase AI hype, Maja treats AI like any other tool: useful only when it moves the business.

Two takeaways I’m keeping (there are lots more in my notes app):

1️⃣ Start with the problem, not the model. If it doesn’t change a KPI, it’s a toy.

2️⃣ Document the workflow. Repeatability beats one-off wins.

3️⃣ Complexity doesn't create value; simplicity does, especially with AI.

If you’re in GTM, subscribe to her newsletter. She’s on the edge of AI-in-GTM workflows, but always grounded in reality. I learn something new in every edition.

Thank you, Maja, for the grounding conversation and the reminder to be my own advocate. 😊

Each week, the format will follow the latest Agentic AI course syllabus from DeepLearning.AI, taught by Andrew Ng (an AI legend), recapping what I learn each week through a product marketing lens and showing you what I’m building along the way.

Here’s what will be covered:

I’m not going to make you wait a whole other week to dive in. Let’s get started NOW!

Let’s define what an agentic workflow is:
Agentic workflows are a process where an LLM-based app executes multiple steps to complete a task. Think apps such as Clay, which find and aggregate prospects through web-scraping for TAL (Target Account List) creation and GTM endeavors.

Why is there controversy around what an “AI Agent” actually is?
In the AI community, there’s been a lot of debate and controversy around what an AI agent actually is, which is just adding to the general confusion and hype-driven noise. That’s why the term “Agentic AI” is being adopted more because it’s not a binary term but instead an adjective and acknowledges that systems can be agentic to various degrees.

1) Better outputs through iteration

Why it matters: A single prompt is like a first draft. Agentic workflows plan → draft → critique → refine. That built-in feedback loop reliably improves quality.

What changes in practice

Takeaway: Expect clearer structure, fewer errors, and results that feel “finished,” not first-pass.

Plain English: Run steps at the same time, not one-by-one.

Example: Instead of searching one site after another, an agent queries multiple sources in parallel, then merges and de-dupes findings.

Where this helps

Takeaway: Same total work, less wall-clock time.

Idea: Agentic workflows are modular. You can swap components to explore what works best—without rebuilding the whole thing.

Example (prospecting with Clay):
Choose which data sources Clay pulls from (company tech stack, hiring signals, firmographics) to assemble a richer list—vs. relying on one silo. Test variants, keep the winner.

Takeaway: Faster learning cycles and steadily improving results.

1) Message-Market Fit Explorer

What: Generate, test, and refine value props by segment.
Agentic loop: Plan segments → draft 3–5 variants → score vs. ICP pains & proof → revise → pick winner.
Inputs: ICP notes, customer quotes, objections, proof points.
Output: Ranked messaging slate per segment.
KPI: Increase in message pull-through (CTR on top-funnel assets, win-rate lift in target segment).

2) Competitive Intel Synthesizer

What: Always-fresh battlecards.
Agentic loop: Parallel web/doc scan → extract claims → fact-check → contrast vs. your differentiators → build one-pager → schedule refresh.
Inputs: URLs, PDFs, analyst notes, call transcripts.
Output: Crisp battlecards with traps/counters.
KPI: Sales usage & influenced win rate in head-to-head deals.

3) Persona Deep-Dive Builder

What: Turn calls, CRM notes, and surveys into living personas.
Agentic loop: Ingest → cluster pains/jobs → map outcomes → validate against win/loss → iterate.
Inputs: Gong/Zoom transcripts, win-loss notes, survey data.
Output: Persona sheets: jobs-to-be-done, triggers, landmines, proof.
KPI: Pipeline contribution from ICP-aligned campaigns.

4) Narrative & Story Arc Generator

What: Converts features → outcomes → strategic narrative.
Agentic loop: Map pains → “from/to” arc → proof mapping → exec-style storyboard → tighten.
Inputs: Feature list, customer outcomes, case studies.
Output: 10-slide narrative backbone for decks/webinars.
KPI: Deck adoption; average meeting progression rate.

5) Win/Loss Insights Miner

What: Turns messy notes into clear, prioritized themes.
Agentic loop: Transcribe → label reasons → quantify → surface “if/then” plays → refresh monthly.
Inputs: Call transcripts, CRM close codes, survey results.
Output: Top 5 reasons to win/lose with actions.
KPI: Reduction in repeatable loss reasons; win-rate delta QoQ.

6) Content Repurposing Assembly Line

What: Atomizes a flagship asset into many formats.
Agentic loop: Identify slices → draft by channel → enforce brand/voice → A/B hooks → schedule.
Inputs: Whitepaper/webinar/case study.
Output: Blog, email, social, 1-pager, speaker notes.
KPI: Content output velocity; sourced MQLs.

7) Proof Point & Reference Hunter

What: Finds the right proof for each claim automatically.
Agentic loop: Parse claims → search internal DB/cases → match ICP → draft snippet → legal check.
Inputs: Case study library, review quotes, analyst notes.
Output: Claim-to-proof matrix with approved snippets.
KPI: Time-to-approve assets; evidence usage in sales.

8) Pricing & Packaging Signal Scanner

What: Surfaces buyer-value signals to inform pricing/tiers.
Agentic loop: Ingest usage + win/loss → cluster value moments → propose tier levers → simulate scenarios.
Inputs: Product analytics, deal notes, churn reasons.
Output: Hypotheses on fences, add-ons, value metrics.
KPI: ARPU lift; attach rate; discount reliance ↓.

9) Sales Enablement Personalizer

What: Tailors decks/emails to account context.
Agentic loop: Pull firmographics/tech stack/news → select narrative path → slot relevant proof → QA.
Inputs: Account list, vertical, recent news, case library.
Output: Account-ready deck + email + talk track.
KPI: Meeting acceptance; stage-to-stage conversion.

10) Demo Script & Objection Coach

What: Creates role-specific demo flows and live counters.
Agentic loop: Map role → pick pains → sequence features → embed objection/counter pairs → rehearse.
Inputs: Feature map, objection library, persona pains.
Output: Demo script + “if they say X, show Y” matrix.
KPI: Demo-to-opportunity rate; close velocity.

11) Partner & Ecosystem Co-Marketing Builder

What: Finds partner overlaps and spins up joint plays.
Agentic loop: Cross-match ICPs → propose angles → compile shared proof → draft campaign kit.
Inputs: Your ICP, partner ICP, mutual customers.
Output: Joint value prop, landing copy, outreach pack.
KPI: Sourced pipeline from partner-influenced deals.

These are some examples of what we could possibly build with agentic AI, stay tuned 👀

Agentic Workflow: a process where an LLM-based app executes multiple steps to complete a task.

Task Decomposition: The process of breaking down a task into individual steps for the purpose of creating an agentic AI workflow

As always, stay curious and have fun!

Best,
Skyler Neal