Unless you’ve been living under a rock (which, no judgement if so), you've probably heard a lot about AI agents lately. Maybe you caught that piece from The VC Corner about the agentic revolution. If not, the TL;DR is this: we're moving beyond AI tools that just respond to prompts, and toward AI agents that can take autonomous action on your behalf, executing complex, multi-step tasks with minimal human supervision.
This isn't just another AI buzzword. I feel like this is happening whether we want it to or not. And it gets me thinking about what this shift means for us in demand gen. It feels like this represents nothing short of a revolution in how we'll identify, engage, and convert prospects into pipeline.
While everyone's focus today is on nailing prompt engineering and content generation, the real transformation is going to come from AI agents that can orchestrate entire demand engines end-to-end.
👋 Hi, it’s Kaylee Edmondson and welcome to Looped In, my newsletter exploring demand gen and growth frameworks in B2B SaaS. If you’re one of the 83 people that have subscribed since last Sunday, hello! So glad you’re here—you’ve just joined 2k+ marketers who read Looped In every Sunday .
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The Problem with Today's Demand Gen Approach
Let's be real. Even the most sophisticated demand gen teams today are struggling with the same fundamental challenges:
1. Signal saturation without clarity - I wrote recently, "We're drowning in signals but starving for conversions." We have more intent data than ever, but struggle to turn that data into action.
2. Disconnected tools and workflows - The average B2B marketing team uses 20+ tools, creating data silos and forcing manual orchestration across systems.
3. The "human bottleneck" - Despite automation, humans still need to set up, monitor, and optimize campaigns, creating an execution ceiling.
4. Reactive rather than predictive - Most teams wait for signals to appear rather than predicting when and where buying intent will emerge.
The common thread? All these challenges come down to orchestration limitations. And that's exactly what agentic AI is going to be designed to solve.
From Signal Collection to Signal Action
Remember my orchestra analogy from last month? Excessive, but true.
"Like a symphony orchestra, achieving GTM Alpha requires coordinating multiple elements that individually make noise but together create harmony."
But here's where that analogy breaks down in today's world: demand gen leaders are forced to be conductors who also have to play multiple instruments simultaneously. It's impossible to do well.
Agentic AI changes this. An AI agent doesn't just identify signals - it orchestrates responses across your entire GTM motion without human intervention for every step.
Take one client I worked with recently. Their team was tracking over 200 different signals across 15 tools. The problem wasn't data collection - it was translating those signals into coordinated action. They had the instruments, but no conductor.
With agentic AI, this changes fundamentally. Instead of humans trying to connect all the dots, the AI agent:
Continuously monitors all signal sources
Identifies high-value signal combinations in real-time
Executes personalized, multi-channel responses
Learns which patterns drive conversion
Proactively adjusts strategies based on results
This isn't just incremental improvement - it's creating a step-change in what's possible.
What Agentic Demand Gen Will Look Like in Practice
I can already hear some of you saying, "Sure, Kaylee, but what does this actually look like in practice?" Fair question! Let's get specific about how AI agents will transform key demand gen functions:
1. Signal Orchestration Agents
Instead of manually prioritizing signals, you'll have agents that:
Continuously analyze signal combinations across your entire tech stack
Identify prospect-specific buying journeys and determine the exact moment to engage
Autonomously execute personalized, multi-channel plays based on a prospect's unique behavior
Dynamically adjust your signal scoring model based on what's actually converting
2. Account Intelligence Agents
Account-Based GTM becomes truly account-based (not just account-targeted) with agents that:
Autonomously research each target account, building comprehensive records through web scraping and existing databases
Identify the complete buying committee and their relationships to each other
Map company initiatives and priorities to your solution
Proactively alert you to trigger events and competitive threats
Draft personalized outreach that directly addresses known pain points
The game-changer: These agents won't just provide static intelligence reports - they'll continuously update their understanding of accounts and autonomously adjust your engagement strategy as new information emerges.
3. Content Orchestration Agents
Content stops being a manual bottleneck with agents that:
Analyze engagement patterns to identify content gaps in your library
Generate personalized content tailored to specific accounts and buying stages
Autonomously test multiple messaging angles and optimize based on engagement
Create personalized assets on-demand for high-value accounts
Distribute content through optimal channels based on target persona behavior
Real-world example: An enterprise SaaS client is piloting a content agent that monitors competitor messaging, identifies gaps or weaknesses, and autonomously creates and deploys counter-messaging within hours, not weeks. This has reduced their content production cycle by 80% while increasing engagement by 37%.
4. Campaign Optimization Agents
Campaign management will transform from reactive to predictive with agents that:
Can continuously test creative elements, timing, channels, and offers
Identify optimal channel mixes for different segments based on historical performance
Proactively shift budget to high-performing channels in real-time (no more logging in on a Sunday to reallocate the budget mix — dreams 🙌)
Diagnose campaign underperformance and implement fixes
Scale successful plays across segments with appropriate customization
The best part? These agents won’t be running in isolation - they'll be coordinating with each other, creating a unified GTM motion that no human team could match in terms of speed, precision, and scale.
The Demand Gen Architect: How Humans Will Work With AI Agents
So what does this mean for us as demand gen marketers? Are we being replaced?
Absolutely not. Or at least not as I see it. But our role is fundamentally changing.
We're evolving from campaign executors to agent orchestrators. The skills that will matter most aren't prompt engineering or tool mastery - they're system design, strategy development, and business acumen.
Let me be very clear:
The demand gen leaders who will thrive in this new era aren't those with the best tactical skills, but those who can design the most effective agentic systems and align them with real business objectives.
Think about it like this: instead of being the person who executes campaigns, you'll be the architect who designs the entire growth system and the strategist who determines which business outcomes to optimize for.
Despite all the autonomy I've described, humans aren't becoming obsolete—we're becoming even more crucial, just in different ways.
Here's what this looks like in practice:
Setting Guardrails, Not Writing Rules
One client I'm working with has completely reimagined how they approach campaign governance. Instead of writing detailed campaign execution playbooks (which quickly become outdated), they now focus on defining clear guardrails for their agents:
- Brand voice parameters that agents must adhere to
- Budget thresholds that trigger human review
- Risk tolerance levels for different types of accounts
- Business metrics that agents are optimizing against
The architect doesn't need to specify exactly how to engage each account—the agent handles that. They just need to ensure the system operates within business parameters that align with company objectives.
Designing Agent Interactions
Perhaps the most fascinating aspect is designing how multiple agents interact with each other. One fintech startup I advise has created an agent ecosystem where:
- The signal detection agent flags accounts showing buying intent
- The research agent builds comprehensive account files
- The content agent prepares personalized materials
- The orchestration agent coordinates outreach across channels
The human architect doesn't manage each agent individually—they design the protocols for how agents communicate with each other and escalate to humans when needed.
"I spend almost no time reviewing individual campaigns anymore," their VP of Marketing told me. "Instead, I focus on optimizing the decision-making frameworks between agents and identifying blind spots in our system architecture."
Building the Agent Training Environment
Here's where the human element becomes truly irreplaceable. For AI agents to learn effectively, they need the right training environment. This includes:
- Curating historical campaign data for agent learning
- Defining success metrics that agents optimize for
- Creating feedback loops that accelerate learning
- Designing exception handling protocols
One enterprise SaaS company I work with has a dedicated "agent gym" where they simulate various market conditions and buying scenarios to train their agents before deploying them on live campaigns. Their team has evolved from campaign managers to simulation designers.
The New Human Skillset
This shift requires a fundamentally different skillset than what most demand gen professionals have developed:
- Systems thinking: Understanding how complex systems behave and interact
- Decision science: Knowledge of how to structure decision frameworks
- Experiment design: Creating effective testing environments
- Ethical judgment: Making value-based decisions that AI cannot
- Exception handling: Managing the unexpected cases AI can't address
"The biggest mindset shift," one CMO told me, "was moving from 'how do I execute better campaigns?' to 'how do I design better systems that execute campaigns?'"
A Practical Division of Labor
So what does this human-AI partnership actually look like day-to-day? Based on clients already implementing early versions of this approach:
The beauty of this division is that it plays to the strengths of both humans and AI. Humans handle the ambiguous, the novel, and the strategic, while AI manages the repeatable, the data-intensive, and the tactical.
My Boldest Predictions for Agentic Demand Gen
I promised to be bold (even if these predictions pan out to be dead-wrong), so here are my predictions for how agentic AI will transform demand gen over the next 3-5 years:
1. The death of the traditional campaign cycle - Static campaigns planned weeks in advance will be replaced by dynamic, agent-driven engagement that continuously adapts.
2. Hyper-personalization becomes the baseline - Every prospect will receive completely personalized experiences across all touchpoints, making today's "personalization" look laughably crude by comparison.
3. The collapse of channel silos - The distinction between paid, owned, and earned media will become meaningless as agents orchestrate unified experiences across all touchpoints.
4. The emergence of competitive agent warfare - As multiple companies deploy agents targeting the same accounts, we'll see agent-vs-agent competition, with the most sophisticated systems winning.
5. Demand gen team structures will be completely reimagined - Instead of channel specialists, teams will be organized around agent oversight, system design, and strategy.
6. GTM tech stacks will consolidate dramatically - Point solutions will be replaced by agent platforms that can interface with any data source or execution channel.
7. The metrics revolution - Pipeline velocity and volume metrics will be augmented by agent effectiveness metrics, with teams competing on automation rate and decisioning quality. ← Literally had my first client ask me to provide benchmarks for this last week. 😳
How to Prepare for the Agentic Revolution
So what should you do today to prepare for this shift? Well, first off I hope it’s abundantly clear that I’m no expert here. I’m trying to ride the wave alongside everyone else and am just trying to share out as much as I am learning week-to-week, but here are a few things I’m thinking about and building alongside clients.
1. Build your agent architecture now - Start mapping what an ideal agent-driven demand gen system would look like for your business. What decisions would agents make? What data would they need? What actions would they take?
2. Consolidate your signal sources- Agents need centralized access to all intent and engagement data. Break down data silos and create a unified data layer.
3. Document your decision-making logic - Before you can automate decisions, you need to understand how you currently make them. Document the rules, heuristics, and logic you use today.
4. Start with single-purpose agents - Don't try to build a comprehensive system overnight. Begin with agents focused on specific, high-value tasks like signal prioritization or content personalization.
5. Invest in agent orchestration skills - The demand gen leaders of tomorrow need to understand how to design, train, and manage AI agent systems. Start building these skills now.
Current Limitations: The Reality Check
Real quick. Last thoughts here. I promised to be bold about the future of demand gen, but I also want to be honest about where we are today. The agentic revolution is coming, but there are still significant hurdles to overcome before we reach the full vision I've described.
Technical Constraints
Let's talk about what today's AI agents can and can't do well:
What works today:
Pattern recognition in structured data
Content generation with human-defined parameters
Single-step task automation
Scheduled monitoring and alerts
What's still challenging:
True autonomous decision-making across multiple systems
Handling novel situations without human guidance
Understanding nuanced brand voice without extensive training
Maintaining contextual awareness across long sequences of actions
One marketing leader at a Series B SaaS company told me: "Our current agent can analyze intent signals and draft personalized outreach, but we still need humans to validate the decisions and refine the messaging. We're at 'AI-assisted' more than 'AI-autonomous' right now."
The Data Quality Problem
Even the most sophisticated AI agents can't overcome fundamental data limitations:
Many companies have fragmented data across disconnected systems
Historical campaign data often lacks proper tagging or structure
Attribution models remain imperfect, making it difficult for agents to learn what actually works
Customer data is frequently incomplete or outdated
I recently worked with a company that wanted to implement an AI agent for personalized outreach. Their biggest hurdle wasn't the AI technology but their data infrastructure. We realized we needed to spend six months just cleaning the data and connecting our systems before we could even start with any of the fun stuff.
Trust and Transparency Barriers
Perhaps the most significant current limitation is the trust factor:
Marketing leaders are hesitant to give AI agents control over significant budgets
Sales teams resist fully automated lead qualification and outreach
Teams struggle to understand why agents make certain decisions
There's anxiety about AI agents potentially damaging brand reputation
"We're facing a 'trust chasm' with our executive team," one demand gen director shared with me. "They love the concept of AI agents handling routine tasks, but the idea of agents autonomously making decisions about our largest accounts makes them deeply uncomfortable."
The Integration Reality
The vision I've painted assumes seamless integration across your GTM stack, but the reality is messier:
Most marketing tools have limited API capabilities
Each platform has different data models and terminology
Permission settings often block true cross-platform automation
Custom integrations require significant engineering resources
To Sum-It-All-Up
The shift to agentic AI isn't just another incremental advance in martech. It feels like the biggest shift I’ve seen in my professional career.
The demand gen teams that embrace this shift won't just be more efficient - they'll be playing an entirely different game. While competitors struggle with manual orchestration and human bottlenecks, agent-enabled teams will execute with a speed, precision, and scale that was previously impossible.
This isn't science fiction or far-future speculation. The technology exists today, and the early adopters are already seeing results that would have seemed impossible just a year ago.
The question isn't whether agentic AI will transform demand gen. It's whether you'll be leading that transformation or playing catch-up. Trust me, there will be so many that miss this wave entirely.
What's your take? Are you already experimenting with AI agents in your demand gen stack? What possibilities are you most excited about? Let me know in the comments!
See ya next week,
Kaylee ✌
Awesome read, Kaylee! Quick Q's,what’s running your “agent gym” (LangGraph/AutoGen DIY or a vendor stack)? And where would you point someone who wants to get hands-on with agent orchestration? Thanks!