For years, AI has been talked about in terms of narrow tasks and statistical predictions. But that’s changing fast. With the rise of large language models and increasingly capable autonomous systems, we’re entering a phase where AI is not just generating outputs but making decisions and taking actions. The shift toward agentic AI, systems that can plan, execute and adapt, is no longer confined to academic papers or open-source demos. It’s happening now, and it’s reshaping how teams build B2B software.
This transition brings enormous potential, but it also comes with real operational and philosophical questions. What should agents handle on their own, and what needs a human in the loop? How do you build trust in a system that acts without asking? What’s the infrastructure required to make this all viable at scale?
These are questions I’ve had to wrestle with directly. While working at my previous start-up, I was tasked with building browser automation agents to enrich data across dozens of websites. On paper, the agent was simple — visit a URL, extract information and upload it. In reality, it broke constantly. Some sites would change layout mid-week. Others would throttle or fail silently. We learned quickly that autonomy isn’t about doing a task once; it’s about doing it reliably, recoverably and without losing context when things fall apart. What surprised me most wasn’t how smart the agent was, but how much infrastructure it needed just to act like it was smart. Early versions broke often, or returned inconsistent results, especially when the context was lost across retries. Overcoming this wasn’t just a modeling problem ɘ— it was an engineering challenge involving memory management, fallback flows and more precise user control.
From Features to Workflows: A New Developer Mindset
Traditional software development in B2B has centered around building discrete features. Need a reporting tool? Build a dashboard. Need lead scoring? Build a rule-based system. With agentic AI, that frame starts to break down. Developers are no longer thinking about isolated capabilities. They’re thinking in terms of orchestrated workflows — how an agent can observe, reason and take action across multiple systems — without constant user input.
This changes the way teams scope work. Instead of designing screens and buttons, they’re defining goals, triggers and boundaries. An agent might be tasked with managing a campaign budget or responding to customer tickets based on priority and tone. The product becomes more about the outcomes the system can drive and less about how it achieves them.
This shift highlights something I’ve learned the hard way — you don’t build AI instead of software, rather, you build it into software. It’s not a layer you sprinkle on top. It’s a mindset change, where the agent becomes part of the nervous system of your product. You stop thinking in terms of buttons and APIs and start thinking in terms of intentions, outcomes and what the system should understand. The more embedded the agent becomes, the more invisible the “AI” part starts to feel and that’s a good thing.
That also changes how value is measured. Success isn’t just about feature adoption. It’s about whether the agent actually achieved a result, closed a ticket, improved ROI or reduced time to resolution. This requires deeper instrumentation and new types of metrics. It means that teams need to get comfortable with outputs that aren’t always deterministic.
In one of the tasks I worked on, we replaced a manually scheduled lead enrichment process with an autonomous agent that used web search, LinkedIn data and CRM context to fill in missing company and contact details. Instead of clicking through UIs and exporting CSVs, users could drop in a domain list and let the agent decide what to enrich and when. The agent could take a list of domains, crawl relevant pages, infer missing fields and pipe the results back into our system. This taught us that agentic workflows weren’t just faster, but also reduced mental load, created more consistent results and let users focus on higher-value tasks. Overnight, we went from “click-heavy busywork” to “send and forget.” But it also reframed our job. Suddenly, we were asking: Should the agent enrich aggressively or conservatively? How much is too much automation? It wasn’t just about speed but also about designing a system we could trust, and that meant thinking through failure cases and edge behavior upfront.
B2B Marketing Platforms Make the Ideal Testbed
While the hype often centers on consumer tools or creative applications, B2B marketing is proving to be an ideal environment for agentic AI. These platforms deal with well-structured data, repeatable processes and measurable goals. They also operate across multiple systems, such as CRM, email, ad platforms and content management systems, which create the perfect ecosystem for agent orchestration.
Marketing teams often juggle a dozen repetitive tasks — scheduling posts, reallocating budgets, adjusting bids and optimizing headlines. These are precisely the types of workflows that agentic AI can take over, provided the right constraints and feedback mechanisms are in place. Even semi-autonomous systems, where the agent suggests actions, but a human approves them, can save hours of manual work.
What’s even more promising is how quickly teams begin to trust the agent. When they see it making smart recommendations or catching things they missed, adoption grows naturally. But it’s not always smooth. If the agent moves too fast or acts without transparency, users will disengage. That’s why the human-agent interface is critical. Trust is built not just on accuracy, but on clarity and control.
At one point, we ran an experiment where an agent suggested optimized subject lines for outbound email campaigns based on historical performance and persona matching. Adoption was initially slow as people hesitated to trust the AI’s tone. Some didn’t like it, others just didn’t trust it. But when we showed a side-by-side comparison of human vs. agent-generated lines and the agent’s success rate, confidence grew. The key insight was that explainability — why a subject line was chosen — mattered just as much as the result. This shaped how we designed agent UIs going forward. We added a simple UI showing what signals the agent used, which personas it targeted and why it made certain choices. One marketer told us, “I don’t need it to be right all the time. I just want to understand its logic.” That stuck with me. Autonomy is earned, not assumed.
Early Lessons and Infrastructure Gaps
Prototypes teach fast. Some things work right away. Agents that handle prioritization, clean up messy data or auto-generate variations of content often create immediate value. But others fall short. Complex decision trees or agents that lack context tend to produce shallow results. And anything involving emotion, tone or brand judgment usually still needs a human in the loop.
One lesson is clear — human feedback is still essential. Even if the agent is technically autonomous, it improves dramatically when paired with structured input from real users, which could be as simple as a thumbs up or down, or as rich as letting users adjust parameters and see how the agent adapts. The tighter the feedback loop, the better the results.
Another lesson is that infrastructure matters more than people expect. Agentic systems introduce new requirements. They need low-latency environments to stay responsive. They need persistent memory to track context across sessions. They need fallback logic when things break. And they need interfaces that let users understand what just happened and why.
The biggest issues companies face are about resilience. Things like observability, fallbacks and state management become the real engineering challenges. Agents can only act confidently if they know what just happened, where they are in a flow and what to do when something fails. Without that, even a great model becomes a liability. Early on, we were tempted to go fast. Wire up the agent, connect it to a few APIs and ship. But it didn’t take us long to realize that we needed a stronger foundation. So, we paused and built memory persistence, recovery logic and tools to trace agent behavior step by step. Those weren’t shiny features, but they made all the difference when things got real in production. You can’t fake reliability.
Many teams are building agents without first investing in these control layers. This creates a risk of hallucination, lost context or agent drift. It also limits the complexity of what agents can do. For agentic AI to truly scale in B2B, product and engineering teams need to think about orchestration layers, observability and safeguards, not just models.
One challenge we faced was managing memory and context across multiple asynchronous agent steps. The first time one of our agents forgot what it had done two steps before and made the wrong decision because of it, we realized we were missing something fundamental — memory. To fix this, we built a persistent memory layer that stored the agent state between steps, along with a context summarization module that prevented memory bloat. We ended up building a memory layer that let agents persist state and refer to previous contexts, plus a “Thoughts Panel” that exposed their internal reasoning to users. The funny part? It wasn’t just for debugging. People actually started using it to learn how the agent thought. This not only improved results but helped users trust that the agent was thinking clearly. One product manager called it “like reading the agent’s mind.” That transparency turned confusion into curiosity and helped us move from fragile demos to something users could actually rely on.
Agentic AI is becoming a real tool in the B2B builder’s toolkit. However, realizing its potential requires a shift in mindset from features to flows, from control to collaboration and from deterministic outputs to adaptive systems.
It also requires hard-won lessons from real-world use. Trust takes time. Infrastructure needs investment. And even the smartest agent still benefits from a smart human partner.
The funny thing is that people don’t actually want to give up control. They just want better control. They want agents to handle the grunt work, but still be able to peek in, override or steer things when it matters. The more we leaned into that, the more adoption we saw. Agentic systems that honor that balance — autonomy with oversight — are the ones that actually stick.
We’re still in the early days, but those who are quick to adapt and iterate will find that agentic AI changes not only the products they deliver but the way they operate.
