Landbase is a cutting-edge AI-driven platform that empowers businesses to intelligently automate their go-to-market strategies, enhancing productivity and efficiency.

An AI-powered platform that combines outbound, inbound, and market intelligence to deliver a genuine omnichannel approach for go-to-market success.

Publication - i1mtX4ST2aXgdFUfb4evmvlrvp43

Hey there, and welcome back to another episode of our podcast! Today, we're diving into the world of AI-driven automation and how it's impacting go-to-market strategies. We're tackling a super important topic: building trust in agentic AI systems.

Landbase is leading the charge in AI-driven platforms, helping businesses automate their go-to-market strategies intelligently, boosting productivity and efficiency. I'm Ethan, and I'm here with my co-host, Sophia. Together, we're going to dig into the challenges and opportunities of agentic AI and how we can design these systems to earn trust from the get-go.

Thanks, Ethan! I'm excited to be here. So today, we're zeroing in on a big challenge for agentic AI: how do we get users to trust these systems enough to confidently click 'Start' when an AI agent is about to execute thousands of tasks for them?

Exactly, Sophia. Users are intrigued by AI's potential, but they're hesitant to just hand over control without some assurance. Surveys show that a lack of explainability and transparency is a MAJOR barrier to adopting AI. I mean, a McKinsey study even found that 40% of people pointed to explainability as a key concern.

Right, Ethan. Users and business leaders are understandably cautious about 'black box' AI that they can't predict or control. One study even found that just labeling something as 'AI' can make people less likely to use it. So, if we want agentic AI to really take off, we need to design these systems to build trust right from the start.

Let's dive into the four design priorities that'll help build this trust: presenting information at scale in a way that's intuitive, enabling fluid iteration and user feedback, balancing user control with AI autonomy, and framing AI tools as accessible. Sophia, why don't you kick us off with the first one?

Sure thing, Ethan. Presenting information at scale is all about giving transparent summaries that build confidence. When an AI agent like Landbase’s GTM-1 Omni is set to perform hundreds or thousands of actions, users need to see information-dense summaries and previews that inform without overwhelming them.

Absolutely, Sophia. Transparency in AI systems directly fosters trust, leading to better human-AI collaboration. For instance, a 'preview dashboard' can highlight key details of the agent’s plan, like the number of tasks, target audience segments, and expected duration. This helps users understand what the agent plans to do in a digestible format before it even starts.

Exactly. And by showing sample outputs, users can gauge the quality and tone of the AI’s work. Providing a concrete example helps set expectations and lets users vet the style. Projected outcomes, like predicted results or confidence metrics, also give valuable context on why the plan is beneficial.

That makes sense. At Landbase, our platform might summarize an autonomous campaign by stating how many contacts will be reached and showing samples of personalized emails, along with explanations. This kind of transparent preview reassures users that the agent’s scope and approach are visible upfront.

Moving on to fluid iteration, this is where users can easily tweak the agent’s behavior or outputs and immediately see the agent adapt. It’s like giving feedback to a junior colleague – if the first report isn’t quite right, you guide them on what to change for the next draft.

Right, Sophia. In agentic AI, we want a collaborative feel. The system might allow users to edit sample outputs or adjust parameters before fully launching. For example, if the tone of a sample outreach message is too formal, the user could adjust the tone setting and instantly preview a new sample.

Exactly. AI systems should continuously learn and improve from each interaction. By giving users a sense of control through iteration, we reassure them that they’re not stuck with whatever the AI produces on the first try. Instead, the AI becomes a partner that welcomes guidance.

That’s insightful, Sophia. From a trust perspective, fluid iteration shows humility and reliability. The AI isn’t a black box asserting it’s perfect; it’s a smart assistant willing to take feedback. This dramatically increases user confidence.

Now, let’s talk about balancing control and autonomy. This is about finding the sweet spot between user control and AI autonomy. If the user has to micromanage every step, we lose the efficiency that agentic AI promises. But if the AI runs unchecked, users worry about surprises or mistakes happening out of sight.

Exactly, Sophia. Establishing clear guardrails and oversight mechanisms can make users comfortable without requiring them to hand-hold the AI through each action. Think of it like a self-driving car – you want to enjoy the ride without constantly gripping the wheel, but you also want assurances that the car knows the rules of the road.

Right. For AI agents, guardrails can include things like requiring confirmation for high-stakes actions, setting constraints, and providing real-time monitoring dashboards. These controls let the user define the bounds of autonomy.

And equally important is conveying to the user that a human is always in the loop when needed. Even if manual intervention is rarely needed, just knowing it’s possible gives users peace of mind. Predictability and accountability breed trust.

Finally, democratization of AI is about making autonomy accessible to everyone. This means designing with approachability and empowerment in mind, so any user feels that this technology is for them.

Exactly. Democratization has always been a driving theme for us at Landbase. We want capabilities once reserved for huge enterprises to be accessible to small and medium businesses. But accessibility isn’t just about offering a free trial or a low price – it’s about user confidence.

And by building trust through transparency, iteration, and control, we lower the knowledge and psychological barriers. It’s like the early days of personal computing – the GUI made computers usable by billions, not just programmers.

Right. We’re at a similar juncture with AI. Agentic AI must come with a human-centric design that demystifies autonomy. This includes using familiar metaphors and language, and educating users gently as they go.

Exactly, Ethan. Building user trust isn’t just a UX exercise – it’s part of a broader mission to democratize advanced AI tools. By designing for inclusivity and clarity, we ensure that democratization isn’t just a promise, but a reality.

As we wrap up, remember that when users trust an AI agent, they’re more likely to use it. And when they use it, they gain tremendous efficiency and capability. It’s our job as AI builders and designers to earn that trust through thoughtful design and rigorous transparency.

Absolutely. By treating AI agents not as magical black boxes but as collaborative partners, we bridge the gap between automation and trust. We turn a scary 'unknown' into a governed, auditable, and ultimately reliable ally in our workflow.

Thanks for joining us today. We hope you found this discussion insightful. Consider how these insights might reshape your approach to AI in your business. Until next time, keep exploring and innovating.

How do we build enough trust for users to confidently click “Start” when an AI agent is about to execute thousands of tasks on their behalf? This question captures a core challenge for agentic AI (autonomous AI systems): users admire the potential, but they hesitate to hand over the reins without assurance(1). In fact, surveys show that lack of explainability and transparency is a top barrier to adopting AI – in one McKinsey study, 40% of respondents cited explainability as a key concern(3). Users (and business leaders) are understandably wary of “black box” AI they can’t predict or control. One study even found that simply labeling a product as “AI” can reduce people’s willingness to use it(1). Clearly, if we want the next wave of AI – agentic AI – to deliver on its promise, we must design these systems in a way that earns user trust from the outset.
I believe building this trust comes down to four design priorities: presenting information at scale in an intuitive way, enabling fluid iteration and user feedback, balancing user control with AI autonomy, and framing AI tools as accessible (democratizing them for all users). By focusing on these areas, we can turn apprehensive users into empowered co-pilots of AI agents. Let’s explore each of these focus points and how they help get users to click “Start” with confidence.
Information at Scale: Transparent Summaries Build Confidence
When an AI agent like Landbase’s GTM-1 Omni is set to perform hundreds or thousands of actions – sending emails, updating records, researching prospects, etc. – no user wants to read a log of every single step. Yet, they also don’t want to fly blind. The solution is to provide information-dense summaries and previews that inform without overwhelming. Studies indicate that transparency in AI systems directly fosters trust, leading to better human-AI collaboration(2). In practice, this means showing users what the agent plans to do in a digestible format before it does it.
How might this look in a user interface? One approach is to present a “preview dashboard” before execution, highlighting key details of the agent’s plan: for example, “This AI will send ~5,000 personalized emails targeting [Industry] prospects over the next 2 weeks”. We can include:
Summaries and Stats: High-level stats (e.g. number of tasks, target audience segments, expected duration) give a bird’s-eye view.
Sample Outputs: A few example outputs (like one draft email or a snippet of an outreach message) let the user gauge quality and tone. Providing a concrete example of the AI’s work helps set expectations and allows the user to vet the style.
Projected Outcomes: Where possible, show predicted results or confidence metrics (e.g. “Projected open rate: 45% based on similar campaigns”). This provides context on why the plan is valuable.
At Landbase, for instance, our platform might summarize an autonomous campaign by stating how many contacts will be reached and even show samples of personalized emails, along with explanations. This kind of transparent preview assures the user that nothing “crazy” will happen – the agent’s scope and approach are visible upfront. By explaining its reasoning and decisions at each step, an agent makes its thought process transparent and debuggable, which in turn builds user trust(1). In fact, others building AI agents have noted that users will not trust an AI agent unless they can follow along and audit its work – especially the first few times(1). Early on, giving users an easy way to review what the agent will do (and even step through initial actions) is critical. Over time, as the AI proves itself, users need to check every step less and can lean on the summaries. The goal is a UI that answers the user’s unspoken questions: “What is this agent about to do, and why?” at a glance.
Even with the best upfront summaries, there will be times when the AI’s output isn’t exactly what the user envisioned. Perhaps the tone of those draft emails is slightly off-brand, or the targeting criteria need adjusting. Rather than this becoming a trust-breaking moment, we should design for it to be an opportunity for quick correction – a way for the user to teach the agent and see it improve. This is where fluid iteration comes in.
Fluid iteration means the user can easily tweak the agent’s behavior or outputs and immediately see the agent adapt. Think of it as giving feedback to a junior colleague: if the first report isn’t right, you guide them on what to change for the next draft. In agentic AI, we want a similar collaborative feel. Concretely, the system might allow the user to edit the sample outputs or adjust parameters before fully launching. For example, if the sample outreach message is too formal, the user could adjust the tone setting (“make it more casual”) and instantly preview a new sample. Or if the targeting seems off (“hmm, these contacts are mostly in finance, I wanted tech”), the user can refine the criteria and have the agent update its plan on the fly.
AI systems should continuously learn and improve from each interaction. On the backend, an advanced agentic system like Landbase’s GTM-1 Omni is already doing this: it monitors outcomes and refines its strategy via reinforcement learning, so each wave of emails gets smarter based on responses(4). But the front-end user experience needs a similar loop for human feedback. We must empower users to say “this isn’t quite right” and have the agent respond immediately with a better attempt. By giving users a sense of control through iteration, we reassure them that they’re not stuck with whatever the AI produces on the first try. Instead, the AI becomes a partner that welcomes guidance. This dramatically increases confidence – the user knows that if the agent veers off course, they have the tools to course-correct quickly.
From a trust perspective, fluid iteration also demonstrates humility and reliability: the AI isn’t a black box asserting it’s perfect; it’s more like a smart assistant willing to take feedback. In practice, this might be as simple as a “Refine” button next to an output or an interactive slider to adjust the creativity level of content generation. The key is a low-friction feedback loop. When users see that they can iteratively steer the agent to better results, they become far more comfortable letting it handle large tasks. In essence, we’re answering the user’s concern: “What if it’s not right?” with the solution: “Then we’ll fix it together, quickly.”
Balancing Control and Autonomy: Guardrails without Micromanagement
A classic trust dilemma in autonomous systems is finding the sweet spot between user control and AI autonomy. On one hand, if the user must micromanage every step the agent takes, we lose the efficiency and “minutes, not months” speed that agentic AI promises. On the other hand, if the AI runs off completely unchecked, users worry about surprises or mistakes happening out of sight. The answer lies in establishing clear guardrails and oversight mechanisms that make the user comfortable, without requiring the user to hand-hold the AI through each action.
Think of this like a self-driving car. You want to enjoy the ride without gripping the wheel constantly, but you also want assurances that the car knows the rules of the road and will alert you if something needs your intervention. For AI agents, guardrails can include things like: requiring confirmation for high-stakes actions, allowing users to set constraints (e.g. “don’t email contacts from XYZ company”), and providing real-time monitoring dashboards. In our context at Landbase – an AI for go-to-market – a guardrail might be a budget cap on ad spend the agent cannot exceed, or a rule that it should pause and ask if reply rates drop below a threshold. These controls let the user define the bounds of autonomy.
Equally important is conveying to the user that a human is always in the loop when needed. As one AI architect put it, the AI should operate within a secure perimeter with a human ready to intervene as a fail-safe(1). Even if manual intervention is rarely needed, just knowing it’s possible gives users peace of mind. We should design interfaces that surface what the agent is doing in understandable terms and log its decisions for audit. If an agent can explain why it’s taking a certain action (“I’m contacting these leads because they match the target profile and have shown interest recently”) and if the user can halt or adjust the process at any time, the trust barrier lowers significantly.
In short, predictability and accountability breed trust. Users need to feel that the agent is reliable and behaves within expectations. Research on trustworthy AI emphasizes that an AI agent should be auditable and override-able, not a mysterious box(1). Even a highly accurate AI will make people uneasy if it offers no insight or control over its actions(1). By contrast, if we share the rationale behind decisions and give users an emergency brake, the AI transitions from a risk to a partner. In designing Vibe (Landbase’s autonomous campaign tool), this balance has been a guiding principle – Vibe aims to handle the heavy lifting of, say, prospecting autonomously, but always in a way that the user understands what’s happening. For instance, Vibe might autonomously sequence a multi-channel campaign, but it will visibly report progress (“200 emails sent today, 47% opened”) and flag any anomalies. The user isn’t controlling each email, but they are kept in the loop, building trust that the agent is competent and under watch. Over time, just as experienced drivers grow to trust cruise control on the highway, users will trust their AI agents – because they know the guardrails are there if needed.
Democratization of AI: Making Autonomy Accessible to Everyone
Finally, building user trust is not just a UX exercise – it’s part of a broader mission to democratize advanced AI tools. If only a select group of tech experts or large enterprises feel comfortable using agentic AI, we haven’t truly unlocked its potential. We want any user – whether a solo entrepreneur or a sales rep at a mid-size company – to feel this technology is for me. That means designing with approachability and empowerment in mind.
Democratization has always been a driving theme for us at Landbase. One of our mottos is to “reclaim your day” by letting AI handle the drudgery, so anyone can grow their business without needing a massive team or budget. In positioning Landbase, we highlight that capabilities once reserved for huge enterprises with multi-million-dollar tech stacks are now accessible to small and medium businesses. But accessibility isn’t just about offering a free trial or a low price – it’s about user confidence. The most powerful AI features mean little if a user is too intimidated to use them. Therefore, building trust through transparency, iteration, and control (the things we discussed above) is building accessibility. It lowers the knowledge barrier and psychological barrier.
Consider the early days of personal computing: the GUI (graphical interface) was what finally made computers usable by billions, not just programmers. We’re at a similar juncture with AI. Agentic AI must come with a human-centric design that demystifies autonomy. This includes using familiar metaphors and language (no unnecessary jargon), and educating users gently as they go. For example, in our product we avoid internal code names or acronyms when explaining what the AI is doing; we say “writing emails” instead of “executing sequence sub-task 4”. We might include tooltips or a guided tour for new users to show, here’s how you can tweak the agent’s actions. The tone is empowering – you are in charge of this powerful tool, and you can trust it just as you trust a well-trained team member.
Crucially, democratization is also about freeing the user from drudgery without losing quality. When users see that an autonomous system can handle tedious tasks and still deliver great results, they’ll embrace it. Landbase’s agentic model, powered by GTM-1 Omni, has been trained on 40M+ B2B campaign interactions and 175M+ sales conversations, meaning it carries a wealth of knowledge that even a large team would struggle to match(5). The user might be a one-person marketing team, but with an AI agent at their side, they suddenly have the “sophistication of a world-class GTM strategy, without complexity”. Knowing the AI is backed by such rich experience (and seeing it in action through transparent results) builds institutional trust. It reframes the technology from a risky leap of faith to an evolution of everyday tools. In other words, we present agentic AI as simply the next step from automation to true autonomy – and we do so in plain language that invites everyone in. Landbase’s VibeGTM interface, for instance, is pitched as “launch B2B campaigns in minutes, not months”; the focus is on outcomes and ease, signaling that you don’t have to be an AI guru to benefit. By designing for inclusivity and clarity, we ensure that democratization isn’t just a promise, but a reality – users from all backgrounds can trust and harness agentic AI.
Closing the Trust Gap: The Path Forward
When users trust an AI agent, they will use it – and when they use it, they stand to gain tremendous efficiency and capability. It’s our job as AI builders and designers to earn that trust through thoughtful design and rigorous transparency. We’ve seen that providing clear information at scale, enabling quick user iterations, and maintaining sensible control can convert skepticism into confidence. And when users feel confident, they click “Start” – not because they’re taking a gamble, but because they understand and believe in what the AI will do for them.
The primary obstacle to adopting autonomous AI isn’t the technology itself; it’s the deficit of trust(1). The good news is that this is a solvable problem. By treating AI agents not as magical black boxes but as collaborative partners that come with manuals, gauges, and feedback loops, we bridge the gap between automation and trust. We turn a scary “unknown” into a governed, auditable, and ultimately reliable ally in our workflow. As one report aptly noted, to capture AI’s value, organizations must build trust – after all, if people don’t trust the outputs, they won’t use them(3).
I’m optimistic that we are on the right track. Every day, we are seeing more best practices emerge – from explainability toolkits to user-centric UX research – all reinforcing the idea that transparency and user empowerment are key. At Landbase, we will continue applying these principles as we pioneer agentic AI for go-to-market teams. The broader vision is clear: an era of true autonomy where AI handles complexity in the background, and users reap the benefits without fear.
The question now isn’t whether users will trust agentic AI, but how we as an industry will continue to shape that trust. By building AI systems that are as transparent, accountable, and easy to interact with as they are intelligent, we can help anyone embrace this technology. The next time you see that “Start” button next to an AI-powered campaign, we want you to feel excited – not anxious – about clicking it.

Building User Trust in Agentic AI: How to Get Users to Click “Start”

August 21, 2025 • Agentic AI in Action

Read Article

About Agentic AI in Action

https://www.landbase.com

Subscribe to Agentic AI in Action Podcast