User trust in autonomous booking systems

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. 𝐋𝐞𝐭 𝐦𝐞 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 👇 We were working on improving product adoption for a US-based platform. Most founders would instinctively look at cutting down clicks and removing steps in the onboarding journey. Faster = Better, right? That’s what we thought too—until real usage patterns showed us something very different. Instead of shortening the journey, we tried something counterintuitive: -We added more decision points -Let the user customize their flow -Gave options to manually choose settings instead of setting defaults And guess what? Conversion rates went up. Engagement improved. And most importantly—user trust deepened. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐈 𝐫𝐞𝐚𝐥𝐢𝐬𝐞𝐝: You can design a sleek 2-click journey…  …but if the user doesn’t feel in control, they hesitate. Especially in the US market, where data privacy and digital autonomy are hot-button issues—transparency and control win. 𝐒𝐨𝐦𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭 𝐭𝐨 𝐦𝐞: → People often disable auto-fill just to manually type things in.  → They skip quick recommendations to do their own comparisons.  → Features that auto-execute without explicit confirmation? Often uninstalled. 💡 Why? It’s not inefficiency. It’s digital self-preservation. It’s a mindset of: “Don’t decide for me. Let me drive.” And I’ve seen this mistake firsthand: One client rolled out a smart automation feature that quietly activated behind the scenes. Instead of delighting users, it alienated 15–20% of their base. Because the perception was: "You took control without asking." On the other hand, platforms that use clear confirmation prompts (“Are you sure?”, “Review before submitting”, toggles, etc.)—those build long-term trust. That’s the real game. Here’s what I now recommend to every tech founder building for the US market: -Don’t just optimize for frictionless onboarding. -Optimize for visible control. -Add micro-trust signals like “No hidden fees,” “You can edit this later,” and clear toggles. -Let the user feel in charge at every key point. Because trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner: Stop assuming speed is everything. Start building systems that say, “You’re in control.” That’s what creates adoption that sticks. What’s your experience with this? Would love to hear in the comments. 👇 #ProductDesign #UserExperience #TrustByDesign #TechForUSMarket #DigitalAutonomy #businesscoach #coachishleenkaur Linkedin News LinkedIn News India LinkedIN for small businesses

Most people still think AI agents are just fancy chatbots and that mindset is dangerous. If an AI agent can book a hotel or call an internal API, it needs the same guardrails as a human. 🪪 That means real identity. 🔎 That means scoped access. 🔐 That means Zero Trust. In a recent project, Microsoft and WSO2 built an enterprise-grade multi-agent system where every AI agent gets a digital ID. Complete with OAuth2 access tokens, token introspection, and scope-limited permissions. Every action must be authorized. Every step is auditable. Nothing happens “just because the agent asked nicely.” The system uses: ➡️ GPT-4 on Azure OpenAI ➡️ AutoGen for multi-agent orchestration ➡️ WSO2 Asgardeo for identity and access ➡️ A new SecureFunctionTool that forces agents to authenticate before touching sensitive APIs This isn’t some vague concept. The full hotel-booking scenario is live and open-sourced. ✔️ You can clone it. ✔️ Test it. ✔️ Build on top of it. And the best part? It aligns with Microsoft’s upcoming Entra Agent ID — meaning future-proof integration is just config away. Zero Trust isn’t a nice-to-have for AI agents. It’s the only way forward. ➡️ See the full breakdown + working repo here: https://lnkd.in/grxh5PKb ➡️ Clone the solution and try it yourself ➡️ Give your agents their ID cards #AIsecurity #ZeroTrust #AzureAI #OpenSource #IdentityManagement

AI agents will soon be booking travel, managing workflows, and making purchases on our behalf. The problem is, our identity systems were built for people, not for autonomous software. During our recent live podcast with Peter Horadan, CEO of Vouched, he mentioned the five critical identity problems we need to solve before agents become the default way we interact online: 1. Agent Identity: Systems must be able to tell when an agent — not a human — is acting. Every agent needs its own unique, verifiable identity so actions can be tracked and trusted. 2. Delegation: Users should be able to delegate only specific actions to an agent (e.g., “book flights” but not “redeem loyalty points”). These permissions must be granular, explicit, and revocable. 3. Reputation: Just like people, agents will have good or bad reputations. Service providers need ways to assess an agent’s trustworthiness and block those with poor track records. 4. Human Identity Without Real-Time Presence: Humans shouldn’t have to approve every action in real time. Once identity and permissions are established, agents should be able to operate within those boundaries without repeated authentication. 5. Legal Consent: Agents can’t click “I agree” on legal terms. We need mechanisms for humans to provide durable consent upfront, so agents can act within legal frameworks without violating the terms. The Model Context Protocol – Identity (MCP-I), combined with verifiable credentials, fixes this by adding the missing identity and trust layer to agent interactions. When an agent connects to a service: > It identifies itself with a decentralized identifier (DID) that’s unique and verifiable. > The human authorizes the agent through a familiar OAuth-like flow — logging in directly to the service, not through the agent — and grants only the actions they’re comfortable with. These permissions are saved and shared as verifiable credentials. > The permissions are durable and revocable, so the user stays in control without constant re-authentication. > Every action is auditable, allowing services to assess and update the agent’s reputation in real time. > Legal terms are agreed to once by the human during setup, solving the “checkbox” problem for ongoing agent actions. Verifiable credentials make this even stronger. Instead of sharing usernames or passwords, users can issue their agents cryptographically signed credentials that are scoped to specific tasks, auditable for compliance, and revocable at any time. This eliminates password sharing, prevents session hijacking, and ensures every action can be tied to both the human and the authorized agent. The result: a secure, privacy-preserving foundation that enables AI agents to work on our behalf, without compromising trust.

Designing UX for autonomous multi-agent systems is a whole new game. These agents take initiative, make decisions, and collaborate, the old click and respond model no longer works. Users need control without micromanagement, clarity without overload, and trust in what’s happening behind the scenes. That’s why trust, transparency, and human-first design aren’t optional — they’re foundational. 1. Capability Discovery One of the first barriers to adoption is uncertainty. Users often don't know what an agent can do, especially when multiple agents collaborate across domains. Interfaces must provide dynamic affordances, contextual tooltips, and scenario-based walkthroughs that answer: “What can this agent do for me right now?” This ensures users onboard with confidence, reducing trial-and-error learning and surfacing hidden agent potential early. 2. Observability and Provenance In systems where agents learn, evolve, and interact autonomously, users must be able to trace not just what happened, but why. Observability goes beyond logs; it includes time-stamped decision trails, causal chains, and visualization of agent communication. Provenance gives users the power to challenge decisions, audit behaviors, and even retrain agents, which is critical in high-stakes domains like finance, healthcare, or DevOps. 3. Interruptibility Autonomy must not translate to irreversibility. Users should be able to pause, resume, or cancel agent actions with clear consequences. This empowers human oversight in dynamic contexts (e.g., pausing RCA during live production incidents), and reduces fear around automation. Temporal control over agent execution makes the system feel safe, adaptable, and co-operative. 4. Cost-Aware Delegation Many agent actions incur downstream costs, infrastructure, computation, or time. Interfaces must make the invisible cost visible before action. For example, spawning an AI model or triggering auto-remediation should expose an estimated impact window. Letting users define policies (e.g., “Only auto-remediate when risk score < 30 and impact < $100”) enables fine-grained trust calibration. 5. Persona-Aligned Feedback Loops Each user persona, from QA engineer to SRE will interact with agents differently. The system must offer feedback loops tailored to that persona’s context. For example, a test generator agent may ask a QA to verify coverage gaps, while an anomaly agent may provide confidence ranges and time-series correlations for SREs. This ensures the system evolves in alignment with real user goals, not just data. In multi-agent systems, agency without alignment is chaos. These principles help build systems that are not only intelligent but intelligible, reliable, and human-centered.

✈️ When AI Gets It Wrong: A Personal Flight Booking Fiasco (And What Airlines Must Learn) Yesterday, I tried to reschedule a flight through my credit card’s concierge. They quickly routed me to Airline’s new AI chatbot and here’s what happened: The bot blandly told me: “No flights available on your new date”. No options. No hope, So plan to cancel my flight then Concierge connected me to Airline's customer service hotline. The HUMAN agent from same airline offered several good rescheduling choices in seconds. As a leader deep in AI & enterprise transformation, this left me asking: - Are we creating new broken processes with bad AI handoffs? - Why didn’t the bot just say: “More options may be available or I am not able to get details - please talk to an agent” and option to connect with agent instead of shutting the door How many customers are losing trust because of these silent digital failures? There is similar case In 2024, Air Canada was taken to court after its chatbot gave false fare info, and the airline lost: the company is now liable for what their bots say. This isn’t just a glitch  but it’s a business risk. Here’s my expert take for the industry: - If your bot can’t serve, it should smoothly escalate instead of dead ends. - Customers deserve clear, honest prompts (“You may get more help from an agent”) else you will loose the customer Fix your processes. Test your bots for different scenarios (Negative Test cases) and own the outcomes transparently. 💡 Airlines, banks, everyone: Digital trust is hard-won, and easily lost. Don’t let automation undo your brand. Who else has faced these “AI dead ends”? What experiences would you want from future customer service bots? Let’s champion #ResponsibleAI for customer trust and real innovation. #AI #CustomerExperience #DigitalTrust #Airline #Leadership #Transformation

Building Trust in Agentic Experiences Years ago, one of my first automation projects was in a bank. We built a system to automate a back-office workflow. It worked flawlessly, and the MVP was a success on paper. But adoption was low. The back office team didn’t trust it. They kept asking for a notification to confirm when the job was done. The system already sent alerts when it failed as silence meant success. But no matter how clearly we explained that logic, users still wanted reassurance. Eventually, we built the confirmation notification anyway. That experience taught me something I keep coming back to: trust in automation isn’t about accuracy in getting the job done. Fast forward to today, as we build agentic systems that can reason, decide, and act with less predictability. The same challenge remains, just on a new scale. When users can’t see how an agent reached its conclusion or don’t know how to validate its work, the gap isn’t technical; it’s emotional. So, while Evaluation frameworks are key in ensuring the quality of agent work but they are not sufficient in earning users trust. From experimenting with various agentic products and my personal experience in building agents, I’ve noticed a few design patterns that help close that gap: Show your work: Let users see what’s happening behind the scenes. Transparency creates confidence. Search agents have been pioneer in this pattern. Ask for confirmation wisely: autonomous agents feel more reliable when they pause at key points for user confirmation. Claude Code does it well. Allow undo: people need a way to reverse mistakes. I have not seen any app that does it well. For example all coding agents offer Undo, but sometimes they mess up the code, specially for novice users like me. Set guardrails: Let users define what the agent can and can’t do. Customer Service agents do it great by enabling users to define operational playbooks for the agent. I can see “agent playbook writing” becoming a critical operational skill. In the end, it’s the same story I lived years ago in that bank: even when the system works perfectly, people still want to see it, feel it, and trust it. That small "job completed" notification we built back then was not just another feature. It was a lesson learned in how to build trust in automation.