Measuring trust in agentic AI via emotional signals

TL;DR

Accuracy alone is a poor proxy for user trust in agentic AI systems. Even if an internal AI copilot gives technically correct answers, users may feel frustrated, confused, or unsafe – eroding trust. Measuring emotional signals (like satisfaction, confusion, or frustration) offers a more human-centric view of AI success. By ethically capturing these signals (e.g. detecting when users rephrase queries in frustration or express gratitude when goals are met) and anonymizing sensitive data, organizations can truly gauge whether employees trust their AI tools. This emotional analytics approach helps identify where AI oversteps or misses the mark, especially in high-stakes fields like finance or healthcare. Ultimately, it lets teams refine AI behavior and workflows to boost user comfort and adoption.

Agentic AI – think internal GPT-4 copilots that can make decisions or take actions autonomously – is spreading across enterprises. From drafting reports to assisting with customer support, these AI “agents” promise efficiency. But as AI becomes more embedded and agentic, pure performance metrics (like accuracy or latency) are no longer enough. Leaders in AI research and industry caution that success must be measured in terms of trust, comfort, and user adoption, not just technical correctness. In other words, an AI could give 99% correct answers and still fail if users don’t trust it.

Why can’t accuracy alone capture user trust? Consider a scenario: An employee asks an internal chatbot for legal policy guidance. The AI’s response is factually correct – yet it’s phrased bluntly and misses the context of the employee’s specific situation. The user leaves the chat saying “thanks,” but in reality they’re dissatisfied and confused. Web analytics or accuracy metrics would record this as a successful interaction, while missing the frustration or unmet need hidden in the conversation. The user might not complain explicitly, but their trust in the tool subtly erodes. If this pattern repeats, adoption falters. Indeed, surveys find that many AI projects don’t fail due to model errors – they fail because human factors (like user comfort and behavior) were overlooked.

This gap is especially critical in regulated industries and internal enterprise settings. A financial advisor bot might perfectly calculate numbers but, if it oversteps by making an unauthorized trade suggestion, the human user will immediately lose confidence. A healthcare AI assistant might retrieve the correct patient file but then recommend an outdated treatment – technically it “worked,” but it violated the doctor’s expectations and trust. In such contexts, trust is tightly coupled to safety and compliance. Users need to feel the AI understands their goals and boundaries. McKinsey notes that organizations can only realize AI’s benefits if employees believe in it and feel supported – a purely technical rollout without a focus on trust and change management will stall adoption. And according to Stanford’s AI Index, trust remains a major challenge for AI adoption at large: people worry whether AI systems (and the companies behind them) will handle data responsibly and treat users fairly.

The bottom line? Trust is a human feeling, not a number on a dashboard. It grows (or shrinks) through each interaction. If an AI agent causes confusion, irritation, or a sense of lost control, users will either resist using it or engage in risky workarounds. (For example, if an internal tool frustrates employees, they might turn to unauthorized external AI services, creating “shadow AI” risks.) To scale agentic AI safely, organizations must broaden their measurement of success: not just “Is the AI accurate?” but “Are users comfortable, confident, and finding value – or are they getting frustrated and losing trust?”

Emotional Signals 101: Measuring What Users Feel in AI Interactions

User trust has an emotional dimension. It’s influenced by how users feel during their interaction with an AI system, not merely whether the AI’s output was correct. This is where emotional signals come into play. Emotional signals are subtle cues that indicate the user’s state of mind – for instance, signs of frustration, confusion, satisfaction, or delight – as they use an AI assistant. Unlike traditional accuracy metrics (which measure the AI’s performance against some objective standard), emotional analytics look at the human side of the exchange.

Why are these signals such powerful proxies for trust and success? Because humans naturally express trust or distrust through their behavior and tone. If a user is frustrated, they might rephrase the same question multiple times, use harsher language, or abruptly abandon the chat. If they trust and value the AI, we might see expressions of gratitude (“Thanks, that’s helpful!”) or the user confidently following the AI’s suggestions. These implicit behaviors often speak louder than explicit feedback. (Let’s face it – most people won’t fill out a feedback survey or click the thumbs-down button every time an AI answer misses the mark. Thumbs up/down are rare; instead, implicit feedback is abundant in the nuances of conversation.)

Researchers and product teams have identified several key emotional signals to monitor in AI chat interactions:

- Frustration signals: Repeated or rapid-fire queries on the same topic, often with increasing urgency or negative wording. Users might send short, curt messages (“No, that’s not what I meant.”) or simply go silent after a poor answer – a silent rage-quit. High drop-off rates or users rephrasing questions multiple times are classic frustration indicators. In fact, modern AI analytics can detect spikes in such friction moments during a conversation.

- Confusion indicators: The user asks for clarification or expresses uncertainty (“I’m not sure I understand…”). They might reference the AI’s previous answer (“Earlier you said… can you explain?”). This suggests the AI’s response wasn’t clear or context-appropriate, causing trust to dip as the user struggles to make sense of it.

- Satisfaction markers: Look for positive affirmations and completion signals. For example, the user says “Great, got it!” or the conversation concludes with the user achieving their goal (booking a meeting, solving an IT issue) without needing excessive guidance. A satisfied user often has a longer session because they are engaged and getting value – or conversely, a very quick session if the AI solved their query efficiently. Either can indicate success depending on context (completion vs. abandonment).

- Trust signals: These can be more nuanced, but one strong sign is the willingness of users to follow the AI’s recommendations or share more information with the AI. For instance, if users progressively share deeper details or delegate more tasks to the AI, it shows growing confidence. Language that implies reliance (“Let’s try that”) or a collaborative tone indicates the user sees the AI as a trusted partner.

It helps to contrast emotional trust signals vs. traditional accuracy-based signals side by side:

Measurement focus	Emotional trust signals	Accuracy based metrics
What it captures	User feelings and reactions during AI interactions, such as frustration, confusion, or satisfaction.	AI technical performance on tasks, such as correctness of answers and error rates.
How it is measured	Implicit user feedback, conversation patterns, sentiment analysis of messages, rephrase and abandon rates, tone of language.	Explicit performance data, accuracy against a ground truth, factual error count, compliance with specifications or test cases.
Examples of signals	High rephrasing frequency as a frustration signal, questions like “Are you sure?” as a distrust signal, “Thanks, that helped” as a satisfaction signal, abrupt session end as a possible frustration signal.	Percentage of answers that are factually correct, number of hallucinations, response time in milliseconds, uptime percentage.
What it tells you	Whether the user felt heard, understood, and confident using the AI. It highlights user experience issues, misaligned responses, and points where AI behavior undermines trust.	Whether the AI output was right or wrong and how efficiently it operated. It ensures technical quality and reliability of outputs.
Limitations	Can be subjective or dependent on context. Needs careful handling of privacy and context to interpret correctly. Does not directly tell if the AI answer is correct, only how the user reacted to it.	Misses the human context. An answer can be one hundred percent correct and still leave the user unhappy or unsupported. Does not reveal if users actually achieved their goal or if they trust the system over time.

As the table suggests, accuracy metrics alone might look “green” while the user experience is actually red. In one real example, an AI assistant gave a technically accurate response and the user even politely said “thank you” – yet the user left dissatisfied, and web-style analytics would wrongly count that as a success. Emotional analytics would flag that the user’s satisfaction was in doubt (no follow-up questions answered, possibly a terse tone). This human-centric insight is crucial. Gartner analysts have observed that as AI systems act more like teammates than tools, “performance will be less key and the priorities should become trust, comfort, and behavioral adoption”. If we don’t measure those priorities through emotional signals, we’re flying blind to the very outcomes that determine AI project success or failure.

Practical Framework: Capturing and Using Emotional Signals Ethically

Measuring emotional trust signals in an enterprise environment requires a thoughtful approach. You want to gather rich insights on user-AI interactions without violating privacy or trust in the process. Here’s a practical framework and checklist for teams:

1. Instrument your AI interactions to collect implicit feedback:

Start by ensuring you have the data to detect emotional signals. This means logging conversational data and user interaction patterns (safely). For each AI chat session, consider capturing metadata like: How many times did the user reformulate their query? How long was the pause between the AI’s answer and the user’s next input (could indicate hesitation)? Did the user use exclamation points or phrases indicating frustration (“this isn’t helpful”)? Did they ask a follow-up or just leave? These are implicit feedback indicators – far more plentiful than explicit ratings. Modern LLM user analytics tools can automatically map conversation flows and detect these signals‍. For example, they can flag if a conversation unexpectedly loops or if a user’s sentiment turns negative partway through. Design your logging or analytics to pick up on sentiment and tone (using NLP sentiment analysis on user messages) and conversation outcomes (completed goal vs. abandonment).

2. Anonymize and protect personal data:

AI conversations – especially internal ones – can contain sensitive info (customer data, financial figures, personal identifiers). It’s essential to bake in privacy from the start. Best practices include scrubbing or pseudonymizing personally identifiable information (PII) in conversation logs.

For instance, Nebuly’s platform automatically detects PII (like names, emails, ID numbers) and replaces them with tokens so that analytics can run on patterns without exposing real identities. If you’re building your own logging, consider using regular expressions or AI to redact things like social security numbers or client names. Also, follow data retention policies: don’t keep raw conversation data longer than needed. (By default, Nebuly retains data indefinitely for time-series analysis, but admins can set shorter retention windows to comply with policies.)

Encryption is a must – all logs should be encrypted in transit and at rest, just as you’d treat sensitive customer data. Additionally, aggregate whenever possible. Your goal is to see broad patterns (e.g. “Finance team users show more frustration signals than Engineering team users”), not to spy on individual employees. Keeping analysis at group or trend level helps maintain privacy and trust. In regulated sectors, consult with your compliance or legal team to ensure this user analytics approach aligns with data protection regulations (GDPR, etc.) – often it will, if done with anonymization and legitimate interest in mind.

‍Transparency is also a good practice: let users know (perhaps in an internal policy or onboarding) that the AI tool collects usage insights to improve their experience, while strictly protecting personal data.

3. Use ethical AI techniques – focus on improvement, not surveillance:

The purpose of collecting emotional signals is to improve the system and support users, not to penalize them.

Establish clear governance on how this data will be used. For example, if you discover many users are frustrated with a particular feature, the action item is to fix the feature or provide better training – not to scold users for “not using it right.” Avoid any sense of “Big Brother” monitoring. In fact, frame it as empowering: you’re listening to users’ unspoken feedback to make the AI better for them.

Also, be cautious with “emotion AI” that might infer sensitive attributes. It’s one thing to detect frustration from obvious signals like repeated queries; it’s another to, say, analyze a user’s face on a webcam (don’t go there unless absolutely necessary and with consent!).

Stick to low-intrusion, high-value signals. As a rule, never use emotional analytics to manipulate users (for instance, don’t try to exploit a frustrated user by upselling something unrelated – that will backfire and destroy trust). The goal is assisting users, not nudging their emotions for profit. Even Stanford HAI researchers, who explore mood-detection AI, emphasize using such methods ethically and with privacy protection if at all. In enterprise settings, this means keeping the analysis internal and geared toward product improvement and user support.

4. Translate signals into action for continuous improvement:

Capturing data is only half the battle – you need to close the loop by acting on what you learn.

Establish a regular review of emotional analytics with your product team. For example, if the logs show a surge of confusion signals whenever the AI talks about “Policy XYZ,” that could indicate the policy content is too complex or the AI’s explanation is unclear.

The fix might be to update the AI’s prompt or add a clarifying response for that topic. If you see frustration spikes at a certain step in a workflow, consider adding a fallback option (like offering to escalate to a human or providing more examples). Some teams set up alerting: e.g. if a conversation has multiple frustration flags in a row, automatically ping a human supervisor or trigger the AI to say “I’m sorry, let me get a human to assist.” This kind of real-time escalation based on frustration detection can prevent small issues from snowballing.

Over time, you can also use emotional signal trends as a success metric themselves – for instance, “user satisfaction score” measured via average sentiment or task completion rate. Many forward-thinking organizations incorporate such metrics alongside technical ones. In fact, reliability frameworks for agentic AI are evolving to include user trust and frustration rates as key performance indicators‍. For example, a healthcare AI team might track physician satisfaction and override rates in parallel with diagnostic accuracy.

If doctors are frequently overriding the AI’s suggestions, that’s a red flag that trust is low, even if the AI’s suggestion quality is ostensibly high. By monitoring these human-in-the-loop behaviors, the team can adjust the AI’s autonomy level or improve its explanations to rebuild trust.

5. Close the feedback loop with users (optional but beneficial):

In some cases, especially for internal tools, you might share back insights or improvements with the user base. For example, “We noticed a lot of frustration around the vacation policy bot, so we’ve updated it to better handle complex questions about carryover days.” This can increase transparency and demonstrate that the organization is actively improving the AI based on employees’ experiences, further boosting trust. It also encourages users to remain patient and engaged, knowing that their struggles aren’t ignored.

By following these steps – instrument, anonymize, act, and iterate – teams can harness emotional signals in a way that is both effective and respectful. When done right, emotional analytics becomes an early warning system for AI issues. It often surfaces problems before they show up in traditional metrics or formal complaints. For instance, a rising trend of mild frustration signals might alert you to a growing gap in the AI’s knowledge domain or a misalignment with user expectations, allowing a fix before users lose trust entirely. Remember, AI is fast, but trust is slow. Building that trust requires vigilance to the emotional undercurrents of user interactions.

Nebuly’s Solution: Purpose-Built Emotional Analytics for GenAI (Privacy-First)

Measuring and acting on emotional trust signals may sound complex – but this is exactly where Nebuly shines. Nebuly is a user analytics platform specifically designed for LLM-based and agentic AI products, built to capture how users actually behave and feel when interacting with AI. Think of it as “Google Analytics for AI conversations,” providing a window into user intent, sentiment, and friction that traditional logs would miss.

How Nebuly captures emotional signals

The platform sits between your AI systems and your users, recording each interaction in real time and extracting key signals automatically. Out of the box, Nebuly detects things like: the user’s intent (what they’re trying to accomplish), sentiment and emotion in their messages, frustration markers (e.g. repeated queries or abrupt endings), as well as any risky or compliance-related flags‍. In practice, this means you get dashboards and alerts about conversations where, say, the user became upset or the dialogue went in circles. One of Nebuly’s strengths is surfacing implicit feedback that would otherwise be buried. Logs tell you what the system did, but user analytics reveal what the human experienced. For example, your LLM telemetry might show “response generated in 0.8 seconds,” whereas Nebuly can show that the user abandoned the conversation 10 seconds later out of frustration – a critical insight into trust erosion.

Privacy-first design

‍From day one, Nebuly recognized that LLM interaction data is sensitive. The platform prioritizes security and privacy, offering features like automatic PII removal (anonymizing names, emails, etc.) in all captured data. It’s compliant with enterprise standards – Nebuly is SOC 2 Type II and ISO 27001 certified, and fully GDPR compliant. For companies in regulated industries or those with strict IT policies, Nebuly provides deployment flexibility: you can self-host Nebuly in your own cloud or on-premises environment. Whether you use Azure, AWS, GCP, or a private data center, Nebuly can be deployed under your control (via Docker), so your conversational data never has to leave your security perimeter. This is a huge plus for banks, hospitals, and government users who need analytics but cannot send data to an external SaaS. At the same time, Nebuly’s cloud offering is available for those who prefer a managed solution – giving you choice depending on your compliance needs.

Real-time frustration detection and proactive support

‍One of Nebuly’s differentiators is how it enables real-time intervention. The platform can be configured to trigger alerts or actions when certain conditions are met – for example, if an internal support copilot receives two angry responses in a row from a user, Nebuly can flag that conversation for a supervisor to review immediately. This kind of real-time frustration detection helps teams catch issues in the moment, before they escalate. It’s like having an early warning system that pings you, “Hey, user X seems really unhappy with the HR bot’s answers right now.” As noted earlier, timely escalation or human follow-up in such cases can rescue the user’s trust. Nebuly makes implementing these workflows much easier by providing the analytics hooks and integrations (e.g., sending alerts to Slack or your dashboard when thresholds are crossed).

Holistic insights for continuous improvement

‍Nebuly doesn’t just collect data – it presents it in digestible ways for different stakeholders. Product managers can see which features or topics cause the most friction. Compliance officers can get reports on how often users trigger policy violations or mention sensitive data (useful for preventing unintentional leaks). AI developers can dive into transcripts of failed or frustrating conversations to understand model shortcomings. By bridging technical metrics with human-centric metrics, Nebuly creates a complete picture of AI success: you can literally see, side by side, the system’s performance and the user’s sentiment. For example, a Nebuly dashboard might show that “Model accuracy is 85% on finance questions, but user satisfaction on those questions is only 60%” – pointing to a gap where answers might be correct yet not hitting the mark for users. Armed with that knowledge, you can target improvements (maybe the tone needs adjusting, or perhaps users actually wanted a different kind of help than what was provided).

Nebuly’s key differentiators at a glance:

1. Built for conversational data: It understands natural language inputs and the non-linear flow of dialogues (as opposed to web analytics tools that expect pageviews and clicks). It can interpret conversation threads, measure drops in engagement, and identify intent shifts in ways traditional analytics can’t.

2. Emotional and behavioral analytics: Nebuly tracks sentiment progression, trust indicators, and friction points throughout the conversation. It doesn’t stop at “user asked question, AI answered” – it looks at how the user responded to the answer (tone, follow-ups, etc.).

3. Risk and compliance monitoring: Alongside emotions, Nebuly flags compliance issues (PII exposure, toxic content, or other risky behavior) in real-time. This ties directly into trust, because a user is unlikely to trust an AI that, for example, leaked sensitive info or produced a policy-violating answer. Nebuly helps catch those incidents to maintain overall trust in the AI system.

4. Privacy and security: As discussed, Nebuly’s design avoids centralizing sensitive data in a way that would spook IT or users – it can run in your environment, scrubs PII, and meets stringent security standards. This is a crucial enabler for actually deploying emotional analytics in enterprises that have strict data governance.

5. Ease of integration and use: Nebuly integrates with your AI stack (whether you’re using OpenAI, Anthropic, or open-source LLMs) by capturing the inputs/outputs and chat context. The insights are provided in dashboards that non-technical stakeholders can understand – no need to be a data scientist.

In summary, Nebuly acts as the connective tissue between user experience and AI performance. It enables organizations to measure what truly matters for adoption – human satisfaction, trust, and safety – alongside the usual technical metrics. By doing so, it closes the gap we’ve identified: you might have an AI that’s fast and accurate, but now you’ll know if it’s actually delivering value in the eyes of your users. As one Gartner expert noted, ignoring the emotional side of AI adoption risks resistance and missed opportunities. Nebuly ensures you don’t have to fly blind on that front.

If you’re looking to build AI systems that people truly embrace, measuring and acting on emotional trust signals is the new imperative. Nebuly provides a turnkey way to do this, from frustration detection to satisfaction scoring – all with enterprise-grade privacy. Ready to turn user trust into your AI’s strongest KPI? Nebuly can help you get there.

(Nebuly is offering demos to show how this works in real-world scenarios – from internal copilots to customer-facing chatbots. You can see how the platform captures live sentiment, flags risks, and drives improvements in AI responses. If improving your AI’s emotional IQ and user trust is a priority, book a demo with Nebuly to experience these capabilities firsthand.)

Frequently asked questions

What is agentic AI exactly?

Agentic AI refers to AI systems, often powered by large language models, that can act with a degree of autonomy or agency. A simple chatbot only responds when asked. An agentic AI can take proactive steps, make decisions, and execute tasks on behalf of a user.

For example, an internal assistant that answers employee questions, schedules meetings, drafts emails, or runs scripts is agentic. These systems operate more like junior colleagues or decision support partners than static tools.

Because they have more autonomy, measuring their trustworthiness is critical. Users need to feel confident when they let the AI handle tasks without constant oversight. That is why tracking trust signals and having clear escalation paths are important in agentic AI environments.

How can we detect frustration or satisfaction from text conversations?

Detecting user emotions from text uses natural language processing to analyze both content and patterns in messages. For frustration, the system can look for repeated phrases, negative wording, many short corrective replies, or a sudden stop in the conversation.

It can also track how often the user rephrases the same question in different ways. That often shows they did not get a satisfying answer. On the satisfaction side, thank you messages, positive language such as “perfect, thanks,” or a smooth task completion are strong signals.

Some systems calculate a sentiment score for each message and chart how that score changes across the conversation. If sentiment drops over time, the user is likely getting frustrated. If it rises or stays positive, they are likely satisfied. Tools like Nebuly also consider meta behavior, such as session length or repeat usage, to infer trust and engagement.

Is it legal and compliant to analyze employees’ emotional signals?

Privacy and ethics matter. The short answer is yes, this can be done legally and safely, but only if you follow proper practices. If you operate under GDPR, you can usually analyse conversation data under “legitimate interests” to improve an internal tool, as long as personal data is protected. That means anonymising or pseudonymising it so it cannot be tied back to an individual. It also means avoiding the extraction or use of sensitive attributes unless strictly necessary. Most companies add a note in their internal privacy or IT policies explaining that employee use of company tools, including AI assistants, may be monitored or analysed for improvement and security. That transparency is important.

From a technical perspective, a compliant setup helps. Tools such as Nebuly support SOC 2 and ISO 27001, offer on-prem deployment, handle PII stripping, and secure access controls. Limit who can view analytics and share only aggregated trends when possible rather than anything that identifies a single employee. Ethically, focus on system or team insights, not individual scores. The aim is to improve the AI and understand training needs, not to judge employees’ emotions. In regulated sectors like finance and healthcare, this work can even support duty of care. If an AI tool confuses analysts or clinicians, you must know. When data is handled transparently and securely, analysing emotional signals is about ensuring the tools actually help people, not about surveillance.

Why not just ask users if they trust the AI or use a simple rating?

Direct feedback is useful and you should collect it when you can. The challenge is that most users rarely click rating buttons or fill out surveys in chat interfaces. They are focused on finishing their task, not on grading the AI after every interaction.

Trust also changes during a conversation. A single rating at the end may not capture rising frustration in the middle of the flow. Implicit signals fill that gap by reflecting in the moment reactions through behavior such as rephrasing or early exits.

You can still run surveys and interviews for deeper qualitative insight. For continuous, scalable monitoring across thousands of chats, emotional and behavioral analytics give a much richer and more reliable picture than ratings alone.

How do emotional signals help in high stakes fields like finance or healthcare?

In high-stakes industries, the margin for error (and tolerance for confusion) is even thinner. Emotional signals become early warning signs of risk. For example, in healthcare, if a clinical decision-support AI is suggesting something and doctors frequently show confusion or frustration in response, that could indicate the AI is providing unsafe or irrelevant guidance.

Maybe it’s not contextualized to the patient, or uses medical jargon that frontline staff don’t understand. By catching those reactions, the hospital’s AI team can intervene before any harm occurs – perhaps by adjusting the AI’s recommendations or inserting an automatic escalation to a human specialist whenever it senses the doctor’s hesitation. In finance, consider an internal AI tool that helps analysts generate reports. If analysts start bypassing the tool or expressing frustration (“these numbers don’t look right…”), it may be flagging that the AI is making calculation errors or misinterpreting data – which left unchecked could lead to a compliance violation or financial loss. Gartner’s insights align with this: they emphasize that leaders must monitor emotional and behavioral outcomes of AI with the same rigor as technical outcomes, especially as AI becomes a colleague in the workplace.

Also, regulators themselves care about user trust and understanding. For instance, guidelines around “AI transparency” essentially ask: can the human understand and appropriately trust the AI’s output? If your users are confused (low trust), that could be a compliance issue in areas like informed consent or accountable decision-making. Lastly, emotional signals help you tailor the AI’s autonomy. In a bank, if the AI notices users are hesitant or keep double-checking its work, you might dial back the autonomy (have more human review) until trust improves. Conversely, if users are smoothly accepting the AI’s suggestions (high satisfaction), you can consider giving the AI a bit more leeway, knowing it’s meeting user needs. In short, emotional signals are like a canary in the coal mine for both user trust and potential risk in critical domains – they alert you before a small issue becomes a big disaster.

Conclusion

User trust in agentic AI is a multifaceted, deeply human metric. By measuring it through emotional signals, organizations can ensure their AI deployments truly empower and satisfy users rather than alienate them. Accuracy will always matter, but understanding emotions and perceptions is the key to AI that people love to use. With careful data practices and the right tools (like Nebuly) in place, teams can capture these insights and continuously refine their AI’s behavior – creating a virtuous cycle where better AI experiences lead to higher trust, which leads to broader adoption and value. In the AI era, the soft metrics are becoming the hard currency of success. Invest in them, and you’ll build AI solutions that not only work, but win hearts and minds as well.

Lastly, if you’re ready to boost your AI’s “EQ” and see how emotional analytics can transform your GenAI projects, consider giving Nebuly a try. We invite you to book a demo and discover how capturing trust signals can elevate your AI’s impact across the enterprise.

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