Toward Safe and Responsible AI Agents:
A Three-Pillar Model for
Transparency, Accountability, and Trustworthiness
Edward C. Cheng
echeng04@stanford.edu
Jeshua Cheng
jeshua.cheng@inquiryon.com
Alice Siu asiu@stanford.edu
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Abstract – This paper presents a conceptual and operational framework for developing and operating safe and trustworthy AI agents based on a Three-Pillar Model grounded in transparency, accountability, and trustworthiness. Building on prior work in Human-in-the-Loop systems, reinforcement learning, and collaborative AI, the framework defines an evolutionary path toward autonomous agents that balances increasing automation with appropriate human oversight. The paper argues that safe agent autonomy must be achieved through progressive validation, analogous to the staged development of autonomous driving, rather than through immediate full automation. Transparency and accountability are identified as foundational requirements for establishing user trust and for mitigating known risks in generative AI systems, including hallucinations, data bias, and goal misalignment, such as the inversion problem. The paper further describes three ongoing work streams supporting this framework: public deliberation on AI agents conducted by the Stanford Deliberative Democracy Lab, cross-industry collaboration through the Safe AI Agent Consortium, and the development of open tooling for an agent operating environment aligned with the Three-Pillar Model. Together, these contributions provide both conceptual clarity and practical guidance for enabling the responsible evolution of AI agents that operate transparently, remain aligned with human values, and sustain societal trust.
Keywords— Generative AI, AI Agent, Human-in-the-Loop, HITL, RLHF, Responsible AI, Trustworthy AI
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1. Introduction
The emergence of AI agents marks a new phase in the evolution of generative AI. While traditional chatbots focus on generating text-based responses, AI agents extend this capability into real-world action. These systems can execute tasks, reason over goals, and make decisions on behalf of humans. This shift from text generation to autonomous task execution holds the key to unlocking the economic and practical value of generative AI. Yet, as these systems gain autonomy and agency, the risks of error, bias, and misalignment also multiply. When AI agents make consequential real-life decisions, such as transferring funds, filing drug prescriptions, drafting contracts, or guiding robotic actions, their mistakes may lead to financial losses, privacy breaches, or even physical harms. These errors may arise from training biases, lack of situational context, hallucinated reasoning, or misalignment between user intent and model objectives. Consequently, the field is confronted with an urgent challenge: how to ensure safe, transparent, and accountable AI agents that enhance productivity without compromising accuracy, trust or human values.
A growing body of recent research has emerged to address this challenge, focusing on the Human-in-the-Loop (HITL) paradigm and its extensions as means to govern, calibrate, and align AI agent behavior. These works explore how human expertise, oversight, and ethical grounding can be woven into the AI learning and action loop to produce systems that are both impactful and controllable. Collectively, they represent a growing consensus that human-AI collaboration, rather than full automation, is the most promising pathway toward efficient, effective, and safe AI agents that will result in higher productivity gain [1].
To organize this literature, we can group the representative surveys into three major thematic clusters that trace the conceptual evolution of safe AI agent design:
1. Foundational theories of human-in-the-loop AI and machine learning.
1. Operational frameworks and platforms for human-AI collaboration.
1. Emerging approaches for uncertainty alignment and human-governed AI agents.
1. Foundational Theories of Human-in-the-Loop AI
Early research established the theoretical and ethical foundations for integrating humans into the AI lifecycle. Zanzotto (2019) proposed Human-in-the-loop Artificial Intelligence (HitAI) as both a moral and structural correction to the unregulated growth of autonomous AI [2]. He argued that humans are not mere annotators but the original “knowledge producers” whose insights underpin AI performance and thus must remain central to both credit and control. Wu et al. (2022) expanded this notion through a systematic survey of HITL for machine learning, framing it as a data-centric methodology that unites human cognition with computational scalability. They demonstrated that effective human involvement improves labeling efficiency, interpretability, and robustness, forming the foundation for iterative feedback loops in model development [3].
Building on these theoretical bases, Mosqueira-Rey et al. (2023) presented a unifying taxonomy of Human-in-the-Loop Machine Learning (HITL-ML) paradigms [4]. They identified key interaction modes, which include Active Learning, Interactive ML, Machine Teaching, Curriculum Learning, and Explainable AI. They revealed that human-AI relationships exist along a continuum of control: from machine-driven query optimization to human-driven knowledge transfer and interpretation. These early frameworks collectively redefined HITL as not simply supervision, but shared agency between human reasoning and machine inference, setting the epistemic groundwork for subsequent advances in safety and transparency.
Extending the HITL perspective beyond technical design, recent studies from MIT Sloan introduced a management-oriented framework known as AI Alignment. This paradigm emphasizes that model accuracy, reliability in real-world contexts, and stakeholder relevance must be achieved through continuous human engagement. It reframes human involvement not only as a safeguard but also as a means for organizations to learn and adapt as they deploy AI. Grounded in empirical case studies, this framework shows that practices such as expert feedback and stakeholder participation are essential for building safe, context-aware AI systems [5]. A complementary MIT Sloan study found that asking critical safety questions early in the AI development process helps prevent systemic errors and security vulnerabilities, further reinforcing the importance of proactive human oversight [6].
1. Operational Frameworks for Safe and Collaborative AI Agents
As Human-in-the-Loop principles matured, a second wave of research shifted toward practical frameworks and system architectures that enable effective human-AI collaboration in real-world, embodied environments. Bellos and Siskind (2025) exemplify this transition by introducing a structured evaluation framework, a multimodal dataset, and an augmented-reality (AR) AI agent designed to guide humans through complex physical tasks such as culinary cooking and battlefield medicine. Their empirical studies demonstrate that interactive, context-aware guidance significantly improves task success rates, reduces procedural errors, and enhances user experience. Importantly, their results also show that exposure to AI-assisted guidance leads to measurable improvements in subsequent unassisted task performance, indicating that AI agents can support not only immediate task completion but also longer-term human skill acquisition. These findings position AI agents as collaborative partners that augment human capability rather than as purely automated systems [7].
In parallel, Mozannar et al. (2025) introduced Magentic-UI, an open-source user-interface platform for human-in-the-loop agentic systems. Built on Microsoft’s Magentic-One framework, it enables users to co-plan, co-execute, approve, and verify AI actions in complex digital tasks such as coding and document handling [8]. The platform embeds human oversight through structured, repeatable mechanisms. It supports co-planning, co-tasking, action approval, and answer verification, establishing a controlled environment for studying trust calibration, safety, and usability in AI agents. Together, these efforts move the field from abstract advocacy to practical system engineering, demonstrating that safety and transparency can be designed into agent interfaces, workflows, and orchestration protocols.
1. Emerging Approaches for Uncertainty-Aware and Human-Governed AI Agents
Recent work has deepened the mathematical and procedural foundations of safety and alignment. Retzlaff et al. (2024) surveyed the domain of Human-in-the-Loop Reinforcement Learning (HITL-RL), arguing that reinforcement learning (RL) inherently depends on human feedback and should be understood as a HITL paradigm. Their work outlined design requirements such as feedback quality, trust calibration, and explainability for moving from human-guided to human-governed learning [9]. Complementing this, Ren et al. (2023) proposed the KNOWNO (“Know When You Don’t Know”) framework for LLM-driven robotic planners to identify critical moments that require human involvement. By employing conformal prediction to quantify uncertainty, KNOWNO enables robots to detect when their confidence falls below a safety threshold and proactively request human input to ensure safe and reliable task execution [10]. This model of uncertainty alignment provides formal statistical guarantees on task success while minimizing unnecessary human intervention. This work represents a crucial step toward self-aware, help-seeking agents.
At a broader institutional level, research from Harvard University has expanded the discussion of AI safety to include ethics, governance, and societal accountability. Allen et al. (2024) proposed a democratic model of power-sharing liberalism, emphasizing human flourishing, shared authority, and institutional accountability. They argued that AI governance must move beyond risk management to actively promote public goods, equality, and autonomy through inclusive participation and transparent oversight. Their framework identifies six core governance tasks: mitigating harm, managing emergent capabilities, preventing misuse, advancing public benefit, building human capital, and strengthening democratic capacity [11]. Complementing this perspective, Barroso and Mello (2024) examined AI as both a revolutionary and perilous force shaping humanity’s future, calling for a global governance framework grounded in human dignity, transparency, accountability, and democratic oversight [12]. Together, these contributions frame AI not as a force to restrain but as a catalyst for renewing democracy and reinforcing collective well-being.
Finally, Natarajan et al. (2025) reframed the entire discussion through the concept of AI-in-the-Loop (AI2L). Their analysis reveals that many systems labeled as HITL should be considered as AI2L, where humans, not AI, remain the decision-makers. They argue that this distinction is critical for designing systems that emphasize collaboration over automation, human impact over algorithmic efficiency, and co-adaptive intelligence over substitution [13]. This reorientation marks a philosophical inflection point: moving from human-assisted AI to AI-assisted humanity.
1. Toward a Framework for Safe, Transparent AI Agents
Across these studies, a clear trajectory emerges. The field has progressed from recognizing the ethical necessity of human oversight, to engineering collaborative systems, and to developing experimentally grounded mechanisms for uncertainty and governance. Collectively, these efforts affirm that the challenge of AI agent safety, transparency, and alignment is both urgent and tractable. Embedding humans as teachers, collaborators, and governors within the AI lifecycle consistently improves reliability and trustworthiness, yet fragmentation persists across methodologies and evaluation metrics.
This paper advances the next step to synthesize these developments into a unified conceptual framework and a set of guiding principles that integrate HITL, AI2L, uncertainty alignment, and human-governed learning into a progressively improving autonomous environment. Together, these foundations define an operational setting for a new generation of AI agents that are transparent by design, collaborative by nature, and accountable in operation, with the explicit goal of enabling increasing level of autonomy in a safe, controlled, and trustworthy manner.
1. The Evolution Path Towards Autonomous Agents
The vision of achieving fully autonomous AI agents represents one of the most ambitious goals in artificial intelligence. However, this vision cannot be realized in a single leap. It must evolve through progressive stages of validation and oversight, where human involvement is reduced only as confidence in the system’s performance and alignment grows through proven safety, reliability, and accountability. This evolutionary approach has clear precedents in other industries, particularly in the development of autonomous driving.
1. Lessons from Autonomous Driving
The field of autonomous driving provides an instructive example of how automation can evolve responsibly. Early driver-assist systems such as adaptive cruise control and lane-keeping support were designed to assist rather than replace human judgment. These systems required the driver to maintain foot on the pedal, hands on the wheel, and eyes on the road at all times. As perception models, control algorithms, and sensor fusion technologies advanced, vehicles began to handle more complex scenarios independently, such as automatic parking and highway lane changes. At this stage, the human driver could briefly disengage from active control but still had to monitor the road and be prepared to intervene if necessary.
<details>
<summary>SafeAIAgent-img001.png Details</summary>
### Visual Description
## Diagram: Sequential Adjustment of Vehicle Seat Position
### Overview
The image depicts a four-stage sequence illustrating the progressive adjustment of a vehicle seat position. Each stage is represented by a simplified blue icon of a person in a car seat, connected by light blue arrows indicating directional movement. The first icon includes red circular highlights to emphasize specific body parts and steering wheel interaction.
### Components/Axes
- **Stages**: Four sequential icons representing incremental adjustments.
- **Arrows**: Light blue arrows connect each stage, indicating the direction of movement.
- **Highlights**: Red circles in the first icon mark the head, steering wheel, and foot.
- **Final State**: The last icon shows the seat detached from the vehicle, with the steering wheel floating.
### Detailed Analysis
1. **Stage 1**: A person seated in a car, with red circles highlighting:
- Head (top of the figure)
- Steering wheel (held by both hands)
- Right foot (on the pedal)
- Dotted blue arrows point forward, suggesting forward motion or focus.
2. **Stage 2**: The person shifts their posture:
- Steering wheel is now held with one hand.
- Dotted blue arrow points forward, maintaining directional emphasis.
3. **Stage 3**: Further adjustment:
- Steering wheel is no longer held.
- Person leans back in the seat.
- Arrow points downward, indicating seat recline.
4. **Stage 4**: Final state:
- Seat is fully reclined and detached from the vehicle.
- Steering wheel floats independently.
- No human figure remains in the seat.
### Key Observations
- The sequence demonstrates a logical progression from active driving to complete seat detachment.
- Red highlights in Stage 1 draw attention to critical interaction points (head, steering wheel, foot).
- Arrows consistently point forward or downward, guiding the viewer through the adjustment logic.
- The final stage implies a safety or emergency scenario where the seat is no longer functional.
### Interpretation
This diagram likely serves as an instructional or safety guideline for vehicle seat adjustments. The red highlights in Stage 1 may indicate areas requiring caution or specific interaction during driving. The progressive detachment of the seat in Stage 4 suggests a scenario where the seat becomes non-operational, possibly due to mechanical failure or an emergency. The absence of text implies the diagram is designed for universal understanding, relying on visual cues rather than language-specific instructions. The use of arrows and posture changes emphasizes cause-and-effect relationships in seat adjustment mechanics.
</details>
Figure 1: Full Autonomous Driving was a Gradual Evolving Process
This gradual and transparent evolution allowed engineers to identify edge cases, improve algorithms, and refine user interfaces based on real-world feedback. Most importantly, it allowed trust of the society to grow incrementally. Each technological improvement was accompanied by clearer communication about the system’s limitations and capabilities. Drivers learned when to rely on the system and when to take over. Through testing, validation, and iterative learning, both the technology and its human users matured on autonomous driving together. Only through this patient process did the industry approach Level 4 and Level 5 autonomy, where vehicles can operate without human intervention in most or all conditions [12, 13]. The success of this journey lies not only in technological innovation but also in earning human trust through transparency, communication of system limits, and clear accountability.
1. Parallels in AI Agents Development
A similar path must be followed in the evolution of autonomous AI agents. These systems act on behalf of humans in both digital and physical environments, making decisions that can have significant consequences. Like early autonomous vehicles required drivers to remain attentive, current AI agents still depend on Human-in-the-Loop (HITL) oversight to ensure that their actions align with human intent. Human involvement serves as both a safeguard and a source of learning, helping the system adapt responsibly. As discussed in the earlier introduction, research by Wu (2022), Mosqueira-Rey (2023), and Retzlaff (2024) consistently shows that HITL systems improve interpretability, accountability, and model reliability [3, 4, 9]. Rather than viewing human oversight as an administrative overhead, it should be recognized as a critical step in the learning and governance process that helps agents mature progressively.
1. HITL as a Mechanism for Trust and Safety
Human oversight is particularly essential during the intermediate stages of agent development and deployment. At this point, agents are capable of complex reasoning but still lack the contextual, ethical, and situational awareness required for independent operation [16]. Well-designed HITL mechanisms allow humans to validate outputs, correct errors, and prevent harm caused by hallucinations, data biases, or incorrect assumptions. This feedback loop not only safeguards users but also enables the system to learn and improve over time. As the system demonstrates consistent accuracy and reliability, the level of human intervention can be reduced. However, this reduction must be based on measurable improvements, not assumption.
The importance of this gradual approach becomes even more evident in trust-sensitive domains such as finance, human resources, healthcare, legal, and areas that require regulatory compliance. Human oversight ensures share responsibility between humans and AI, maintaining compliance with both legal standard and societal expectations. Just as self-driving systems underwent years of supervised testing before being trusted on public roads, autonomous AI agents must demonstrate reliability before operating independently in high-stakes environments. Yet, their journey to full automation will likely unfold more rapidly, driven by the accelerating pace of AI research and development.
1. A Collaborative Path Toward Full Autonomy
The journey toward fully autonomous agents is both a technological and social process. Technological progress enables higher levels of independence, while social acceptance depends on observable safety and accountability. Research from Bellos (2025) and Mozannar (2025) has shown that when humans and AI collaborate effectively, the result is higher task success rates, improved trust, and greater user confidence [7, 8]. Collaboration thus provides a bridge between current assisted systems and the future of full autonomy.
This process can be viewed as four evolutionary stages of AI agency:
1. Assisted Agents: Humans make decisions while AI supports them through recommendations and reasoning.
1. Collaborative Agents: Humans and AI share responsibility in decision-making and task execution, combining human contextual understanding with AI computational precision and scalability. Human participation remains essential within the agentic workflow, as it enriches situational and semantic context, ensuring that AI agents produce responses and actions that are relevant, accurate, and aligned with user intent and real-world constraints [16].
1. Supervised Autonomy: AI operates independently in constrained environments while remaining accountable through human review.
1. Full Autonomy with Human Governance: AI functions independently within transparent, auditable frameworks that preserve human oversight at the policy level.
Advancement through these stages must be validated by evidence of safety, predictability, and alignment with human intent. This progressive process reflects the same progression that made autonomous driving successful. Skipping these steps would risk premature deployment and loss of confidence, which could set back both innovation and adoption.
1. Toward Trustworthy Autonomy
True autonomy cannot be declared by design; it must be demonstrated through experience and data. Each stage of progress should confirm that the agent can act responsibly and transparently within defined boundaries. By embedding Human-in-the-Loop principles throughout the development process ensures that autonomy and trust grow in tandem. As seen in autonomous driving, confidence arises from steady progress and accountable design. While AI agents may reach maturity more quickly due to faster digital feedback loops and lower physical risks, their path to autonomy must still be guided by the same principles of transparency, validation, and ethical oversight.
1. A Three-Pillar Model for a Safe AI-Agent Operating Environment
In the previous sections, we demonstrated that as AI systems evolve from passive chatbots to fully autonomous agents capable of acting on behalf of humans, the potential for both benefit and harm expands dramatically. In addition, as AI agents evolve to become increasingly independent of humans, their autonomy must emerge through a gradual, trust-building process in which human oversight and collaboration remain essential until AI systems demonstrate consistent reliability and alignment.
Building on these foundations, this section proposes that to enable this evolutionary process to unfold safely and productively, AI agents must operate within a structured environment designed to support growth, supervision, and accountability. Without such an environment, autonomous evolution would occur in an uncontrolled manner, exposing organizations and individuals to unacceptable risks.
To address this need, we propose a Three-Pillar Model (3PM) to support a safe AI-agent operating environment. This model defines the fundamental principles and environmental conditions required to develop, deploy, and operate safe autonomous agents while maintaining a balance between automation and human collaboration. The three pillars are:
1. Transparency of AI Agents ensures visibility into how agents operate across their life cycles.
1. Accountability in Decision-Making provides mechanisms to attribute and explain decisions made by both humans and AI.
1. Trustworthiness through Human-AI Collaboration establishes confidence in agentic systems through well-timed human oversight and fallback safeguards.
Together, these pillars create the foundation for a safe and productive ecosystem where AI agents and humans can share responsibilities and co-evolve toward higher levels of autonomy. They support the long-term goal of achieving responsible, human-aligned AI while ensuring that enterprises can realize measurable return on investment through efficient, reliable, and trustworthy automation.
1. Pillar One: Transparency and Building Trust with AI
Transparency provides the visibility necessary for humans to understand, monitor, guide, and audit agent behavior. It allows operators to know how the agent works, what it is doing, and why it acts in a particular way. This visibility is critical during the evolutionary path described earlier, because it enables humans to supervise and calibrate the agent’s performance as autonomy increases.
Every agent instance passes through a lifecycle consisting of three stages: initiation, active operation, and completion or termination. Transparency must exist throughout each stage to make the process comprehensible and auditable.
<details>
<summary>SafeAIAgent-img002.png Details</summary>
### Visual Description
## State Machine Diagram: Workflow Process
### Overview
This diagram illustrates a state machine workflow with seven distinct states and transitions between them. The process begins at "Begin" and progresses through initialization, activation, completion, or abortion, ending at "End." Key features include a feedback loop in the "Active" state and explicit error-handling paths.
### Components/Axes
- **States** (represented by colored circles/squares):
- Begin (gray square)
- Initiating (light gray circle)
- Initiated (light green circle)
- Active (blue circle)
- Finish (dark blue circle)
- Abort (red circle)
- End (gray square)
- **Transitions** (arrows with labels):
- Create → Initiating
- Config Prompt → Initiated
- Launch → Active
- Notify → Active (loop)
- Callback → Active (loop)
- Complete → Finish
- Cancel → Abort
- Close → End (from Finish and Abort)
- **Legend**: Colors correspond to state types (e.g., blue = Active, red = Abort).
### Detailed Analysis
1. **Initialization Phase**:
- "Begin" (gray) → "Initiating" (light gray) via "Create"
- "Initiating" → "Initiated" (light green) via "Config Prompt"
2. **Execution Phase**:
- "Initiated" → "Active" (blue) via "Launch"
- "Active" has a self-loop labeled "Wait (for HITL)" with internal actions "Notify" and "Callback"
3. **Termination Paths**:
- **Success Path**: Active → Finish (dark blue) via "Complete" → End via "Close"
- **Error Path**: Active → Abort (red) via "Cancel" → End via "Close"
### Key Observations
- The "Active" state is central, with both success and error paths originating from it.
- The "Wait (for HITL)" loop suggests human-in-the-loop intervention capability.
- "Abort" is the only non-terminal state that directly transitions to "End," bypassing "Finish."
- Color coding emphasizes critical states (red for Abort, blue for Active/Finish).
### Interpretation
This diagram models a robust workflow with:
1. **Sequential Initialization**: Clear setup steps before activation.
2. **Flexible Execution**: The HITL loop allows iterative refinement during active processing.
3. **Graceful Degradation**: The "Abort" state provides an immediate exit path, ensuring system stability.
4. **Resource Management**: The "Close" transition from both Finish and Abort suggests shared cleanup procedures.
The design prioritizes fault tolerance, with explicit error handling and recovery mechanisms. The HITL loop indicates a system requiring human oversight during critical operations, while the direct Abort→End path minimizes downtime during failures.
</details>
Figure 2 Agent State Transition Diagram Within the 3-Pillar Model
Initiation State. During initiation, a human defines the scope, context, and objectives of the agent’s work. This stage establishes the foundation for safe collaboration. For example, a Research Agent tasked with supporting a product’s go-to-market strategy must receive a clearly defined configuration that includes market segments, data sources, and success criteria. By setting these parameters, the human ensures that the agent’s goals are properly aligned with organizational objectives and ethical standards. This stage also serves as a point of human control, where configurations, role definitions, and constraints can be verified before the agent begins operation.
Active State. Once launched, the agent enters its active state, where it performs the actions for which it was designed. For instance, a Research Agent may conduct web searches and synthesize findings. Likewise, a Payment Agent may initiate payment transactions. A Collection Letter Agent may draft personalized communications based on debtor information and credit conditions. During this phase, activity recording and observability become essential. The environment must automatically generate activity journals that record the agent’s decisions, interactions, and results.
These logs enable oversight and provide a transparent record for post-task evaluation. Moreover, during this phase, the Human-in-the-Loop (HITL) mechanism plays an important role. When the agent encounters uncertainty or ambiguity, it may consult a human collaborator for guidance. Depending on task complexity and risk level, human involvement can vary from direct supervision to collaborative decision-making to minimal observation. Transparency allows both sides to know when and why such handoffs occur.
Abort State. Both human operators and authorized AI subsystems should have the ability to abort or suspend an active agent when necessary. Abort events may occur if the agent cannot fulfill its mission due to missing resources, time constraints, or safety violations. The authority to abort should follow clearly defined governance rules, reflecting the contractual and regulatory conditions under which the agent operates.
Finish State. When an agent finishes or terminates its task, it should produce a clear output along with a record of its entire operation. Transparency requires three complementary forms of documentation:
1. State transition records: Marking changes from initiation to finish.
1. Work progress records: Showing the detailed actions taken by the agent.
1. HITL records: capturing every human–AI interaction and decision.
These records serve as the backbone of transparency within the agent operating environment. They allow developers, regulators, and users to reconstruct events, assess system performance, and identify opportunities for improvement. Without sufficient transparency, human collaborators cannot effectively supervise agent behavior, learn from outcomes, or develop trust in autonomous agent systems. While these three record types are not exhaustive, they represent the minimum information required to achieve acceptable transparency. In practice, the agent system may also maintain additional journals, such as system logs, user feedback logs, performance metrics, and other operational traces, to further support monitoring, analysis, and continuous improvement.
1. Pillar Two: Accountability and Responsibility
While transparency answers what happened, accountability answers why it happened and who is responsible. In the previous section on the evolutionary path, we emphasized that autonomy must be earned gradually. Accountability provides the ethical and operational framework that makes this process safe. As AI agents gain more independence, the environment must ensure that each decision, whether made by a human or AI, is traceable to its source and understandable and explainable in context.
Achieving accountability requires comprehensive decision journaling that records not only the outcomes but also the reasoning and contextual factors behind each choice. This is closely related to the principle of explainability in AI. Agents must be able to provide, upon request, the rationale for their decisions, including the data sources consulted, the constraints considered, and the degree of confidence associated with their outputs.
A practical example illustrates this need. Suppose an automated food-ordering agent failed to account for a customer’s allergy to wheat or soy, resulting in a serious medical incident. In such a case, assigning responsibility requires a clear understanding of each participant’s role in the agentic workflow. Was the customer’s input ambiguous? Did a human worker at the restaurant fail to verify the order details during preparation? Did the AI agent miscommunicate the constraints? Or did the underlying language model generate an inaccurate summary of the order that omitted critical information? Without explicit records of each decision and the reasoning behind it, no clear accountability can be established or assigned.
Accountability serves both corrective and developmental purposes. From a legal or regulatory perspective, it ensures that organizations can assign responsibility when things go wrong. From a technical perspective, it enables learning and continuous improvement. By identifying which part of the agentic workflow led to an undesirable outcome, AI and engineers can make targeted improvements to prevent recurrence. Accountability thus becomes the engine of continuous improvement within the agent ecosystem, reinforcing the learning loop necessary for safe autonomy and growing trust.
1. Pillar Three: Trustworthiness & Human-in-the-Loop
The third pillar, trustworthiness, unites and build on top of the previous two. Transparency makes operations visible, accountability clarifies responsibility, and trustworthiness converts these attributes into confidence and willingness to rely on autonomous systems.
As discussed in the evolutionary path section, human trust is not granted by design but earned through consistent, observable, and reliable performance. During the early phases of adoption, enterprises and end users will trust AI agents only if they can see clear boundaries of control and know that humans can intervene when necessary. Therefore, the operating environment must include mechanisms to specify risk thresholds and escalation rules that determine when human oversight is required.
For example, in domains such as finance or healthcare, high-risk actions such as large transactions or clinical recommendations should automatically trigger human review. These checkpoints form structured Human-in-the-Loop interventions that ensure oversight at critical moments. Conversely, in high-volume, low-risk tasks, AI may operate independently for greater efficiency. Over time, as the system demonstrates reliability, the frequency of human interventions can be gradually reduced, following the same incremental trust-building logic that was illustrated in the autonomous driving analogy. However, any decision to increase the level of autonomy must be explicitly approved by a human authority and clearly documented. In addition, periodic spot checks should be conducted to verify safety and correctness, even after incremental advances in autonomous decision-making have been introduced.
Trustworthiness also recognizes that in some contexts, AI can be more dependable than humans. Machines do not suffer from fatigue, emotional fluctuation, or inconsistency, and in repetitive or data-intensive tasks, AI may exhibit higher reliability than human operators. Accordingly, a trustworthy operating environment must support mutual confidence. Humans must trust AI agents to function within clearly defined safety boundaries, while AI systems must be designed to rely on validated human inputs and to defer judgment appropriately when required. The objective is not blind reliance but calibrated trust, grounded in empirical performance evidence and shared accountability. To support this calibration, every decision and every change must be properly recorded and remain auditable.
Finally, trustworthiness ensures that when failures occur, they do not propagate unchecked. The environment must include robust fallback and recovery mechanisms that detect anomalies based on historical patterns, suspend automated actions, and transfer control to human operators before harm occurs. These safety measures ensure that risk remains manageable in very large-scale deployments with thousands of concurrently operating agents, even as autonomy levels continue to increase.
1. Integrating the Three Pillars in the Evolutionary Process
The Three-Pillar Model is not a theoretical abstraction but a practical extension of the evolutionary approach described earlier. As agents progress from Assisted to Collaborative, to Supervised Autonomy, and ultimately to Full Autonomy under Human Governance, the balance among the three pillars must evolve in parallel with each successive stage of autonomy.
In early stages, transparency plays the dominant role, ensuring that every action is observable, explainable, and auditable. As systems progress into collaborative stages, accountability becomes increasingly important because humans and AI share responsibility for decisions and outcomes. In the later stages, once agents have demonstrated consistent reliability and alignment, trustworthiness becomes the decisive factor that enables increasing levels of autonomy. Importantly, companies and users will always retain the ability to determine the degree of autonomy they are comfortable and willing to grant to different agents operating in their environments. This flexibility allows organizations to balance efficiency with risk tolerance, enabling a gradual and confident transition toward greater autonomy while maintaining control and trust throughout the process.
These pillars together form a feedback ecosystem in which humans and AI learn from each other. Transparency provides data for accountability. Accountability identifies what needs improvement. Trustworthiness motivates greater delegation of control. Through this cycle, autonomy grows safely and progressively.
In conclusion, the 3PM for agent creation, deployment, and operation establishes the essential conditions for safe evolution toward autonomous agents. It ensures that the journey from collaboration to independence occurs within a structure that is observable, responsible, and trustworthy. Only through such an environment can enterprises accelerate adoption, build user confidence, and achieve the full potential of AI agents while preserving human values and safety.
1. A Sample Use Case: Group Email Agent
To illustrate the application of the Three-Pillar Model within a practical context, we consider a Group Email Agent operating in an enterprise-grade agentic environment. This use case demonstrates how transparency, accountability, and trustworthiness jointly ensure safe and effective collaboration between humans and AI.
A Group Email Agent is a common and valuable application for enterprises that need to compose, review, and distribute communications to internal employees, customers, or business partners. Such messages can include policy updates, marketing announcements, product release communications, event invitations, or crisis management notifications. Because of their wide impact, group emails typically require coordination among multiple stakeholders, including representatives from the business unit, marketing and communications teams, legal and compliance departments, and senior management. These participants contribute to drafting, editing, verifying, and approving both the message content and the list of recipients. The Group Email Agent acts as an author, a coordinator, and executor, automating repetitive tasks while preserving human oversight where contextual understanding and judgment are critical.
Figure 3 displays the agent activity records captured by the system throughout the lifecycle of a Group Email Agent instance. These records include state transitions, detailed task progress, and Human-in-the-Loop interactions, illustrating how the operating environment maintains continuous transparency and traceability from initiation to completion.
<details>
<summary>SafeAIAgent-img003.png Details</summary>
### Visual Description
## Screenshot: Technical Status Log
### Overview
The image displays a technical status log with a blue header labeled "Status" and a vertical list of bullet-pointed events. The log includes timestamps, technical workflows, and system status indicators. A green dot labeled "Active" appears at the bottom, indicating the current operational state.
### Components/Axes
- **Header**: Blue bar with white text "Status".
- **Main Content**:
- 14 bullet points detailing system events (e.g., email generation, API calls, HITL workflows).
- HTML-like tags (e.g., `[HITL]`, `[AMP]`) and technical parameters (e.g., `action=Approval`, `model: gpt-oss:20b`).
- Email address: `edwardgem@gmail.com`.
- Green dot labeled "Active" at the bottom.
- **Footer**: No explicit footer, but the green dot acts as a status indicator.
### Detailed Analysis
1. **Event Sequence**:
- **Email Generation**: "Calling LLM to generate email" initiates the process.
- **Agent Interaction**: "Receive call from agent work item processing" with a response to modify an event date to October 31.
- **HITL Workflow**:
- `[HITL] hitl flow finished; waiting for human response`.
- `[HITL] AMP workitem inserted` with a UUID and `action=Approval`.
- **Resolution Details**:
- `[HITL] process_how resolved channel: amp`.
- `[HITL] process_what resolved action: approval`.
- `[HITL] process_who resolved initiator email: edwardgem@gmail.com`.
- **LLM Evaluation**:
- `[HITL] process_when LLM evaluation result: met=true; reason=The progress records include a "generated html email" event at 2025-10-20 08:24:37...`.
- **Configuration**:
- `[HITL] HITL config: {"enable":true,"how":"amp","what":"approval","when":"after email is generated","who":"initiator"}`.
- **API Interaction**:
- `[HITL] received hitl call from instance - 1 time`.
- "Completed generating email using LLM (model: gpt-oss:20b)".
- "Calling LLM to generate email".
- Green dot labeled "Active".
- "Receive API call: generate-send".
2. **Timestamps**:
- Explicit timestamp: `2025-10-20 08:24:37` (LLM evaluation result).
- Implicit timestamps inferred from event order (e.g., "after email is generated").
3. **Technical Parameters**:
- UUIDs: `email-20251020082318-20251020082444`.
- Model identifier: `gpt-oss:20b`.
### Key Observations
- The log captures a multi-step workflow involving LLM (Large Language Model) and HITL (Human-in-the-Loop) processes for email generation and approval.
- The system is actively processing an email generation task, as indicated by the "Active" status.
- The HITL workflow includes explicit approval steps and initiator identification (`edwardgem@gmail.com`).
- The LLM model used is `gpt-oss:20b`, suggesting a specific version or configuration.
### Interpretation
This log represents a real-time technical workflow for automated email generation with human oversight. The process begins with LLM-driven email creation, followed by agent interaction to modify event details. The HITL component introduces approval steps, ensuring human validation before finalizing the email. The system’s "Active" status and the final API call (`generate-send`) suggest the email is being prepared for dispatch. The inclusion of timestamps and UUIDs indicates traceability and auditability, critical for debugging or compliance. The configuration parameters (`how: amp`, `what: approval`) imply a structured decision-making framework, likely part of a larger automation pipeline. The email address `edwardgem@gmail.com` identifies the human initiator, linking the technical process to a specific user.
</details>
Figure 3: Agent Activity Records Captured While Running a Group Email Agent
Figure 4 demonstrates a user interface (UI) portal that enables an authorized human participant to provide contextual inputs, review agent activities, and intervene when necessary. This interface supports Human-in-the-Loop collaboration by allowing users to configure, guide, or correct the agent’s actions in real time, ensuring that human oversight remains an integral part of the agent’s operational workflow.
<details>
<summary>SafeAIAgent-img004.png Details</summary>
### Visual Description
## Screenshot: Group Email Agent Interface
### Overview
The image depicts a user interface for managing a "Group Email Agent" instance. The interface includes navigation controls, status indicators, a summary section with AI-assisted content, actionable instructions, and decision buttons. The layout is structured for administrative review and modification of email automation tasks.
### Components/Axes
1. **Header Section**:
- **Back Button**: Blue text with leftward arrow (top-left corner).
- **Title**: "Group Email Agent" in bold black text (center-left).
- **Instance ID**: `email-20251020082318` in gray text (right of title).
- **Status Indicators**:
- "active" in green text on a light green background (top-right).
- "Abort Agent" button in gray with white text (adjacent to status).
2. **Summary Section**:
- **Label**: "Summary" with downward arrow (left-aligned, below header).
- **AI Icon**: Purple square with "AI" text and a pink plus symbol (right of "Summary").
3. **Action Section**:
- **Label**: "Action" with downward arrow (left-aligned, below Summary).
- **Instruction Text Box**:
- Text: "Please review this agent instance and choose an action: Change the event date to October 31, include the day of the week. Also change the time to 1-2 pm."
- Positioned centrally, below the Action label.
4. **Action Buttons**:
- **Approve**: Green button with white text (bottom-center).
- **Reject**: Red button with white text (bottom-right).
- **Modify**: Blue button with white text (bottom-left).
5. **Additional Elements**:
- **Refresh Button**: Blue circular arrow (right of Summary section).
### Detailed Analysis
- **Textual Content**:
- The instruction text box specifies modifying an event to October 31 (day of the week unspecified) and adjusting the time to 1-2 PM.
- No numerical data or categorical labels are present beyond the Instance ID.
- **Spatial Grounding**:
- Header elements (Back, Title, Status) occupy the top 15% of the screen.
- Summary and Action sections are stacked vertically, occupying ~40% of the screen.
- Action buttons are aligned horizontally at the bottom 20% of the screen.
### Key Observations
- The interface is designed for administrative control, with clear visual hierarchy (bold titles, color-coded buttons).
- The AI icon suggests automated content generation or suggestions in the Summary section.
- The "active" status implies the agent is currently operational, while "Abort Agent" provides emergency termination.
### Interpretation
This interface likely belongs to an email automation system where agents handle scheduled group communications. The presence of AI-assisted content ("AI" icon) indicates machine learning integration for drafting or modifying emails. The three action buttons ("Approve," "Reject," "Modify") suggest a workflow where administrators validate or adjust AI-generated proposals before execution. The "active" status and abort functionality imply real-time monitoring capabilities, critical for time-sensitive email campaigns. The lack of numerical data suggests this is a configuration/review screen rather than a performance dashboard.
</details>
Figure 4: UI for Obtaining Human-in-the-Loop Inputs During an Agentic Workflow
Figure 5 illustrates how users can interact with the large language model (LLM) to discover, query, and inspect the progress of agents operating within the environment. Through conversational interfaces, users can retrieve explanations, review activity logs, and monitor task completion status. This interactive transparency fosters mutual understanding and trust between humans and AI agents, allowing confidence to grow naturally as agents demonstrate reliability and accountability over time.
<details>
<summary>SafeAIAgent-img005.png Details</summary>
### Visual Description
## Screenshot: Chat Interface with Agent Workflow Summary
### Overview
The image shows a chat interface with a technical summary of an agent's workflow. The summary includes key takeaways, a user query about task duration, and an AMP (Agent's response) detailing the workflow timeline. The interface includes UI elements like a "Switch to Summary" button, a "Send" button, and a prompt to ask questions about the agent instance.
### Components/Axes
- **UI Elements**:
- Top-left: "Chat (with gpt-oss:20b)" label with a blue speech bubble icon.
- Top-right: Trash can icon.
- Header: "Switch to Summary" button (gray).
- Footer: "Ask me anything about this agent instance" input field with a "Send" button (blue).
- **Text Content**:
- **Key Takeaways**:
1. The agent tried three times to generate an email, saving HTML and waiting for HITL approval.
2. Three HITL work-items were created via the AMP channel, all pending until the HITL step was skipped.
3. After skipping HITL, the agent sent the email and logged the "sent email" event.
- **User Query**: "how much time did it take to complete this agent work?" (timestamp: 6:16 am).
- **AMP Response**:
- Total workflow duration: **8 minutes 52 seconds**.
- Breakdown:
- Start: 15:05:35 ("I'm generating email").
- End: 15:14:27 ("sent email").
- Duration: 8 minutes 52 seconds.
### Detailed Analysis
- **Workflow Steps**:
- The agent attempted email generation three times, each time saving HTML and awaiting HITL approval.
- Three HITL work-items were created via the AMP channel but remained pending until the HITL step was skipped.
- After skipping HITL, the agent sent the email and logged the event.
- **Time Breakdown**:
- Start time: 15:05:35.
- End time: 15:14:27.
- Total duration: 8 minutes 52 seconds (calculated as 557 seconds).
### Key Observations
- The agent’s workflow involved multiple retries (3 attempts) for email generation, indicating potential instability or validation checks.
- HITL work-items were created but not resolved until the step was skipped, suggesting a dependency on human approval.
- The total task duration (8m52s) is under the stated "just under nine minutes" threshold.
### Interpretation
- **Efficiency**: The agent completed the task in 8m52s, demonstrating a streamlined process after skipping HITL.
- **HITL Dependency**: The creation of three pending HITL work-items highlights a bottleneck in the workflow, resolved by bypassing human approval.
- **Retry Mechanism**: Three email generation attempts suggest the agent may handle errors or validation failures iteratively.
- **Temporal Precision**: The exact start/end times and duration provide granular insight into the agent’s performance.
**Note**: No numerical data visualization (e.g., charts) is present; the image focuses on textual workflow documentation.
</details>
Figure 5: Transparency Enables Human-AI Collaboration with Trustworthiness in Agent Operation
By systematically capturing and recording agent activities, the operating environment enables a high degree of transparency that supports comprehensive analytics on both agent behavior and Human-in-the-Loop interactions. This transparency makes it possible to surface aggregated insights through a dashboard component, which serves as a central interface for monitoring, managing, and improving a large-scale agent operating environment. The dashboard plays a critical role in supporting operational oversight, performance evaluation, and continuous improvement, while also informing decisions about when and how to safely increase the level of autonomy within agentic workflows.
Figure 6 illustrates the dashboard view, which presents a collection of analytic charts summarizing agent execution patterns, lifecycle states, intervention frequencies, and HITL engagement metrics. These visualizations allow users to quickly assess system health, identify bottlenecks, detect anomalous behavior, and understand where human involvement is most frequently required. By consolidating this information at scale, the dashboard enables organizations to manage thousands of concurrently operating agents in a controlled and informed manner.
In addition to static visualization, the dashboard integrates interactive analysis through a natural language interface powered by a large language model. As shown in Figure 7, selecting a chart allows users to open an LLM-driven chat window that generates a contextual analysis report explaining observed trends and patterns. Users can further engage in dialogue with the LLM to ask follow-up questions, explore root causes, and derive business insights related to efficiency, risk, and workflow optimization. This combination of visual analytics and conversational analysis supports deeper understanding of agentic behavior and helps users identify targeted opportunities to refine processes, improve safety, and incrementally advance the autonomy of the overall agentic workflow system.
<details>
<summary>SafeAIAgent-img006.png Details</summary>
### Visual Description
## Dashboard: Real-time Adoption & Quality Trends
### Overview
The dashboard presents a comprehensive analysis of agent performance, quality metrics, and operational efficiency across multiple dimensions. It includes time-series data, categorical distributions, and comparative visualizations spanning August 1, 2025, to December 3, 2025.
---
### Components/Axes
#### Header Section
- **Date Range**: Start: 08/01/2025 | End: 12/03/2025
- **Filters**:
- Agent Types: 5 selected (dropdown)
- User: All users (dropdown)
- Granularity: Weekly (dropdown)
#### Volume & Adoption Section
1. **Total Agents Over Time**
- **X-axis**: Dates (2025-08-11 to 2025-12-01)
- **Y-axis**: Agent count (0–8000)
- **Legend**: Single blue line
- **Trend**: Sharp rise to ~6000 agents by 2025-09-15, followed by a decline to ~1500 by 2025-12-01.
2. **Volume by Agent Type**
- **X-axis**: Dates (2025-08-11 to 2025-12-01)
- **Y-axis**: Percentage (0–100%)
- **Legend**:
- Customer-Support (blue)
- Group-Email (orange)
- Invoice-Payment (green)
- Newsletter (purple)
- Research (pink)
- **Trend**: Invoice-Payment dominates (~75% of stack), with Customer-Support (~20%) and Group-Email (~10%) as secondary contributors.
3. **Top Agent Types**
- **X-axis**: Count (0–40,000)
- **Y-axis**: Agent types (horizontal bars)
- **Values**:
- Invoice-Payment: ~35,000
- Customer-Support: ~15,000
- Group-Email: ~10,000
- Research: ~8,000
- Newsletter: ~3,000
#### Quality & HITL Section
1. **Finished vs Aborted**
- **X-axis**: Dates (2025-08-11 to 2025-12-01)
- **Y-axis**: Count (0–8000)
- **Legend**:
- Finished (green)
- Aborted (pink)
- **Trend**: Finished tasks peak at ~6000, while aborted tasks remain below 1000.
2. **Error Distribution**
- **Pie Chart Segments**:
- None (light blue): ~85%
- Validation Error (dark blue): ~10%
- Timeout (orange): ~3%
- User Cancelled (red): ~1.5%
- System Error (purple): ~0.5%
3. **HITL Rate (%)**
- **X-axis**: Dates (2025-08-11 to 2025-12-01)
- **Y-axis**: Percentage (0–80%)
- **Legend**: Purple line
- **Trend**: Starts at ~75%, drops sharply to ~40% by 2025-10-15, then stabilizes with minor fluctuations.
#### Performance & Operations Section
1. **Average Duration by Agent Type**
- **X-axis**: Agent types (Customer-Support, Invoice-Payment, Research)
- **Y-axis**: Duration (0–333ms)
- **Values**:
- Customer-Support: ~167ms
- Invoice-Payment: ~333ms
- Research: ~167ms
2. **Queue Wait Trend**
- **X-axis**: Dates (2025-10-05 to 2025-12-04)
- **Y-axis**: Wait time (0–60s)
- **Legend**: Orange line
- **Trend**: Fluctuates between 45s and 55s with no clear upward/downward pattern.
3. **Concurrency Heatmap**
- **X-axis**: Dates (2025-10-05 to 2025-12-04)
- **Y-axis**: Concurrency levels (0, 6, 12, 23)
- **Legend**: Purple lines
- **Trend**: Consistent horizontal lines indicate stable concurrency levels across dates.
---
### Key Observations
1. **Agent Volume**: Sharp initial growth followed by a steep decline suggests a temporary surge in demand or resource allocation.
2. **Agent Type Dominance**: Invoice-Payment handles the majority of workload, while Newsletter has the smallest footprint.
3. **Quality Metrics**: High HITL rates (>75%) initially drop significantly, indicating potential process inefficiencies or automation improvements.
4. **Error Patterns**: Validation errors and timeouts are the primary failure modes, with user cancellations being rare.
5. **Operational Efficiency**: Invoice-Payment tasks take twice as long as Customer-Support/Research tasks, suggesting potential optimization opportunities.
---
### Interpretation
The data reveals a dynamic operational landscape with significant shifts in agent activity and quality metrics. The initial surge in agents (Volume & Adoption) correlates with high HITL rates, suggesting manual intervention during peak demand. The subsequent decline in agents coincides with a drop in HITL rates, possibly indicating automation adoption or process streamlining.
The dominance of Invoice-Payment tasks, combined with their longer average duration, highlights a potential bottleneck. Meanwhile, the stability in queue wait times despite fluctuating agent numbers suggests effective load balancing. Error distribution emphasizes the need to address validation and timeout issues, which account for ~13% of failures.
The concurrency heatmap’s uniformity implies consistent system load, but the lack of granularity (e.g., hourly breakdowns) limits deeper analysis. Overall, the dashboard underscores the need for targeted optimizations in high-volume, high-duration tasks and error-prone processes.
</details>
Figure 6 Analytic Charts Illustrating Realtime Adoption and Quality Trends of the Agentic System
<details>
<summary>SafeAIAgent-img007.png Details</summary>
### Visual Description
## Screenshot: Chat Analysis of "Total Agents Over Time" Chart
### Overview
The image is a screenshot of a chat interface between a user and an AI assistant (AMP) discussing the analysis of a chart titled "Total agents over time." The user requested trend identification, anomaly detection, and key takeaways. The AI's response includes a comparative analysis with HITL (High-Volume Initiator/Reviewer Tracking) rate charts, observations about trends, and interpretations of anomalies.
### Components/Axes
- **Chat Interface Elements**:
- **Header**: "Chat (llama3.1)" with a close button (X) in the top-right corner.
- **Timestamp**: "8:16 pm" aligned to the right of the user's message.
- **User Message**: Gray background box with the query:
*"Analyze the chart 'Total agents over time'. Highlight trends, anomalies, and key takeaways."*
- **AI Response**: Structured text with bold headings and bullet points.
- **Textual Content**:
- **Headings**:
- "AMP:"
- "Comparison of Total Agent Over Time and HITL Rate Charts"
- "Chart Analysis"
- "HITL Rate Charts"
- "Comparison with Total Agent Over Time Chart"
- **Key Text**:
- *"We will compare the 'Total agents over time' chart with the HITL rate charts to identify potential correlations and insights."*
- *"The HITL rate charts provide information on the top initiators and reviewers of agent runs."*
- *"The significant increase in total agent runs around 2025-09-01 is also reflected in the HITL rate charts..."*
- *"The decline from 2025-11-03 to 2025-11-10 observed in the 'Total agents over time' chart is not as pronounced in the HITL rate charts..."*
### Detailed Analysis
- **Trends**:
- **Increase**: A notable rise in total agent runs around **2025-09-01**, mirrored in HITL rate charts.
- **Decline**: A drop in total agent runs from **2025-11-03 to 2025-11-10**, less evident in HITL charts.
- **Anomalies**:
- The divergence between the "Total agents over time" chart and HITL rate charts during the November 2025 decline suggests potential user-specific or technical factors.
- **Key Takeaways**:
- The September 2025 surge indicates heightened activity by top initiators/reviewers.
- The November 2025 decline may stem from isolated incidents (e.g., user behavior, technical glitches) rather than systemic trends.
### Key Observations
1. **Temporal Correlation**: The September 2025 increase aligns across both datasets, suggesting a systemic driver (e.g., policy change, marketing campaign).
2. **Divergence in November 2025**: The muted decline in HITL charts implies the drop in total agents is not uniformly experienced by top contributors.
3. **HITL Rate Charts**: These focus on high-volume agents, filtering out less active users, which explains their reduced sensitivity to the November decline.
### Interpretation
- **Systemic vs. Isolated Factors**: The September surge likely reflects a broad organizational shift (e.g., new tool adoption), while the November decline may involve specific users or technical issues (e.g., login failures, workflow bottlenecks).
- **HITL Charts as a Filter**: By focusing on high-volume agents, these charts obscure smaller-scale fluctuations, emphasizing the need for granular analysis to diagnose anomalies.
- **Actionable Insight**: Investigate the November 2025 period for user-specific issues (e.g., access logs, error reports) and validate the September surge with operational records (e.g., onboarding campaigns, system updates).
## Notes
- No visual chart or diagram is present in the image; all data is textual.
- Dates and trends are inferred from the AI's textual analysis, not direct visual inspection.
- The chat interface uses a minimalist design with bold headings and bullet points for clarity.
</details>
<details>
<summary>SafeAIAgent-img008.png Details</summary>
### Visual Description
## Screenshot: Technical Analysis Report
### Overview
The image is a screenshot of a technical analysis report with structured sections: **Key Takeaways**, **Possible Explanations**, **Recommendations**, and a command input area. The document discusses trends in agent runs, HITL rates, and actionable steps for investigation.
### Components/Axes
- **Headers**:
- "Key Takeaways" (bold, top-left)
- "Possible Explanations" (bold, mid-left)
- "Recommendations" (bold, bottom-left)
- **Text Blocks**:
- Bullet points with bolded titles (e.g., "Changes in user behavior", "Technical issues")
- Numbered recommendations (1–2)
- **UI Elements**:
- Hyperlink: "Regenerate report" (blue, underlined, bottom-left)
- Command input box: "Type a command..." (white, bordered, bottom-center)
- "Send" button: Blue, rectangular, bottom-right
### Detailed Analysis
#### Key Takeaways
1. **Growth in agent runs**:
- Growth around **2025-09-01** driven by top users initiating/reviewing agents.
2. **HITL rate decline**:
- Stable HITL rates during decline from **2025-11-03** to **2025-11-10**, suggesting user-specific or technical issues.
#### Possible Explanations
1. **User behavior changes**:
- Top users adjusting behavior may explain growth (2025-09-01) and decline (2025-11-03 to 2025-11-10).
2. **Technical issues**:
- Infrastructure/configuration problems affecting specific users/processes, causing activity variations.
#### Recommendations
1. Investigate user behavior changes around **2025-09-01** and **2025-11-03** to assess impact on agent activity.
2. Analyze technical logs to identify infrastructure/configuration issues contributing to agent run variations.
### Key Observations
- The document focuses on correlating temporal trends (agent runs, HITL rates) with potential root causes (user behavior, technical issues).
- Dates are explicitly tied to events (e.g., growth at 2025-09-01, decline starting 2025-11-03).
- Recommendations are action-oriented, emphasizing investigation and log analysis.
### Interpretation
This report appears to address anomalies in agent activity and HITL rates, proposing a dual approach:
1. **Behavioral analysis**: To determine if user actions (e.g., initiating agents) drove observed trends.
2. **Technical diagnostics**: To rule out system-level issues (e.g., infrastructure failures) as causes.
The inclusion of a command input box ("Type a command...") suggests this is part of an interactive tool, possibly for generating or refining reports. The "Regenerate report" link implies the document may be dynamically generated, allowing users to refresh data or adjust parameters.
No numerical data or visualizations (e.g., charts) are present; the focus is on textual analysis and procedural guidance.
</details>
Figure 7 LLM-Integrated Chat Interface Enabling Analytic Insights From Agent Dashboard Charts
1. Next Steps and Future Work
The work presented in this paper establishes both a conceptual framework and an operational foundation for developing safe, transparent, and trustworthy AI agents through the 3PM. Building on insights from prior research in Human-in-the-Loop systems and safe AI, the model is extended into a comprehensive operational approach supported by practical principles and implementation guidelines. As a practical and applicable framework, the 3PM is lightweight, easy to understand, and straightforward to apply in the development, deployment, and operation of large-scale, enterprise-grade agentic systems. At the same time, it is grounded in a complete and coherent theoretical foundation and is designed to evolve as the scope, scale, and complexity of the agentic environment expand. The next phase of this initiative focuses on translating these principles into real-world practice, ensuring that both industry and society can fully benefit from the responsible adoption of autonomous agents. To achieve this, three primary work streams have been initiated to extend, validate, and operationalize the ideas introduced in this study.
1. Public Deliberation through the Stanford Deliberative Democracy Lab
The first work stream involves a collaboration with the Deliberative Democracy Lab (DDL) at Stanford University, which is conducting a series of public deliberative forums focused on the social and ethical dimensions of AI agents [17, 18]. These forums bring together a diverse range of stakeholders, including AI industry leaders, policymakers, researchers, and members of the public, to engage in structured, open discussions about the Three Pillars of transparency, accountability, and trustworthiness.
In the initial phase, the DDL will conduct forums in North America and in India. The goal of this initiative is to bridge the gap between technological innovation and societal readiness. By involving the public in open, informed conversations, this work stream seeks to better understand how people perceive the risks and benefits of autonomous agents, what level of transparency they expect, and what safeguards they require to build trust. The insights from these dialogues will guide both technical and policy frameworks, ensuring that the development of AI agents aligns with public values and expectations across both business and consumer contexts.
Through these deliberative processes, the AI community can establish mutual understanding and legitimacy around agent governance, helping society evolve toward an era of AI-enabled collaboration rather than resistance or fear.
1. Industry Collaboration through the Safe AI Agent Consortium
The second work stream focuses on industry collaboration through the Safe AI Agent Consortium, an emerging alliance of leading organizations that share a commitment to advancing the responsible use of autonomous agents [19]. The consortium’s core members include Anthropic, Cohere, DoorDash, Meta, Microsoft, Oracle, PayPal, Stanford, and other key players across academia, technology and enterprise sectors.
This group is jointly developing a set of industry guidelines and best practices grounded in the 3PM. These guidelines aim to operationalize the concepts of transparency, accountability, and trustworthiness in a way that developers, implementers, and users can readily apply to real-world AI systems. By creating common standards for agent design, documentation, observability, and governance, the consortium seeks to promote safe adoption of AI agents at scale. This initiative enables enterprises to capture productivity gains without compromising human oversight or public trust.
The consortium’s open work may also expand to developing shared benchmarks, safety testing protocols, and interoperability frameworks for agent operating environments. These outcomes will serve as practical tools for both startups and large organizations to evaluate the maturity, safety, and reliability of their agentic systems. Through collective action and transparency among participants, this initiative aspires to make safety and responsibility a competitive advantage in the growing agent economy.
1. Open Tools and the Three-Pillar Agent Operating Environment
The third work stream extends this research into applied development and community tooling. The objective is for industry leaders and startups to design and release a set of open-source tools and frameworks that embody the 3PM and accelerate the adoption of safe agentic systems. This includes the creation of an agent operating environment, as illustrated in this paper, that integrates transparency, accountability, and trustworthiness by design across the full agent lifecycle.
This environment will provide a standardized foundation for safe and effective agent operations, offering key capabilities such as:
- Agent activity logging and lifecycle tracking to ensure full transparency and traceability across initiation, execution and completion stages.
- Decision journaling and explainability modules to support accountability by recording the reasoning, context, and outcomes behind each agent decision.
- Configurable human oversight controls and fallback mechanisms to maintain trustworthiness and provide dynamic risk management through defined intervention thresholds.
- AI generated analytics derived from agent activity logging and decision journals, with LLM deployed throughout the 3PM operating environment to enable interactive monitoring, health assessment, and insight generation. This capability allows users to better understand system behavior, identify improvement opportunities, and make informed decisions about progressively increasing levels of agent autonomy.
- AI-assisted 24x7 monitoring of agentic workflows to continuously learn behavioral patterns, detect anomalies, and trigger timely human involvement when necessary to preserve system safety and security.
By providing a shared technical foundation, this work stream aims to lower the entry barrier for organizations to adopt AI agents. It allows developers to embed safety and governance principles from the outset, rather than retrofitting compliance and oversight after deployment. The tools will be open for collaboration and extension by the research and developer communities, designed to integrate with existing agentic interoperability standards such as the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) communication protocol. This openness will encourage cross-industry experimentation, validation, and interoperability, fostering a unified ecosystem where safe, transparent, and accountable AI agents can evolve and operate seamlessly across different environments.
Through continuous contribution from the developer community and iterative improvement, the resulting ecosystem will foster a trusted agent economy in which innovation can advance both responsibly and efficiently. Over time, this environment may serve as a reference implementation for regulators, researchers, and practitioners seeking to harmonize safety and governance standards across industries and geographic regions, thereby accelerating the safe and scalable adoption of autonomous agents worldwide.
1. Conclusion
This paper has presented a conceptual and operational framework for developing safe, transparent, and trustworthy AI agents through the 3-Pillar Model (3PM), consisting of Transparency, Accountability, and Trustworthiness. Building upon prior research in Human-in-the-Loop (HITL) systems, reinforcement learning with human feedback, and collaborative AI, this model provides a practical foundation for guiding the evolution of AI agents from assisted to fully autonomous operation. The framework emphasizes that autonomy must be achieved through a gradual, verifiable process in which trust is earned over time, rather than assumed by design.
We have argued that the development of autonomous agents parallels the evolutionary path of autonomous driving, where safety, reliability, and human confidence were cultivated through progressive stages of shared control. Similarly, the journey toward trustworthy AI autonomy requires environments that support visibility, ethical reasoning, and human collaboration. The proposed Three-Pillar Model ensures that every stage of agent development and deployment remains transparent, accountable, and grounded in timely and appropriate human oversight. Transparency provides observability into agent behavior and decision-making processes; accountability ensures that both actions and decisions are traceable, explainable, and correctable; and trustworthiness transforms these safeguards into lasting confidence among users, organizations, and the broader public.
To move from concept to practice, this research has initiated three complementary work streams. The first engages the public through the Deliberative Democracy Lab at Stanford University, facilitating informed dialogue between citizens and AI industry leaders about the social implications of agent transparency, accountability, and trust. The second advances industry collaboration through the Safe AI Agent Consortium, uniting leading technology organizations to establish shared best practices, evaluation benchmarks, and governance standards for safe agentic systems. The third work stream focuses on open tooling, with the goal of developing an open agent operating environment that embodies the Three-Pillar principles and supports interoperability among both native and external agents through protocols, including the Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, Agent Communication Protocol (ACP), and Agent Network Protocol (ANP).
Through these efforts, the 3PM progresses from theoretical construct to actionable framework, enabling the responsible evolution of autonomous agents. Through sustained collaboration across academia, industry, and society, we can shape a future in which AI agents operate in alignment with human values, advancing innovation while upholding safety, transparency, and ethical integrity.
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