Not Just a Tool: Why AI “Agents” Need a New Approach to Trust and Accountability
AI systems are rapidly moving beyond simple chatbots and data-crunching tools. Today, we entrust AI to schedule meetings, manage customer service, even make autonomous decisions in finance and operations. Tech leaders have started calling these more autonomous systems “AI agents,” suggesting they’re not just software tools but something akin to virtual representatives or assistants. However, hype and confusion abound – even industry experts admit “no one can seem to agree on what an AI agent is, exactly” (No one knows what the hell an AI agent is | TechCrunch). This lack of clarity isn’t just semantic; it has real consequences for how businesses deploy AI and manage risk. If an AI can act on your behalf, how do you trust it to do the right thing? Who is accountable if it goes wrong? And how do you govern something that operates with a degree of autonomy?
This article offers a practical framework for business leaders to answer these questions. By looking at AI agents through the lens of legal agency theory – the same principles that govern relationships between human agents and their principals – we can define what true AI agents are and how they differ from mere AI tools. We’ll explain why giving an AI a defined domain of authority is critical to managing it, and explore implications for trust, governance, and accountability when AI takes on agent-like roles. Finally, we introduce an emerging best practice for keeping AI systems trustworthy: an automated, authenticated AI “Bill of Materials” (BOM) that tracks an AI agent’s components and enforces policies based on its integrity. The goal is to provide clear, actionable insights so you can responsibly harness AI agents in your organization and stay ahead of the curve in an era when autonomous AI is poised to join the workforce (No one knows what the hell an AI agent is | TechCrunch).
What Are AI Agents? A Lesson from Legal Theory
In the business world, agency has a specific meaning: under legal theory, an agent is someone authorized to act on behalf of another party (the principal) in a way that can create binding obligations for the principal. In plain terms, if your employee or representative makes a deal on your behalf, you are on the hook for it. Several key components define a classic agency relationship, and they provide a powerful lens for understanding AI agents:
Authority: The agent has explicit or implicit permission from the principal to act in certain matters. In an AI context, this means a human or organization delegates power to the AI to carry out specific tasks or make decisions. An AI without granted authority is just a tool operating at a user’s direct command, not an independent agent.
Autonomy (within scope): The agent can exercise independent judgment and initiative within the limits set by the principal. Similarly, an AI agent might decide how to achieve a goal or react to circumstances on its own, but what it’s allowed to do is bounded by its mandate. This is what distinguishes a truly agentic AI from a basic program that only follows a fixed script.
Accountability: The principal remains ultimately responsible for the agent’s actions. If an AI agent does something on your behalf, you (or your company) are on the hook for the outcome, just as if a human employee had acted for you. You cannot use the AI as a convenient scapegoat to dodge liability – in legal and practical terms, the buck still stops with the humans in charge.
Enforceable Outcomes: Actions taken by an agent carry real weight. A human agent can sign contracts, spend money, or make promises that the principal must uphold. Likewise, an AI agent’s decisions or commitments should have consequences that matter in the real world – otherwise it’s more like an advisory tool. If an AI’s “actions” have no direct effect (for example, it only suggests options that a human must approve), then that AI is functioning as an assistant or tool, not as an agent.
Bringing these elements together, we can define an AI agent as an AI system explicitly authorized to act on behalf of a principal, operating with reasoned autonomy within a defined domain of authority, and producing outcomes that the principal is obligated to uphold. One succinct definition puts it this way: “An AI agent is an AI system explicitly authorized to act on behalf of a principal, with the ability to make reason-based decisions in line with the principal’s objectives, and whose actions create enforceable consequences within a defined domain.” In short, an AI agent isn’t just doing work for you; it’s doing work as an extension of you.
AI Tools vs. AI Agents: Understanding the Difference
Why draw this distinction between tools and agents? Because it changes how we design, deploy, and supervise AI in practice. Most current AI applications are tools – they require direct human input for each action and do not independently initiate significant changes in the world. For example, a code assistant like GitHub Copilot can suggest lines of code or help explain an error, but it’s ultimately the human programmer who decides what gets implemented. Copilot “primarily responds to human requests and requires human approval to carry out actions,” fitting the profile of a capable AI assistant rather than an autonomous agent. The AI helps a person work more efficiently, but it isn’t trusted to carry out an entire task on its own from start to finish.
In contrast, an AI agent can be entrusted with a goal and allowed to figure out the steps and execute them with minimal hand-holding (within agreed limits). A simple example is a Roomba robotic vacuum. You don’t manually drive a Roomba around your living room; you just tell it to clean, and it autonomously navigates the space, avoids obstacles, adjusts its path, and returns to its dock when done. It “exhibits significant autonomy within a defined domain,” namely the task of vacuuming your floors. The Roomba makes its own decisions on how to clean (which route to take, how many passes to make) without further human instruction. In this limited domain, it’s acting as an agent carrying out your intent (keeping the floor clean). If the Roomba had a malfunction and knocked over a valuable vase, you wouldn’t blame the vacuum as an independent entity – you’d recognize it as an extension of the owner’s agency (or perhaps hold the manufacturer responsible if it was a product defect).
Many business AI solutions today fall somewhere in between pure tools and true agents. Vendors often market “AI agents” that are essentially glorified chatbots or workflow tools requiring extensive human oversight. This loose use of the term “agent” has led to confusion in the market. It’s important for leaders to cut through the hype by asking: Is this AI actually making autonomous decisions and taking actions on our behalf, or is it just providing recommendations for a human to act on? If it’s the former, you need to manage it with the rigor you’d apply to a human delegate. If it’s the latter, traditional software risk management may suffice.
One helpful way to identify an agentic system is to look at its capabilities and design. Agentic AI systems typically incorporate additional components beyond what a standard tool would have. For instance, an AI agent might be provisioned with identities or credentials to authenticate into other systems (much like an employee badge), and it might have the ability to perform actions via plugins, APIs, or robotic controls without a human in the loop. Advanced agents can also orchestrate complex sequences of steps – planning and executing multiple actions to achieve an objective. They often maintain some form of memory or knowledge base about their environment and can even learn over time from experience.
The Importance of a Defined Domain
Central to the notion of an AI agent is the idea of a defined domain of authority. In law, when you appoint a human agent, you usually delimit what they are authorized to do. A junior buyer at a company might have authority to sign purchase orders up to a certain dollar amount. A real estate agent can negotiate home sales on your behalf, but can’t arbitrarily decide to invest your money elsewhere. This principle of scope is crucial: if an agent stays within scope, the principal is bound by the agent’s acts; if they step outside it, the principal may not be obligated (and the agent might even face personal liability).
For AI, a defined domain is just as essential – arguably even more so. A human agent has common sense and an understanding of their limits; an AI will try to do whatever it was programmed or trained to do, even if the situation changes or the task turns out to be ill-specified. To safely give an AI autonomy, we must clearly define the arena in which it can act and make decisions. In practice, this means setting boundaries on the AI’s functions and access: the data it can use, the decisions it is allowed to make, and the actions it can carry out. The AI’s domain could be defined in terms of business processes (e.g. handling refund requests up to $100, or managing calendar scheduling for meetings), in terms of knowledge scope (e.g. answering questions only about a specific product line), and in terms of accessible systems (e.g. it may interface with the CRM system but not the financial accounts).
Defining the domain isn’t just about preventing worst-case scenarios – it also builds trust and reliability. When an AI knows its job and doesn’t stray outside it, it’s less likely to produce nonsense or harmful actions. For example, Salesforce, which is developing enterprise AI agents, found that giving agents a well-defined set of “topics” or tasks is key to preventing errors. An AI customer service agent might be allowed to handle routine issues like order status inquiries, returns, and refunds, but anything outside those predefined topics (like an unusual legal complaint or a request it doesn’t understand) should trigger an escalation to a human representative (5 Ways To Build a Trustworthy AI Agent). By confining the AI to what it’s been trained and authorized to do, you “make sure the AI agent doesn’t try to answer a question it shouldn’t”, which greatly reduces the chance of it hallucinating an answer or taking an inappropriate action. In essence, domain limits act as guardrails that keep the agent trustworthy.
Moreover, a defined domain simplifies governance and accountability. If something goes wrong, it will likely be within the agent’s sphere of responsibility, making it easier to trace the cause and implement a fix. It also clarifies accountability: if your AI agent for automated billing suddenly starts sending collection notices in error, you know which system (and team) is responsible and what domain knowledge needs adjustment. Contrast this with a scenario where an AI is set loose without clear boundaries – mistakes could be harder to detect and diagnose, and accountability becomes murky. In regulating AI, we see emerging consensus on this point: even defense and ethics guidelines suggest AI systems should have an “explicit, well-defined domain of use” for safe operation. Whether for technical safety or legal clarity, an AI agent without a clear domain is a recipe for trouble. Responsible leaders will insist on scoping AI deployments tightly, at least until we have far more generalized and proven AI that can truly handle open-ended authority (a prospect that remains uncertain).
Trust, Governance, and Accountability
Handing off tasks to AI agents can feel like hiring a new employee or contracting out work – it requires trust. But trust in AI doesn’t come for free; it must be earned through good performance and transparent behavior, and maintained via oversight. Business leaders need to establish governance mechanisms to ensure AI agents stay reliable and aligned with the organization’s goals and values. Here are key considerations for building trust and ensuring proper governance:
Ultimate Accountability Stays with You: First and foremost, never forget that deploying an AI agent does not absolve your organization of responsibility. If an AI-driven HR screening system discriminates against candidates or an AI trading algorithm triggers losses, it’s the company that will be held accountable. Courts and regulators are already reinforcing this – for instance, a recent case involving an AI-powered hiring tool underscored that employers may be held liable for decisions made by their AI systems, which can be viewed as agents of the employer. In other words, legally and ethically, the AI is an extension of your business. This means you must audit and validate an AI agent’s decisions just as you would review an employee’s work during a probation period. Establish clear escalation paths: under what conditions must the AI defer to a human decision-maker? Set those rules early and enforce them.
Governance and Oversight: Treat AI agents as a new kind of stakeholder in your governance model. Many organizations are forming AI governance committees or designating AI oversight roles that involve cross-functional teams (IT, legal, compliance, risk, and business unit leaders). The goal is to continuously monitor AI behavior, outcomes, and risks. Policies should be drafted to cover AI agent conduct – for example, defining acceptable use, risk thresholds, and fallback procedures if the AI encounters a scenario outside its domain. Some leading companies have even created internal “Ethical AI” review boards that evaluate new AI use cases for potential harm or bias before deployment (5 Ways To Build a Trustworthy AI Agent). Regular audits should be conducted on AI outputs and decisions to ensure they remain within expected boundaries (analogous to financial audits or quality control for a process). If an AI agent interacts with customers, gathering feedback from those customers is vital – it provides insight into whether the agent is effective and trustworthy from the user’s perspective.
Transparency and Communication: One of the challenges with AI, especially sophisticated models like neural networks, is that their decision-making process can be opaque. This “black box” nature can erode trust. To counteract that, insist on transparency wherever possible. This could mean having the AI explain its reasoning in simple terms, or providing logs of actions taken. When something goes wrong, a post-mortem should be done just as you would for a human error, and the lessons should be communicated to stakeholders. Externally, if customers or partners are affected by an AI agent’s actions, be proactive in disclosing that an AI was involved (as long as it doesn’t confuse or alarm them needlessly) and what safeguards are in place. Transparency also extends to knowing the provenance of your AI – what data it was trained on, what rules or objectives it’s optimizing for. Being able to answer these questions builds confidence among executives, regulators, and customers that the AI is behaving consistently and as intended.
Aligning Incentives and Ethics: In classical principal-agent theory, a big issue is aligning the agent’s incentives with the principal’s goals (to prevent the agent from pursuing its own agenda). With AI, we don’t worry about greed or ambition, but we do worry about objective functions and reward signals. If your AI agent is optimizing for the wrong metric, it could inadvertently act against your broader interests (for example, an AI that aggressively maximizes short-term sales might start annoying customers or offering unsustainable discounts). Make sure the performance metrics and reward signals for your AI systems encourage the behavior you actually want. Similarly, encode your company’s ethics and compliance requirements into the AI’s operating procedures – if fairness, customer privacy, or safety are core values, the AI’s design should reflect that (e.g., by excluding protected attributes from decisions, or having conservative constraints on actions that could pose safety risks). Governing AI agents means baking your policies and values into their logic from day one.
Fail-safes and Contingency Plans: No matter how much you trust your AI agent and how well it’s governed, you need a Plan B. What if the AI encounters a novel situation and makes a bad call? What if it goes down due to a technical issue? Ensure there are fallback mechanisms. This could be as simple as “if the AI confidence is low or an error occurs, route the task to a human” in customer service, or a hard stop on trading algorithms in finance if unusual volatility is detected. Have clear intervention points where human oversight can pause or shut down the AI’s actions if needed. In other words, keep a metaphorical “off switch” handy for your AI agent processes and know the conditions for using it.
By addressing these areas, you create an environment where AI agents can be trusted participants in your operations. Your employees will feel more comfortable working with or relying on AI if they know there’s oversight and that leadership has set boundaries. Your customers will be more likely to embrace AI-driven services if they sense that you have control over the technology and will take responsibility for its outcomes. And regulators will certainly look more favorably on companies that can demonstrate a strong governance model for AI (as opposed to a hands-off “the algorithm did it, not us” approach, which is a red flag). In sum, treat an AI agent with at least as much diligence as you would a new hire – train it well, set expectations, monitor its work, and integrate it into your accountability structures. Only then can you reap the efficiency and innovation benefits of AI autonomy without losing grip on risk and ethics.
Building Trust through an AI “Bill of Materials”
So far we’ve discussed conceptual and organizational strategies for managing AI agents. Now let’s turn to a more technical but highly effective tool that forward-thinking organizations are adopting: the AI Bill of Materials (AI BOM). Borrowing an idea from software supply chain management, an AI BOM is essentially an exhaustive ingredients list for your AI agent. It is “a comprehensive inventory that lists all the components, data sets, and dependencies” that went into developing and deploying the AI system (Strengthening Cyber Security in US Defense: The AI Bill of Materials | by Valdez Ladd | Medium). This includes the machine learning model (or models) at its core, the training datasets it learned from, any third-party APIs or libraries it relies on, and even the hardware or cloud environment it runs on. In short, if your AI agent is a proverbial recipe, the BOM lists every ingredient and its provenance.
Why does this matter for trust and governance? Consider how much effort goes into quality control and supplier vetting in a physical product’s supply chain. If one part is substandard or faulty, the whole product (and the company’s reputation) is at risk. Similarly, an AI agent is only as reliable as its least reliable component. A tainted training dataset could introduce bias; an open-source library with a security vulnerability could expose your system to hacks; an unvetted plugin might perform unexpected actions. An AI BOM brings transparency and accountability to this complex pipeline (AI Bill of Materials (AI BOM). The AI BOM encompasses everything from… | by Bijit Ghosh | Medium). It allows your team (and potentially regulators or auditors) to audit what’s inside the AI, trace issues to their source, and verify that everything is up to your standards. In fact, maintaining such a detailed BOM “ensures that only approved components are used” in your AI systems. Just as a hospital pharmacy won’t dispense medicine that isn’t from a vetted source, your AI agent shouldn’t be allowed to operate with code or data that hasn’t passed your organization’s security, quality, and ethics criteria.
To be truly useful, an AI BOM should be automated and continuously updated. Modern AI systems are complex and can change over time (for example, models get updated or fine-tuned, new data is ingested, etc.). Manually documenting every component is error-prone and quickly becomes outdated. Instead, organizations are leveraging tools that automatically scan and catalog AI assets across their environments. Such tools can often integrate into MLOps pipelines or IT asset management, so that whenever an AI model is trained or a new dataset is added, the BOM updates. Automation also means the BOM can be used in real-time to monitor compliance – for instance, if someone tries to incorporate an unapproved data source, an alert can be raised before the AI agent goes live with it.
Equally important is that the AI BOM is authenticated – meaning it’s secure and trustworthy. This is where concepts like digital signatures come in. Leading AI security platforms now provide features to cryptographically sign the components and the BOM itself. In practice, this means each element (say, a model file or a data corpus) can have a hash or signature that proves its integrity and origin. The BOM acts like a “certificate of authenticity” for your AI agent. Why go to this length? Because if you’re going to let an AI agent act autonomously in your business, you want high assurance that it hasn’t been tampered with or corrupted. For example, imagine a scenario where an attacker tries to slip a malicious piece of code into an AI agent’s library of plugins. A rigorously maintained, signed BOM would detect that an unknown component is present and flag it, much like a security system catching an unverified device on a network. This allows you to enforce access control and policies based on trustworthiness: your systems could be set up to only allow AI agents that present a valid, clean BOM to integrate with sensitive databases or execute certain transactions. In other words, the BOM becomes a gatekeeper – if the AI doesn’t have its papers (credentials) in order, it doesn’t get the keys to the kingdom.
The concept of AI BOM is gaining traction not just for internal risk management but also as a response to emerging regulations. Governments are increasingly concerned about AI safety and security, with proposals that developers document their AI systems’ ingredients, much like financial disclosures. By getting ahead of the curve and implementing AI BOMs now, companies can better meet future compliance requirements and reassure stakeholders (investors, customers, regulators) that they maintain “secure and responsible” AI practices (AI-BOM: Building an AI-Bill of Materials | Wiz). It’s a proactive investment in trust.
For business leaders, the takeaway is: ask your technology teams about an AI BOM whenever you deploy a significant AI agent. It might sound technical, but it boils down to a simple business question: “Do we know what’s inside our AI and that we can trust every part of it?” If you can’t confidently answer that today, make implementing an AI BOM process a priority. It will enforce discipline in how AI models and tools are sourced and used. And in the event of an incident, it provides a solid foundation for forensic analysis and remediation. In the long run, an AI BOM is as foundational to AI governance as financial auditing is to corporate governance – an enabler of trust, both internally and externally.
Actionable Steps for Business Leaders
Understanding these concepts is one thing; implementing them in your organization is another. Here are some concrete steps to put these ideas into practice and ensure your company is ready to leverage AI agents responsibly:
Inventory Your AI Use Cases: Start by listing where you are (or soon will be) using AI in a potentially autonomous capacity. Identify which systems are mere tools (requiring human sign-off for actions) and which are approaching agent status (making decisions or acting on their own). This audit will highlight where stronger governance is needed. For each AI system, document its principal (the business owner or accountable person/team) and what domain it operates in (tasks, decisions, and limits).
Define Domains and Delegations: For each AI agent use case, explicitly define its scope and authority. Write it down like a job description: what it can do independently, what it cannot do, and when it should defer to a human. Ensure this is reflected in the AI’s technical configuration (through rules or constraints) and communicated to all stakeholders. Just as you wouldn’t hire an employee without a role description, don’t deploy an AI without clear boundaries.
Establish AI Governance Bodies: If you haven’t already, form a governance structure for AI oversight. This could be a dedicated committee or a working group that meets regularly (including stakeholders from IT, data science, legal, risk, and business units). Charge them with creating an AI governance policy that covers areas like testing and validation requirements, monitoring protocols, ethical guidelines, and incident response plans for AI. Have them review any proposal for a new AI agent deployment before it goes live, to ensure it meets your organization’s standards of safety and ethics.
Implement Monitoring and Oversight: Ensure every AI agent in operation has an owner – a human “manager” responsible for its performance and compliance. Set up dashboards or reports for key metrics (accuracy, error rates, decision turnaround times, etc.) and review them as you would review an employee’s KPIs. Establish logs for AI decisions and actions (especially for high-stakes use cases) so that there is an audit trail. Consider running periodic “red team” exercises where someone intentionally stress-tests the AI with novel scenarios to see how it behaves; use those findings to improve the system.
Develop an AI Bill of Materials: Work with your CIO/CTO or data science leaders to institute an AI BOM for major AI systems. This might involve deploying new tools or integrating with your MLOps pipeline to automatically track components. Begin by focusing on your most critical AI agent (say, the one interfacing with customers or financial data) and build a complete BOM for it. Verify that all components are approved and originate from trusted sources. Going forward, make the BOM a requirement in your AI project lifecycle – much like code reviews or QA testing. This investment will pay off by reducing security and compliance risks (What Is Generative AI Security? [Explanation/Starter Guide] - Palo Alto Networks) (AI-BOM: Building an AI-Bill of Materials | Wiz).
Enforce Access Controls Tied to Trust: Collaborate with your cybersecurity team to link the AI BOM and governance policies to your access control systems. For example, you can set rules so that an AI system without a vetted BOM or with outdated/unapproved components is sandboxed or prevented from connecting to live production databases. Leverage techniques like role-based access control (RBAC) for AI just as you do for humans (AI Bill of Materials (AI BOM). The AI BOM encompasses everything from… | by Bijit Ghosh | Medium). Essentially, promote your AI agent to higher levels of access only as it proves itself trustworthy (and as its BOM checks out).
Educate and Communicate: Finally, bring your people along. Train your staff (especially managers of functions where AI is deployed) about what it means to work with AI agents. They should understand the AI’s domain, its limitations, and how to oversee it. Encourage an open dialogue – if employees spot the AI doing something odd, they should feel responsible for reporting it, not assuming “the tech team must know.” Also, communicate to customers or external partners when an AI agent is part of the process and what safeguards you have. This can be as simple as a note on your website or in a service interaction that says, “This response was generated by our AI system under human supervision” – it sets expectations and shows you’re not hiding the use of AI.
By following these steps, you turn abstract principles into tangible practices. Not every organization will get everything perfect at once, but even partial progress (like instituting an oversight committee or drafting initial AI use policies) significantly lowers the odds of an AI-related mishap. Moreover, these actions signal to your whole organization that AI is a strategic asset that will be managed with care, not a magic box to be adopted recklessly.
Conclusion
The rise of AI agents represents a new chapter in how work gets done – one filled with opportunity, from automating drudge tasks to scaling expertise, but also with new forms of risk. Business leaders cannot afford to approach this evolution with either blind fear or blind optimism. The key is to bring clarity and structure to the role of AI in your organization. Thinking of advanced AI systems “through the lens of legal agency” provides that clarity. It reminds us that an AI agent, much like a human agent, must be chosen carefully, given a clear mandate, and supervised appropriately. It also reminds us that ultimately responsibility lies with the principal – the organizations and leaders deploying these agents. By internalizing that, leaders can avoid the trap of attributing failures to a supposedly inscrutable algorithm. Instead, they will proactively shape the AI’s behavior through domain definitions, oversight, and technical safeguards.
Trust in AI is often cited as a barrier to adoption. Trust doesn’t mean assuming AI will never fail; it means having confidence that it will perform well and that if something goes off course, you will catch it and correct it. The approaches discussed – from governance committees to AI BOMs – are all about creating the conditions for justified trust. When your AI systems are transparent, well-audited, and aligned with your intent, you and your stakeholders (whether customers, employees, or regulators) can trust them enough to integrate them deeply into operations. And when that trust is well-placed, it unlocks the full potential of AI agents to drive efficiency, innovation, and value creation.
For forward-looking companies, investing in these governance and trust mechanisms will be a competitive differentiator. As AI agents become more common, those who have mastered managing them will be able to scale up their use confidently, whereas others might hold back or suffer public failures. In essence, treating AI agents “not just as tools” but as accountable, well-governed extensions of your organization could be the difference between leading the AI-enabled economy or lagging behind it. Business history has shown that every transformative technology rewards the prepared and punishes the complacent. With AI agents, preparation means pairing technological prowess with sound management principles. By doing so, you ensure that when your AI agents join the workforce – as Sam Altman and others predict they will – they will be productive, reliable colleagues rather than loose cannons. The future of work will likely include humans and AI agents working side by side; it’s up to today’s leaders to lay the groundwork for a partnership built on trust, clarity, and accountability.