Principals, Agents, and the Chain of Accountability in AI Systems
From a systems engineering perspective, AI agents are autonomous systems designed to operate within defined constraints. These systems are not self-governing; they are provisioned, deployed, and monitored by individuals or organizations who remain ultimately accountable for their behavior.
In this episode of The AI Fundamentalist podcast, Dr. Michael Zargham clarifies the often misunderstood concept of AI agents by grounding them in established engineering principles. Emphasizing human responsibility, accountability, and the Principal-Agent relationship, he provides keys to understanding AI agents, the role of large language models (LLMs), and agentic architectures from a systems engineering perspective. This grounding in engineering practice helps strip away some of the mystique surrounding AI agents, positioning them as engineered systems acting on behalf of human stakeholders.
Zargham is the founder and Chief Engineer at BlockScience, a systems engineering firm focused on digital public infrastructure. He is a Board Member and Research Director at US-based non-profit Metagov – a community of practice focused on digitally mediated self-governance. He is also an advisory council member at NumFocus. He received his PhD from the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania. The AI Fundamentalists is a podcast about the fundamentals of safe and resilient modeling systems behind the AI that impacts our lives and our businesses.
This blog post was produced with the assistance of AI, as part of a broader inquiry into the roles that such systems can play in synthesis and sense-making when they are wielded as tools, rather than as oracles. Based predominantly on the AI Fundamentalist Podcast Episode 31, titled 'Principles, Agents, and the Chain of Accountability in AI Systems' with Andrew Clark, Sid Mangalik, and Zargham, their discussion is reformatted below with the creator's permission and the assistance of AI and humans-in-the-loop.
Podcast Audio
Topics of Discussion
- The Principal-Agent Relationship: The Accountable Stakeholder | The Role of the Principal | Agents, Co-Pilots & Autonomy | Modeling Agents
- The Role of Large Language Models (LLMs) in Autonomous Systems: Components of Agentic Systems | LLM Role in Agentic Systems | LLM-Powered Agents | Humans-in-the-Loop
- Engineering for Safety & Reliability: The Role of Deontics | Guardrails & Constraints | Admissible Actions | System State Models | Policy Engines | Dynamic Behaviour
- Representation vs. Reality | The Epistemic Gap | Abstraction & Meta-Level Analysis | Tools, Not Decision Makers | Autonomous, Not Autopoietic | Designing with Uncertainty in Mind | Shannon's Information Theory
- Why Getting the Metrics Right Isn’t Enough: The Overfitting Trap | The Problem of Validation | Control Theory & Engineering | Fit-for-Purpose Systems
1. The Principal-Agent Relationship
The Accountable Stakeholder
The term agent is a structural definition denoting a principal-agent relationship, wherein the agent acts on behalf of the principal. Agent implies the existence of a principal, so when we refer to something as an agent, we should also ask: Who chose to deploy it, and what expectations were established? Who is the principal? Because every autonomous system is deployed by someone - whether a government agency, a commercial entity, or a lead engineer - there is always an accountable stakeholder.
That accountable stakeholder defines what the system is supposed to do, under what conditions, and with what level of confidence in its performance. Of course, systems rarely behave perfectly, so within systems engineering, we define tolerances—operational boundaries within which we are willing to accept imperfect behavior. If the system stays within those bounds, it is considered safe and reliable for deployment.
The Role of the Principal
To better understand agency in AI systems, we must consider the role of the principal—the party who defines the mission, provides resources, sets constraints, and takes responsibility for the outcome. Real-world examples can help ground this.
As an undergraduate, Zargham had the opportunity to work on a senior design project in collaboration with the U.S. Army Corps of Engineers Cold Regions Research Engineering Laboratory (CRREL). The project involved building an autonomous Arctic rover—an unmanned system equipped with ground-penetrating radar to map ice flows and assess terrain safety for researchers operating in the field. At a technical level, the focus was on designing the navigation system to ensure the robot could perform tight grid patterns and gather clean data, even in conditions with poor GPS coverage, wind interference, and rough terrain.
But zooming out, what made this robot an agent was not just the autonomy in navigation—it was the broader context of its deployment. The rover was not just a technical system but an operational asset deployed by an institution. In this case, the Army Corps of Engineers is the principal. They defined the mission, provided the resources, and oversaw its development, testing, and validation—from lab simulations to field trials in Greenland to being deployed at McMurdo Station in Antarctica.
Importantly, there were people at every level of accountability: engineers leading the project, technicians managing field operations, and operators monitoring the system in real time. Even though the rover was acting autonomously, it was not ungoverned. There were humans-in-the-loop — not necessarily in control at every moment, but responsible for oversight, calibration, and intervention when needed.
Agents, Copilots, and the Spectrum of Autonomy
Understanding delegated control also helps distinguish between different kinds of AI systems. When we talk about "copilots"—a term often used in enterprise AI—we are really referring to decision support systems. These tools assist a human operator, surfacing relevant information or suggesting actions, but always leave the final decision in human hands.
Agents, by contrast, act on their own. The principal defines the mission, sets the boundaries, and then steps back. An agent's autonomy lies in its ability to act independently within the environment, based on the provisioning it received. A helpful analogy is that of a boatswain’s mate in the U.S. Coast Guard, operating as a tactical small-boat driver, given a team, a vessel, and a mission, with the authority and accountability to act on behalf of the Coast Guard. Effectively, an agent of the institution. It is important not to get caught up in whether the agent is a person, a machine, or some human-machine hybrid – as is the case in the Coast Guard example.
- Who defines its goals?
- Who gives it its tools?
- Who is responsible for its behavior?
The answers to these questions define the principal-agent relationship, regardless of the form the agent takes.
Modeling Agents: From Systems to Simulations
In agent-based modeling (ABM), agents are representations of actors in a simulated environment. These agents operate according to rules or heuristics, which may include optimization objectives or learned behaviors. They are not deployed by a principal in the same way a real-world robot or AI system is, but they often represent the perspective or intent of a stakeholder.
Think of an agent in a simulation as a kind of proxy: a small, self-contained unit of decision-making designed to model how an actor might behave under certain conditions. These agents have a state, can perceive their environment, and can take actions; however, the logic of the simulation itself limits their operating environment to a sandbox. In this framing, agent-based models are tools for studying the emergent behavior of systems. A principal may define and provision the agents within it, but agent-based models are tools for study rather than tools which directly support or enact decisions in an operating environment.
2. Understanding the Role of Large Language Models (LLMs) in Autonomous Systems
Components of Agentic Systems
As AI capabilities in the realm of LLMs evolve, there is growing confusion around what it means for a system to be “agentic.” One of the most common misunderstandings is treating an LLM as though it is an agent in and of itself. However, in practice, it is more accurate to think of an LLM as a component within an agentic system, rather than as the agent itself.
To unpack this, we first need to revisit what LLMs actually are. In essence, an LLM is a probabilistic generative model trained to predict the next token in a sequence of text. Functionally, it behaves like a high-dimensional word calculator. The model does not understand goals, contexts, or consequences—it simply infers what text is likely to follow based on statistical patterns in massive training corpora.
From this perspective, it does not make much sense to call a raw LLM an agent. It does not perceive an environment, hold persistent goals, or take action in the world. It has no embedded notion of success, failure, constraints, or feedback loops. It is not, in itself, autonomous.
The Role of LLMs in Agentic Systems
Instead, LLMs are tools that can be invoked to generate plausible text, which can be extremely useful in the right context, meaning LLMs can play an important role in agentic systems. A helpful analogy here is that of a graduate research assistant.
Personally, I use a control systems focused LLM Control Systems Master GPT - precisely because it can speak my language. It understands the jargon of control theory. I can pose open-ended questions about constrained optimization, or ask for feedback on a systems-theoretic framing, and it gives me something coherent and helpful in return.
Do I trust it implicitly? Absolutely not.
But because I have the domain expertise, I can validate whether its response is relevant or misleading. I treat it as a junior collaborator: it is fast, it has a lot of useful context baked in, and when given the right prompt, it helps me explore directions I might not have considered or had not yet articulated. This is where LLMs shine—not as fully independent agents, but as amplifiers of human reasoning within structured tasks.
Zargham
That said, this kind of interaction still does not elevate the LLM to the status of an autonomous agent. It is a responsive tool, not a mission-driven actor. It does not hold state or long-term goals, and it does not take responsibility for outcomes. So when people speak of “LLM agents,” they are usually referring to something more complex than the model itself.
LLM-Powered Agents
So, how do we go from “LLM as tool” to “LLM-powered agent”? The distinction lies in composition. When an LLM is embedded in a larger system—one that includes memory, feedback, control logic, state tracking, and execution policies—it becomes part of something that can legitimately be called an intelligent agent. For instance, many modern agentic systems wrap LLMs in scaffolding architectures that include:
- State Management: Persistent memory to retain goals, prior actions, and results.
- Sensors & Actuators: Interfaces to receive input - from the web, application programming interfaces (APIs), files, etc. - and perform actions, such as making API calls, writing code, or sending emails.
- Planning Loops: Routines that define goals, decompose them into subtasks, and decide what to do next.
- Validation Layers: Policy Engines and associated guardrails that assert that the outputs are sensible, safe, or conform to a required schema..
In these setups, the LLM often serves as a language-based reasoning engine. This flexible, expressive component translates abstract prompts into structured output or interprets noisy input into actionable instructions. This anthropomorphic interaction can feel like human agency, but it is important to realize that what is happening is an automated epistemic process. The agent is not “the LLM.” The agent is the orchestrated system, and the LLM is a crucial—but ultimately subordinate—component. This becomes even clearer when we think about how these systems do (or do not) recover from failure or respond to unexpected inputs.
Humans-in-the-Loop
One of the most powerful features of LLMs is their ability to act as a natural interface for humans. In legacy automation systems, interfacing with an agent meant writing structured logic, commands, or configurations. Now, you can speak in natural language. Ask a question. Give an instruction. Correct.
This new interaction mode lowers the entry barrier for working with complex systems. For example, rather than crafting a carefully formatted API call, you can type “find all users who signed up in the last week and send them a welcome message.” A system like Toolformer or LangChain might parse that intent, call the necessary APIs, and present you with structured results—all while using the LLM to handle the linguistic translation.
However, this still does not mean the LLM is deciding what to do or why it is doing it. It is a natural language reasoning layer that translates between human expressions and machine-readable actions. And when things go wrong—as they inevitably do—the surrounding system (and its human overseers) are responsible for error recovery, not the LLM itself. No matter how many layers of automation are added, there is always a limit to what the system can handle – this limitation is true of all systems – it appears in legal systems as the principle of incompleteness of contracts. A principal (whether individual or institution) is the party responsible for monitoring the limits of and governing any systems acting as its agents.
3. Engineering for Safety & Reliability
The Role of Deontics
Deontics deals with formalizing rules governing what a system should or must do. The concept of constitutional AI involves using long prompts to specify a system’s goals and boundaries. These prompts represent "should" directives, meaning that the AI is expected to follow these guidelines, but they are not absolute constraints. Even the word "must" in a prompt does not guarantee compliance, since it remains a guideline embedded in the system's reasoning rather than a rigid rule.
Constraints & Guardrails
To ensure stronger alignment with specific goals, one can implement constraints in the form of guardrails. These constraints are much more rigid than prompts, preventing the system from responding if the output violates predefined rules. However, the flexibility of language models, combined with attempts to circumvent these constraints (through techniques like "jailbreaking"), introduces vulnerabilities. Due to the expressiveness of natural language, even well-established guardrails can be bypassed through the clever manipulation of input prompts, leading to results that appear permissible from a system's perspective but fail to meet human expectations
Admissible Actions
To make AI systems safe and responsible, we utilize guardrails and constraints—rules that limit the AI's capabilities. In systems engineering, these are clear and predictable boundaries that ensure the AI produces acceptable outputs for any given input. Think of it like this: based on the current situation, the AI has a list of actions it is allowed to take—these are called "admissible actions." Rather than define admissible actions solely in terms of inputs and outputs, we consider context (or state) dependent rules.
System State Models
To determine what actions are allowed, we need a reliable model of the AI system's current state–dynamic models. These models help us determine what is acceptable and what is not, depending on the conditions. These techniques are based on control theory in engineering. Just as we use control methods to keep machines or systems running safely and predictably, we can employ similar techniques to keep AI systems within safe limits and under control.
Policy Engines
A key distinction is that, unlike traditional systems, AI systems often combine deterministic constraints with non-deterministic components, such as LLMs. These models add complexity to the system, as they can generate outputs that are not entirely predictable. The integration of policy engines—tools that enforce a set of predefined rules or protocols—can enhance control over AI systems. These engines can operate alongside LLMs and knowledge bases, guiding the system’s behavior in a more structured and predictable way.
Dynamical Systems Behaviour
As the AI system interacts with its environment, it can be seen as a closed-loop dynamical system. This means that rather than simply responding to inputs and providing outputs, the system actively evaluates its actions, iterates on its strategies, and adapts until it achieves a desired outcome.
This process of iteration is similar to what engineers do when solving complex problems. They break down poorly defined challenges into smaller, more manageable tasks, develop solutions for each, and integrate them back into the larger system to achieve the desired end state. By applying this same mindset to AI systems, engineers can create systems that evolve and adapt dynamically, while remaining within predefined constraints.
In control systems engineering, “stability” has a precise technical meaning-
ing: it refers to the system’s ability to maintain consistent and predictable
behavior in the face of disturbances. Importantly, stability does not neces-
sarily imply returning to a fixed, unchanging state; rather, the equilibrium
of a system can itself be a dynamic behavior — such as a pattern of oscilla-
tion or a stimulus-response pair — as long as the defining properties of that
behavior remain stable over time.
Zargham, M., & Ben-Meir, I. (2025)
Dynamic stability is achieved when a closed-loop system reliably performs a desired function, exhibiting stability in the sense of Lyapunov (a mathematical framework used to model, analyze, and predict the behavior of systems that change over time or space). Such closed-loop "control systems" may be viewed as application-specific AI agents.
4. Representation vs. Reality in AI Systems
The Epistemic Gap
In engineering, a persistent challenge is the epistemic gap—the unavoidable difference between our models of the world and the world itself. This gap is present in how we theorize systems, measure their behavior, and interpret those measurements in practice. In the realm of AI, one of the fundamental challenges is ensuring goal alignment between a system’s designed behavior and its real-world impact, which can arise from the inherent difficulty of specifying goals or from the failure to account for real-world complexities.
Despite these challenges, the epistemic gap between the representation (the AI model) and the represented (the real-world objectives) is a persistent and unavoidable issue. We must acknowledge the limits of AI systems and create appropriate guardrails and constraints to ensure that they behave in predictable and safe ways. This requires ongoing oversight and validation, not only of the AI's performance but also of its alignment with human goals and real-world impacts. Ultimately, the goal is not to eliminate the epistemic gap between representation and reality but to respect and characterize it.
Abstraction & Meta-Level Analysis
Too often, we get lost in meta-level analysis—the abstraction of data and models—while losing sight of the fundamental relationship between the representations we create and the real-world phenomena they aim to model. This is particularly true when working with AI systems, such as LLMs, which may seem sophisticated but can obscure the distinction between abstract representations and reality.
In robotics, the interaction between sensors and actuators provides a direct, tangible link between the system and the physical world. Sensors collect environmental data, while actuators perform actions that influence the world. This interface is clearly defined and, in theory, measurable. However, when we move to areas like economics or compliance domains where LLMs and other AI tools are commonly applied, the question of "what is the world?" becomes less tangible, and therefore, the epistemic gap becomes less legible.
Tools, Not Decision Makers
There is a common misconception in business and AI circles that LLMs are capable of making decisions independently. However, this overlooks a key limitation: LLMs are, at best, components within broader autonomous systems designed to handle mismatches or inconsistencies in data. While LLMs excel at processing language and transforming it into structured outputs, such as converting natural language into a format that matches an API specification, they are not autonomous decision makers. Rather, they serve as a tool to "fit" disparate information into usable formats, akin to using duct tape or WD-40 to smooth out issues. Natural language serves as a kind of “least common protocol” allowing systems to interoperate which otherwise would not be able to. LLMs allow AI systems to speak this language, but they don’t guarantee you will agree with what they have to say.
Autonomous, Not Autopoietic
In deploying AI systems, accountability must align with authority.
A recurring theme in the narratives around AI is that we are treating them not as autonomous systems, but rather as autopoietic systems, self-creating systems that emerge from and maintain themselves. And that is not what they are. I don't think that you can remove the accountability from the actor responsible for provisioning the agent into an operating environment. And so, I think military-based models of accountability make sense... if something extremely problematic occurs, then you can look for where in that chain of command the behavior deviated from what is morally acceptable.
Zargham
The individual or role responsible for making deployment decisions must also be accountable for the system’s performance. This often falls to someone like a project manager or director of data science—roles that may not execute all technical tasks themselves but are responsible for making final decisions about system readiness, rollback, or parallel deployment strategies. Accountability within teams operates through clear lines of responsibility. Each team member must be empowered to work effectively, while leadership maintains oversight and final decision-making authority. This structure supports robust collaboration and ensures that ownership of outcomes is attributed appropriately.
Designing with Uncertainty in Mind
One of the central challenges in deploying AI systems is grappling with uncertainty—both in how the systems behave and in the materials and interfaces that comprise them. In classical engineering disciplines, this challenge is often more manageable: we can analyze the structural properties of concrete, steel, or carbon fiber with well-understood physical laws and empirical data. The materials are tangible, their failure modes are mapped, and safety margins can be precisely calculated.
AI, particularly in the realm of machine learning, presents a more elusive picture. The “materials” here—software packages, data formats, model architectures, optimization heuristics, evaluation metrics, feedback loops—are all abstract, high-dimensional, and the system behavior is non-deterministic. Since their behavior changes across contexts and is shaped by interactions, they may feel opaque or unpredictable. As a result, achieving reliability in AI systems does not come from eliminating uncertainty, but rather from designing explicitly with it in mind.
Shannon's Information Theory
This is where Shannon’s Information Theory provides a powerful and instructive analogy. In the 1940s, Claude Shannon revolutionized our understanding of communication systems by showing that reliable communication is not dependent on perfect channels. Instead, it is possible to build robust systems out of noisy, unreliable components, as long as the noise is understood and accounted for through architecture and design, most notably through redundant encoding and error correction.
In essence, Shannon proved that imperfection need not be a barrier to reliability—if you know how to engineer around it. This principle translates elegantly to AI systems: rather than trying to eliminate uncertainty entirely, we must anticipate, constrain, and design for it. Redundancy, layered safeguards, fallback policies, and robust monitoring are the equivalent of Shannon's encoding schemes—they allow us to construct systems that behave reliably, even when the underlying components are probabilistic or partially understood.
Therefore, understanding the behavior of complex AI systems—especially when deployed in dynamic real-world environments—requires a rigorous, iterative, and failure-tolerant approach. Just as communication systems are tested under varying signal conditions, AI systems must undergo repeated testing across different operational scenarios, including stress-testing for rare but critical failure modes. Robust systems are those whose behavior is predictable under a wide range of uncertain conditions. For contrast, adaptive systems, those which learn, by definition, may exhibit behaviors which were not predictable a priori–before the aforementioned learning could take place. These insights do not emerge from a single test cycle, but from a process of continuous exploration, feedback, and adaptation.
In short, designing AI systems with uncertainty in mind means:
- Accepting that with adaptive systems, unpredictability may be a feature, not just a flaw,
- Borrowing from engineering strategies like redundancy and error correction, and
- Embracing validation in context, rather than seeking false comfort in abstract metrics.
By drawing from Shannon’s foundational ideas, we are reminded that the path to robustness does not lie in eliminating imperfection, but in mastering it.
5. Why Getting the Metrics Right is not Enough
The Overfitting Trap
In business, we are often tempted to overtrust quantitative indicators without deeply questioning whether these metrics accurately our reflect real world objectives. This introduces a common friction with stakeholders who may be uncomfortable with metrics trade-offs, especially when “performance” appears to drop. Yet, such drops are often a sign of the model becoming more context-aware, less brittle, and more aligned with the actual mission.
We see this with digital business systems where people are either unwilling or struggle with techniques that reduce the quality of validation performance, because you're introducing constraints, and based on first principles of optimization, if you constrain the system, it is going to do worse. The act of constraining your AI to avoid a particular problem that you've identified will make it do worse on its scores and validation.
And people have such a hard time doing that because they're like, no, I need my loss function number to go down. And you're like, it's the proxy, it's the representation..we have become so good at basically metaparameter optimization and hacking our systems to make our loss functions go down, that we sometimes forget that was not, actually, the point.
Zargham
The Problem of 'Validation'
In machine learning workflows, the term validation is typically used to refer to statistical validation—e.g., splitting data for cross-validation, optimizing loss functions, or improving hold-out accuracy. The concept of validation is often treated as the gold standard of model performance - practitioners routinely train models, minimize loss functions, boost accuracy scores, and rely on validation datasets to evaluate progress.
However, these forms of 'validation' are more precisely a form of verification: checking whether the system performs well against internal objectives or training specifications. The problem? These metrics provide a sense of confidence—until they do not. Because in practice, a system can look excellent on paper while performing poorly in the real world. These internal metrics can obscure a deeper, more important question: Is the model achieving its intended purpose in the real world? This is the realm of validation, in the sense that the system is effective at performing its function within a larger system or operating context, and it is too often neglected.
Consider a robotics student programming a robot. The terminal displays encouraging signs: the loss function is decreasing, the utility function is increasing, and plots show consistent improvement. According to the metrics, the system is performing well. Then, the student observes the robot repeatedly crashing into walls. This striking disconnect between digital indicators of success and physical outcomes of failure is not rare. It is a perfect metaphor for many AI deployments: models that perform well on validation metrics yet fail to deliver value or act in alignment with business goals, when viewed from the outside. In our framework, it is the Principal who is responsible for identifying the failure of the agent to perform, especially when the agent believes it is performing.
Control Systems & Systems Engineering
In control systems engineering, validation has a very specific meaning. It refers to demonstrating that the system meets its operational goals under real-world conditions. It is not enough for a system to optimize a metric—it must behave as intended in practice. In our robot example:
- The robot is correctly optimizing its internal utility function. Assuming we can prove it is correctly executing the procedure provided, it has passed verification.
- But it keeps crashing into walls. That means it has failed validation. We need to rethink what optimization procedure it should be using.
Business AI systems often show excellent verification results but fail validation:
- A churn prediction model achieves high accuracy using features only observable after the customer has already exited, or suggests interventions along factors outside the organization's control, making it non-actionable in practice.
- A pricing algorithm maximizes revenue in simulation (verified), but aggressively undercuts competitors so frequently that it triggers a price war, ultimately reducing profits. The resulting closed-loop behavior reduces rather than increases revenue.
In each case, the model may be verified, but not validated.
Focuses on implementation—whether the model or algorithm meets its design specs (e.g., correct loss function, convergence, statistical robustness). With formal verification tools (such as TLA+), we can assert that an implementation complies with a specification.
Validation: Did we build the right system?
Focuses on outcome—whether the system, when deployed, accomplishes the real-world goals it was designed for (e.g., ethical decision-making, customer satisfaction, regulatory alignment).
If protocols are mechanisms that control the flow of information (or that
structure the substrate by way of which information, matter, energy, and
thus also action flow), then the field of control systems engineering can
be understood as a rigorous and formal study of the interplay between the
physical world, specified protocols and realized behavior of the assemblage...
Design and analysis of control systems is accomplished by representing these assemblages via dynamical systems. In the context of multi-agent systems, control engineering explores how locally-defined and locally-implemented rules interact within dynamic environments to generate emergent behaviors that are both stable and predictable at the system level.
- Zargham, M., & Ben-Meir, I. (2025)
Fit-for-Purpose Systems
A more holistic engineering mindset asks whether a system is fit for purpose—whether it performs adequately in its intended environment for its intended users. This mindset emphasizes:
- Context-specific testing and ongoing monitoring
- Explicit requirements with evaluable metrics
- Oversight to maintain performance alignment with real-world goals
- Recognition of modeling and measurement limits, including relevant fail-safes
By integrating these principles, engineers and modelers can move beyond naive metric optimization toward the construction of reliable, usable, and trustworthy systems. Systems that an accountable stakeholder, whether individual or institution, can have confidence in.
Conclusion
To build trustworthy AI systems, especially in business applications, we must look beyond the narrow focus on model performance metrics and reintroduce principles from systems engineering. These principles emphasize not just whether we have implemented systems correctly (verification), but whether those systems serve their intended purpose in the real world (validation).
At the center of this approach lies the Principal-Agent relationship: AI systems may operate autonomously, but humans remain fully accountable for their behavior. Those who design, deploy, and oversee AI systems are responsible for ensuring that the system's actions align with ethical, operational, and strategic goals.
To ensure reliable and responsible operation, we must define and implement operating limits—through constraints, state models, and policy engines—that bound the system’s behavior within safe and predictable limits. These are not just good design choices but engineering necessities, especially in complex and dynamic environments.
In the lab, failure is expected and essential to learning; however, the standard shifts once an AI system is intended for production. The system must undergo rigorous testing—not only against benchmarks and validation metrics but also against real-world outcomes. Deployment should occur after the system has been proven fit for its intended environment. This requires more than just better models. It demands:
- Clearer objectives grounded in operational reality
- Explicit constraints that reflect ethical and contextual nuances
- Continuous validation strategies that reflect the complexity of operating environments
Building successful AI systems is not about perfection in abstract benchmarks but about robustness, adaptability, and alignment with real-world needs. This mindset mirrors established engineering practices, where systems are validated under operating conditions, redundancies are built in, and uncertainty is accounted for by design. If we treat AI development with the same care as we treat civil infrastructure, where failure has real consequences, we can create systems that are not only technically sound but socially and operationally responsible. Because in the end, no one wants a robot that keeps crashing into walls.
Acknowledgments
This AI-aided work is part of a broader inquiry into the role AI can play in synthesis and sensemaking when wielded as a tool rather than as an oracle. It is based predominantly on the AI Fundamentalist Podcast Episode May 08, 2025, titled 'Principles, agents, and the chain of accountability in AI systems' with Andrew Clark, Sid Mangalik and Zargham, and with their permission repurposed here with the assistance of Descript, ChatGPT and Openart.ai and humans-in-the-loop for technical and editorial review, and publishing. Thank you, Zargham, Jessica Zartler, and lee0007.
About BlockScience
BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating ethnography, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients, including for-profit, non-profit, academic, and government organizations.
