Chloe Maraina is a specialist who bridges the gap between complex data science and the practical realities of business intelligence. With a focus on the evolving landscape of automated decision-making, she advocates for a shift from simply tracking AI assets to governing their active behaviors. In this conversation, we explore why traditional registries are failing the modern enterprise and how the implementation of Behavior Catalogs is essential for maintaining control as AI moves from a tool we use to an agent we delegate to.
The discussion centers on the operational risks of autonomous workflows, specifically within customer service ecosystems. We delve into the necessity of defining “spheres of influence” for agents, the importance of “data fitness” over simple access, and the critical need for end-to-end traceability when automated decisions go wrong. By moving beyond technical metadata, organizations can ensure that their AI agents remain ethically aligned and operationally sound.
Standard registries track versioning and ownership, but how does this fall short when an agent is actually executing tasks? What specific operational gaps emerge when we know where an agent is deployed but lack visibility into how it makes its decisions?
Traditional registries were designed for a static world where we just needed to know who owned a model and where it lived. However, knowing an agent exists doesn’t explain what it is actually doing in a live environment, which creates a massive gap between visibility and true control. For instance, a registry can confirm that a Receiver Agent and a Resolution Agent are active, but it cannot tell you the logic behind how that Receiver Agent is classifying a specific complaint. Without this visibility, the “behavior” of the agent becomes a hidden risk surface that can lead to outcomes that are technically functional but operationally disastrous. We see gaps in accountability and alignment because we lack the context of the agent’s operating boundaries and the specific rules governing its decision-making process.
In complex workflows like customer complaint resolution, decisions are often distributed across multiple agents. How do you define the “sphere of influence” for each individual agent, and what steps are necessary to prevent these roles from blurring into conflicting actions?
Defining the “sphere of influence” means anchoring every agent within a strict functional boundary so it doesn’t overstep its mandate. In a resolution workflow, the Receiver Agent is strictly limited to intake and sentiment analysis, while the Resolution Agent handles the final outcome; crucially, the Resolution Agent must be prevented from arbitrarily reclassifying the complaint. To keep these roles from blurring, we must explicitly document what an agent is not allowed to do, such as a Receiver Agent attempting to resolve a case or send a final response. This structural clarity ensures that agents don’t create “silent drift,” where conflicting decisions are made because two different agents think they are responsible for the same part of the process. Mapping these dependencies in a Behavior Catalog allows us to see exactly where one agent’s authority ends and the next one’s begins.
Automating responses based on sentiment is efficient, but what happens when customer dissatisfaction turns highly negative? What metrics should trigger an “exception pathway,” and how can organizations ensure human intervention is both timely and context-rich rather than just a reactive stopgap?
When sentiment drops to a highly negative level, it should act as an immediate “exception trigger” that halts the automated loop to prevent the agent from overpromising or worsening the situation. The Response Agent shouldn’t just send another generic apology; it needs to trigger an escalation pathway that hands off the entire history to a human representative. This hand-off must include the full context—the original complaint text, the sentiment scores, and the diagnostic insights from upstream agents—so the human isn’t starting from scratch. By defining these pathways ahead of time, we ensure that human intervention is a built-in feature of the system’s resilience rather than a desperate, reactive measure taken after the customer is already lost. This approach turns a potential failure into a managed outcome where the agent knows its own limits.
An agent’s decision is only as good as its data. Beyond simply having access, how do you evaluate “data fitness” for autonomous agents, and how should a catalog identify gaps in historical patterns that might materially compromise a final resolution?
Data fitness is about more than just connectivity; it’s about whether the data is timely, complete, and high-quality enough to support a specific autonomous decision. For a Resolution Agent, we evaluate fitness by looking at whether it has access to aggregated outputs from all upstream agents as well as current domain-specific data. If the historical complaint patterns are missing, the agent may lack the context to provide a fair or consistent resolution, which is a significant gap. A Behavior Catalog identifies these gaps by mapping exactly what data is required for a successful outcome and flagging where additional or adjacent sources are needed. This creates a forward-looking view where we are constantly improving the “data diet” of our agents to ensure their decisions remain accurate and operationally sound.
When a mistake at the intake stage propagates through downstream agents, the damage can become systemic. What mechanisms are required to trace these errors end-to-end, and what practical steps must be in place to reverse faulty autonomous decisions across an entire network?
To prevent systemic failure, we need decision traceability that allows us to see how a single misclassification by a Receiver Agent flows through and impacts the final resolution. If an error is detected, the system must have a “reversibility action” in place, such as re-routing the complaint and sending an automated update signal to all downstream agents to discard the previous context. Practically, this involves the ability to roll back a resolution and notify any impacted parties or systems that the previous data was flawed. Without these mechanisms, errors compound silently, making it nearly impossible to identify the root cause once the final output is generated. Traceability ensures that every hand-off is logged, making the entire network of agents accountable for the final result.
Defining what an agent must not do is often harder than defining its capabilities. What risk thresholds should be set to prevent agents from overpromising to customers, and how do you ensure these behavioral boundaries remain consistent across different departments?
Setting risk thresholds involves creating hard “guardrails” that prevent agents from taking actions that violate business rules or ethical standards, such as a Response Agent committing to a specific refund amount it isn’t authorized to give. We should set a mandatory human escalation threshold whenever the confidence score of an agent or the data completeness falls below a certain level. To keep these boundaries consistent, they must be centralized in a Behavior Catalog that serves as the “source of truth” for all departments, from customer service to legal. This ensures that an agent in one department isn’t operating under a different set of ethical or operational rules than an agent in another. By explicitly defining these constraints, we protect the organization from agents that might optimize for speed or resolution at the expense of fairness or accuracy.
What is your forecast for the adoption of AI Agent Behavior Catalogs?
I believe that within the next few years, Agent Behavior Catalogs will become as fundamental to the enterprise as data catalogs and model registries are today. As organizations move from simple AI pilots to complex, multi-agent ecosystems, the “behavioral risk surface” will become too large to manage through technical metadata alone. We will see a shift where governance is no longer just about tracking what was built, but about defining the explicit operating context and ethical boundaries of every autonomous action. Companies that fail to adopt these catalogs will likely face systemic operational failures and a loss of accountability that could lead to significant liability. Ultimately, the maturity of an AI strategy will be measured by how well a company can explain and control the behavior of its agents, not just the number of models it has deployed.
