The Good AI: The Need for AI Agent Behavior Catalogs 

AI is no longer being deployed. It is being delegated to. 

Across organizations, AI agents are now making decisions, triggering actions, and interacting across workflows with increasing autonomy. They are no longer confined to isolated tasks; they are embedded within end-to-end business execution. And yet, while their role has fundamentally evolved, the way organizations manage them has not. 

Most enterprises still rely on model and agent registries as their primary mechanism of control. These systems provide visibility into what has been built: tracking versions, ownership, and deployment endpoints. But visibility is not control. Knowing that an agent exists does not explain what it is doing, how it is behaving, or whether its actions align with intended business outcomes. This gap becomes especially evident in real operational scenarios. 

Consider a customer complaint resolution workflow orchestrated by multiple AI agents. A Receiver Agent ingests incoming complaints, analyzes sentiment, classifies the issue, and routes it forward. A Response Agent acknowledges the complaint and adjusts tone based on sentiment; triggering human intervention when dissatisfaction is high. Complaint-Type Agents perform domain-specific analysis, calling relevant data sources and other agents. Finally, a Resolution Agent consolidates inputs and determines the final outcome. 

Each agent performs a clear role. But the system as a whole introduces a new challenge: Decisions are distributed, context is shared across agents, and outcomes depend on how these agents behave collectively; not just individually. 

This is where traditional registries fall short.  

From Tracking to Behavior: Why Registries Are No Longer Enough

Registries were built to answer questions of existence: what is deployed, who owns it, and where it runs. In a static AI landscape, that was sufficient. But in a multi-agent environment, these questions only scratch the surface. 

Returning to the complaint resolution example, a registry may confirm that the Receiver Agent, Response Agent, and Resolution Agent are all deployed and active. But it cannot answer far more critical questions: 

• What boundaries govern how the Receiver Agent classifies complaints? 

• When does the Response Agent escalate to a human, and how consistently does it do so? 

• What data is the Resolution Agent using to determine outcomes, and how complete is it? 

• What happens when one agent makes an incorrect decision that propagates downstream? What is the reversal mechanism? 

 These are not deployment questions, but rather they are questions of behavior. The differences between Registry vs. Behavior Catalog: 

 A registry tells you what exists. An Agent Behavior Catalog defines how it behaves. And in a system where agents act, decide, and interact: “Behavior,” not existence, is the real risk surface. 

Designing the Agent Behavior Catalog

If AI agents are to operate reliably at scale, their behavior must be explicitly defined. This is not a documentation exercise, rather it is the foundation for control. At its core, an Agent Behavior Catalog answers a single question: How does this agent behave within the business? That answer emerges through six core dimensions. 

Using the complaint resolution workflow, we can see how an Agent Behavior Catalog brings structure to what would otherwise be a loosely connected system of actions.

1. Operating Context (Sphere of Influence)

Every agent must be anchored within a clearly defined sphere of influence. This includes where it operates, what boundaries constrain it, and which systems or processes it is allowed to interact with. In the complaint workflow, the Receiver Agent is responsible for intake and classification, but not resolution. The Resolution Agent determines outcomes, but should not reclassify complaints or override earlier stages arbitrarily. 

 Without these boundaries, agents begin to blur across domains, creating ambiguity and leading to conflicting decisions or unintended actions.

2. Expected Outcomes & Exception Pathways

Each agent must be aligned to clear, measurable outcomes that define success. Equally important is defining failure i.e. what conditions indicate the agent is off track and embedding predefined escalation pathways. 

For example, the Response Agent is expected to acknowledge complaints appropriately based on sentiment. But when sentiment crosses a critical threshold, it must trigger human intervention rather than continue automated engagement. 

 This ensures that failures are not silent, and that escalation is intentional, not reactive.

3. Data Access & Data Fitness Layer

Agent decisions are only as good as the data they rely on. This dimension defines which data sources an agent can use; the timeliness, quality, completeness required;  the gaps highlighting additional or adjacent data sources that could materially improve outcomes, creating a forward-looking view of data dependencies. 

In this workflow, the Resolution Agent depends on inputs from multiple upstream agents as well as domain-specific data sources. If any of this data is incomplete, outdated, or misaligned, the final resolution is compromised. 

 An Agent Behavior Catalog ensures that data dependencies are explicit, and continuously improved.

4. Decision Traceability & Reversibility

As agents operate across workflows, organizations must be able to trace decisions end-to-end, understanding how they were made and how they propagate across systems and other agents. When failures occur, there must be mechanisms to contain and correct them, including halting downstream impact, signaling other agents, and retroactively correcting flawed context. 

If the Receiver Agent misclassifies a complaint, that error flows into downstream agents, ultimately impacting resolution. Without traceability, identifying the root cause becomes difficult and without reversibility, correcting it becomes impossible. 

 This prevents small errors from becoming systemic failures.

5. Inter-Agent Dependencies

Agents rarely operate in isolation. They pass context, trigger actions, and build on each other’s outputs. This dimension makes those relationships explicit: mapping upstream and downstream dependencies, defining hand-offs, and enabling visibility into how decisions compound across workflows. 

The Resolution Agent depends on both the diagnostic outputs of Complaint-Type Agents and the context provided by the Response Agent, including whether human intervention has occurred. 

 Mapping these dependencies ensures that decision flows are visible and manageable.

6. Behavioral Boundaries & Guardrails

While much attention is given to enabling capability, equal rigor must be applied to constraint. This dimension defines what an agent must not do, including business rules, ethical considerations, and risk thresholds. For instance, the Response Agent should never promise a resolution, and the Resolution Agent should not finalize outcomes without complete validation of inputs. 

 Without clearly defined guardrails, agents may optimize for outcomes in ways that are technically correct but operationally or ethically misaligned. 

The Cost of Operating Without an Agent Behavior Catalog

When these dimensions are not defined and cataloged, organizations are not scaling intelligence, they are scaling risk. 

• Decisions begin to drift silently from intended outcomes 

• Errors compound as they propagate across interconnected agents 

• Data is misused or underutilized due to lack of clarity 

• Accountability breaks down, making decisions difficult to trace, explain, or correct 

 These are not isolated issues. They are structural consequences of operating without a defined model of behavior. 

Closing Thoughts

Data needed catalogs. Models needed registries. AI agents need Behavior Catalogs. 

Because in the age of AI-driven execution, what you don’t define is exactly what you lose control over.