AI agent inventory: how to identify, classify, and update initiatives

As different areas begin to experiment with artificial intelligence agents, a seemingly simple question arises: how many agents exist in the organization?

The answer is rarely ready.

One team may be testing an agent to consult internal documents. Another may have put into production a solution that accesses systems, analyzes information, and executes actions. At the same time, employees use AI assistants individually, while traditional processes continue to be automated by scripts, integrations, and RPA tools.

If all those initiatives are grouped under the same label, the inventory loses usefulness. If only officially approved projects are recorded, a relevant part of the environment remains invisible.

The first step toward governing AI agents is defining what will be counted.

Not every AI solution is an agent

An AI assistant normally responds to a direct request and depends on the user to drive the interaction. Conventional automation executes rules or sequences established in advance. An agent receives a goal, interprets context, selects tools or steps, and can make decisions during execution.

That distinction does not mean one type of solution is necessarily more important than another. It helps the organization understand different levels of autonomy, access, and impact.

The concept proposed by NIST helps delimit this scope. In an agentic architecture, the system receives instructions, obtains additional context, interacts with tools or data sources, and can execute actions to achieve a given objective [1].

An initial classification can separate:

• individual use of AI assistants;
• AI features embedded in applications;
• traditional automations;
• agents capable of using tools and executing actions;
• systems composed of multiple agents.

That separation prevents a chatbot license from being accounted for in the same way as an agent with access to corporate systems.

The inventory is not a catalog of tools

Listing contracted platforms is not enough.

A single platform may host dozens of agents with different objectives, owners, and access levels. Likewise, an agent may use more than one model, consume different APIs, and depend on services maintained by other teams.

The inventory object should be the initiative or operational agent, not only the technology used.

Microsoft recommends that each agent be recorded in an organizational inventory, with information such as owner, purpose, platform, and access scope [2]. AWS expands this view by proposing a catalog that also represents capabilities, dependencies, lifecycle state, costs, and business value [3].

The inventory then answers practical questions:

Who is responsible for this agent? Why does it exist? What stage is it in? Which systems and data can it access? Who will be affected if it stops or is changed?

What information should be recorded

An initial inventory does not need to start complex. It needs to contain enough information to locate the agent, understand its purpose, and identify who can make decisions about it.

A minimum set can include:

1. Identification: name, description, and unique identifier of the agent.
2. Purpose: process served, problem solved, and expected outcome.
3. Responsible parties: business owner, technical owner, and operating team.
4. Lifecycle stage: idea, experiment, validation, production, suspended, or deactivated.
5. Scope: individual, departmental, corporate, or external public use.
6. Autonomy: whether it only recommends, prepares actions for approval, or executes actions automatically.
7. Resources accessed: systems, APIs, tools, data sources, credentials, and permissions.
8. Operational information: version, dependencies, usage volume, costs, indicators, and date of last review.

The goal is not to fill fields for obligation’s sake. Each piece of information should support a decision.

The lifecycle stage shows which controls are needed. The autonomy level helps prioritize evaluations. Access scope guides security reviews. Dependencies allow assessing the impact of a change or deactivation.

Classification helps define priorities

An experimental agent without sensitive data and without the ability to execute actions does not need exactly the same treatment as a production agent that changes records, consults internal information, or interacts with customers.

The inventory allows applying controls proportional to the context.

Agents with greater autonomy, access to sensitive information, external actions, or impact on critical processes should receive priority in identity, security, observability, and continuity evaluations.

Classification also helps identify similar initiatives. Two areas may be developing agents to query the same databases or execute very close functions. The inventory creates the opportunity to reuse components, consolidate efforts, or at least coordinate dependencies.

This is an important difference from the AI Agent Sprawl problem. Proliferation describes disordered growth. The inventory offers a concrete mechanism to see that growth, classify initiatives, and decide what to integrate, keep, review, or deactivate.

Who should maintain the inventory

If the inventory sits solely with a central team, there is a risk information will age. If each area keeps its own control, the organization loses consolidated view.

The most sustainable model tends to be federated.

A central governance function defines required fields, classification criteria, and review cadence. Business areas confirm purpose, owners, and relevance. Technical teams update versions, integrations, and dependencies. Security, identity, and privacy assess permissions and data use when needed.

The process owner does not need to know all technical details. However, they must confirm whether the agent is still needed, whether it still meets the original objective, and who answers for the results produced.

How to prevent the inventory from becoming outdated

A spreadsheet created during a one-off diagnosis may portray the environment well at that moment. Months later, agents will have changed version, received new tools, lost owners, or been abandoned.

Therefore, updating the inventory must be part of the lifecycle.

Registration may be required before production entry. Changes to model, permissions, tools, or accessed systems should trigger a review. Owner change must also be recorded. Periodically, areas should confirm whether their agents remain active and whether information remains valid.

Microsoft recommends maintaining an updated view of agent projects and deployments, associating that visibility with ongoing administration of the environment [4]. AWS suggests periodic reviews of the portfolio, considering utilization, cost efficiency, redundancy, and operational health [3].

This process also helps locate inactive agents. An agent without use, without an owner, or with outdated configuration should not indefinitely retain active permissions.

An example of classification

Consider a company with four different initiatives.

The first is an assistant used individually to improve texts. The second is an automation that transfers data between two systems following fixed rules. The third is an agent that analyzes requests, consults internal systems, and prepares a response for human approval. The fourth is an agent integrated into service that can update information automatically.

All four involve technology but present different levels of autonomy, access, and impact.

The inventory recognizes these differences. The individual assistant can be handled by general AI use policies. Conventional automation remains in the existing application management process. The two agents need more detailed records, especially on identity, tools, data, permissions, supervision, and traceability.

Correct classification avoids both unnecessary bureaucracy and absent control where it really matters.

Inventory is the beginning, not the final goal

An inventory does not govern agents on its own.

It provides the basis for the organization to know which initiatives exist, identify owners, and prioritize evaluations. From it, it becomes possible to establish approval processes, access reviews, monitoring, incident response, cost evaluation, and deactivation.

It is also important to recognize survey limits. Interviews and forms may not reveal all initiatives. Technical records may show deployed agents but not necessarily explain their purpose or business importance.

Therefore, the most useful inventory combines information from areas with operational evidence. Human confirmation clarifies context. Observability helps verify whether the agent is active, how it is being used, and which resources it consumes.

From organizational visibility to operations: ARGUS

The organizational inventory offers a broad view of initiatives, including experiments, external agents, and solutions not yet integrated into central operations.

ARGUS complements that view by offering a platform for monitoring, orchestration, and governance of agents incorporated into the operational environment.

On the platform, each agent can be associated with its owner, version, dependencies, and operational context. It is also possible to monitor executions, actions, failures, consumption, and other indicators available in each implementation.

With AI Gateway and AgentOps resources, ARGUS helps apply access policies, monitor interactions with models and tools, and correlate agent behavior with the infrastructure and digital services that sustain the organization’s processes.

That distinction is important. ARGUS does not replace the organizational survey nor automatically discover all assistants, automations, and agents existing in the company. Its contribution begins when initiatives are identified and incorporated into the operational management and supervision model.

The inventory shows which agents exist, who is responsible for them, and which resources they use. The platform makes it possible to monitor how those agents operate and whether they continue to act within defined conditions.

About Target Solutions

Target Solutions has worked for more than 15 years supporting organizations in evolving their infrastructure, monitoring, observability, automation, and operational intelligence operations.

Over that journey, it has participated in projects in complex environments across telecom, technology, government, energy, and large enterprises, contributing to higher operational efficiency and service reliability.

With the advance of AI agents, that experience also supports organizations in preparing to adopt, govern, and operate this new class of technology. Target Solutions offers services that can cover environment diagnosis, initiative inventory, maturity assessment, governance definition, process structuring, architecture design, integrations, observability, and operating model.

This work is not limited to implementing a specific platform. The goal is to help the organization build the technical, operational, and governance conditions needed to use AI agents with control and responsibility. When it makes sense for the environment, Target Solutions can also support implementation and integration of a platform such as ARGUS.

Learn about Target Solutions services for AI agents and talk with us about how to structure inventory, governance, operations, and deployment of AI agents in your organization.

About the author

Paulo Florêncio Santos de Menezes is a partner and cofounder of Target Solutions, working in infrastructure, monitoring, observability, automation, IT operations, and AI agent governance.

He holds a degree in Data Processing from the Federal University of Amazonas, an MBA in Business Management from Fundação Dom Cabral, and a Professional Master’s in Economics from Fundação Getulio Vargas.

In his Master’s research, he developed the IBEOP-IA (Brazilian Index of Occupational Potential Exposure to Artificial Intelligence), investigating the potential exposure of Brazilian occupations to AI capabilities through the combination of occupational data, artificial intelligence patents, and natural language processing techniques.

References

[1] NIST NCCoE. Accelerating the Adoption of Software and AI Agent Identity and Authorization.

[2] Microsoft. Govern and secure AI agents across the organization.

[3] AWS. Implement organizational agent portfolio management and governance at scale.

[4] Microsoft. Manage AI agents across your organization.