How to choose a platform to manage artificial intelligence agents in the enterprise

A carrier customer reports that their connection is dropping. Before forwarding the ticket, the representative must check the subscriber record, verify whether there is a regional fault, and review the technical access history. Today, that work may require queries across different screens and exchanging messages with operations.

The company decides to test artificial intelligence agents to bring this information together and prepare the forwarding. One agent queries the subscriber record, another checks the technical signals, and a third organizes the ticket. The representative receives the case with the available information and approves the actions that require their involvement.

To choose a platform that can accompany these agents, the operator can bring the same scenario to the vendor demo. The test must show how information reaches support, what happens when a query fails, and how the team tracks the work performed.

This article uses this illustrative scenario to turn selection criteria into practical checks. The examples do not describe a real deployment nor assume that every platform offers the same features.

What to bring to the demo

The first step is to describe the support experience you want to improve. The team can select past tickets, remove personal data, and record which queries were necessary, who participated, and what characterized a correct forwarding.

This material allows comparing the current routine with the flow supported by agents. The objective may be to reduce ticket preparation time without increasing incorrect forwardings. There is no need to invent a market benchmark. The reference should come from the operation’s own data and needs.

A platform should be evaluated by the work it helps complete, considering the effort required to keep it running.

In the demo, the vendor should clarify whether the solution creates the agents, monitors external agents, or combines these functions. If the company already has an agent that queries the subscriber record, it is worth using it in the test and observing the effort to incorporate it.

That choice relates to AgentOps and operating agents in production. AgentOps, short for Agent Operations, brings together practices to monitor agents’ behavior and operation over time. Here, the focus is verifying how those practices work in the support scenario chosen for evaluation.

Inventory and responsibility across the lifecycle

Tracking executions must be associated with a view of the agents the company keeps in operation. In the Cloud Adoption Framework’s governance guidance, Microsoft recommends an organizational record that identifies the owner, purpose, platform, and access scope for each agent, along with a distinct identity to attribute its actions [3].

In the demo, ask to locate the three support agents and identify who is responsible for each. The agent that queries the subscriber record should have different access than the one that organizes the ticket. The team must be able to verify those permissions and know how the record will be updated when something changes. The vendor should show what is discovered automatically, what arrives through integration, and what requires manual registration.

Next, remove one agent’s query permission in the test environment and try to run the task again. Observe whether access is blocked, where the attempt appears, and who receives the information needed to act. A name in the inventory alone does not demonstrate that permissions are being enforced. That relationship between common governance and distributed tools was discussed in the article on the AI Control Plane, the control plane for agents from different platforms.

Integrations must work in exceptions too

The initial connection to a system shows only part of what needs to be evaluated. A query may return incomplete data, a response may be delayed, and an update may complete without confirmation reaching the agent.

In the operator’s scenario, interrupt the confirmation after the ticketing system has received the request. If the agent repeats the operation, will it open another ticket for the same case? Ask for a demo of how the integration prevents or detects duplication and how the representative finds which record to use.

The integration test must show what happens when the flow stops following the expected path.

It is also important to separate what the platform offers from what will depend on configuration, additional development, or resources from the connected systems. That distinction makes the deployment estimate more realistic and helps distribute responsibilities between teams and the vendor.

IBM, a technology and consulting company, recommends an orchestration layer that coordinates agents across platforms and respects process boundaries [4]. As a selection criterion, it is worth verifying handoffs between stages. If the agent that queries the data finishes its part but the forwarding agent fails, the team needs to know what remains pending and who should take continuity.

The demo should also clarify how the necessary context reaches the next stage and how a person receives the case when they need to intervene. The history should allow resuming support without forcing the customer or the professional to reconstruct everything from the start. These are tests proposed for the company’s evaluation, and their implementation depends on the architecture and integrations available.

In practice, the evaluation should include an agent already used by the company, when possible. That allows verifying the effort to incorporate it into the management environment, which information remains available, and which limitations remain.

Visibility into execution and into the environment

When several agents participate in a support interaction, isolated records can make investigation harder. The team needs to relate the initial request to the queries performed, the tools invoked, and the outcome produced.

Google Cloud’s architecture documentation, Google’s cloud computing platform, includes observability and evaluation among the components to consider in agentic applications [1]. When selecting a solution, that guidance can be translated into a test where the team itself reconstructs an execution and locates the step where the process deviated from expectations.

In the support example, incorrect data in the subscriber record requires a different response from a query failure or a forwarding error. AI agent observability helps understand these differences, provided the records are high quality and can be related to one another.

Ask, for example, for an operations professional to open the execution and identify whether information was missing in the subscriber record, whether the technical query failed, or whether forwarding was done incorrectly. Then verify whether someone without authorization can consult the personal data recorded. Both tests help evaluate the usefulness of the records and access boundaries.

The team needs to understand both the agent’s execution and the conditions under which it acted.

When the activity depends on infrastructure and services, AIOps, the application of artificial intelligence to IT operations, can complement this analysis. By relating technical events to the agents’ work, it can help distinguish an agent failure from environmental degradation. That combination enriches the operational context but is not a prerequisite for adopting AgentOps.

Autonomy with proportional oversight

Reviewing a customer’s history and changing a configuration in production have different consequences. Controls should match the reach and reversibility of each action, allowing routine tasks to be automated and reserving human intervention for situations that justify it.

AWS, Amazon’s cloud computing platform, recommends pairing autonomy with proportional human oversight and defining escalation paths [2]. To evaluate a platform, it is worth testing an action that requires approval and observing what information reaches the responsible person.

At the operator, suppose the flow can request a configuration change but depends on a professional’s approval. The screen should show the affected customer, the proposed change, and the information that justifies it. Reject the request and verify that the action remains blocked. In another run, leave the approval unanswered and observe how the case is routed, without assuming authorization by silence.

Stopping new actions does not automatically undo what has already been executed.

If the agent changed a record or sent a request to another system, recovery may require a compensating action or a specific procedure. The evaluation should clarify these limits and identify how the team regains control while preserving a record of what occurred.

Changes must be evaluated before and after deployment

A platform will be used in an environment that keeps changing. New instructions, tools, or model versions can alter the behavior of an agent that previously met the criteria defined by the company.

IBM recommends incorporating evaluation into the AgentOps cycle, with tests before deployment and continuously in production [4]. For platform selection, the implication is verifying how changes are identified and how their results can be compared with a prior baseline.

In the proof of concept, the team can select representative support cases, including situations with incomplete data or a need for human forwarding. After a controlled change, it should compare correct task completion, rework, and consumption. A worsening after the change must be distinguished from variations in the request profile.

In the support test, include a case without a regional outage, another with a confirmed outage, and a third with an unavailable query. After changing the agent’s instructions, verify whether it continues distinguishing these situations and avoids presenting missing information as confirmation of normality. The evaluation should clarify which tests are native, which depend on external tools, and how results relate to the version evaluated. It also needs to show the procedure for removing a problematic version or returning to a prior configuration, when supported. That possibility does not replace recovering actions already executed in other systems.

The article How to evaluate AI agents in production deepens the distinction between a technically successful execution and a task truly completed. In platform selection, that distinction needs to appear in the tests and reports presented.

Costs tied to results

The financial comparison includes licenses, integration, infrastructure, model consumption, and operational effort. A seemingly cheap execution may require new attempts and corrections that raise the cost of completed work.

Ask to compare two support cases that reached correct forwarding. In one, the agents performed the necessary queries. In the other, they repeated calls because of an unstable integration. Does the report identify the consumption difference and its cause? Can the team relate that cost to the support case and the agent involved?

Beyond model consumption, account for the professional’s time to review or correct each case. The article What it costs to operate an AI agent discusses these costs beyond tokens, the units used to measure content processing in language models. A reduction in model spend may lose relevance if rework increases.

It is also worth verifying whether data can be separated by agent, team, or process, according to the organization’s needs. That granularity helps understand costs and target improvements. Available reports and their limitations should be part of the demo.

A proof of concept that supports the decision

The evaluation can start with a bounded process, few users, and the systems needed to represent the flow. The scope should be enough to observe a successful execution, an integration failure, and an approval-dependent action.

The script should define expected outcomes and evidence to examine beforehand. Besides watching the vendor’s demo, it helps for the team itself to investigate an execution, consult the records, and identify what was completed. It should also verify which data can be exported and in what format, considering operational continuity and a possible solution change.

At the end, the decision can be guided by seven checks.

Record of agents, owners, and accesses, with updates across the lifecycle.
Integration and coordination among agents, people, and systems, with known deployment effort.
Ability to follow the process and investigate unexpected situations.
Operation of permissions, approvals, and intervention mechanisms in the tested scenarios.
Evaluation of changes with test cases and comparison of results across versions.
Costs and outcomes aligned with the objective defined for the activity.
Access and export of records needed for continuity and investigation.

The proof of concept should produce evidence the team can use to decide and operate.

These tests do not remove all uncertainties. They allow a more grounded choice and help define next steps for scaling. Confidence to expand agent autonomy is also built through the ability to understand results, fix issues, and keep responsibilities clear.

From vision to practice: ARGUS

Evaluating a platform needs to show how its capabilities help monitor agents in the process the company chose. ARGUS, Target Solutions’ Operational and Agentic Intelligence platform, uses its AgentOps capabilities to support orchestration, monitoring, observability, and governance of AI agents.

By connecting agents, people, and tools in a shared operational context, ARGUS offers a perspective to understand executions and their relationship to operations. The AI Gateway layer, which mediates access to AI models and services, complements this approach by supporting policies for access and use of those resources. When combined with AIOps capabilities, data about agents can be related to infrastructure and service events, depending on the integrations available.

An evaluation can start from a company process and verify, in a demo, how available capabilities meet integration, tracking, and oversight needs. Scope and success criteria should be agreed for that scenario.

Explore ARGUS’s AgentOps capabilities.

About Target Solutions

Target Solutions has spent more than 15 years helping organizations evolve 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.

About the author

Paulo Florêncio de Menezes is cofounder of Target Solutions and has spent more than 15 years on projects related to infrastructure, monitoring, observability, automation, and IT and telecom operations.

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

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

References

[1] Google Cloud. Choose your agentic AI architecture components. Technical documentation. Accessed October 20, 2026.

[2] AWS. Design principles. AWS Well-Architected Agentic AI Lens. Technical documentation. Accessed October 20, 2026.

[3] Microsoft. Govern and protect AI agents across the organization. Cloud Adoption Framework. Sections on control plane, record, identity, and lifecycle. Accessed October 20, 2026.

[4] IBM. Scale AI agents for business. Recommendations on orchestration across platforms, evaluation in the AgentOps cycle, and business outcomes. Technical documentation. Accessed October 20, 2026.