We evaluated the platforms using seven management questions.

1. Agent Inventory

A serious management system should tell you what agents exist.

That sounds basic.

At enterprise scale, it quickly stops being basic.

Agents may have been created by:

• business teams

• developers

• SaaS vendors

• cloud platforms

• automation teams

• individual employees

The first management problem is simply knowing what is running.

2. Ownership

Every production agent should have an accountable owner.

That owner may be responsible for:

• changes

• access

• performance

• cost

• incident response

• retirement

An orphaned agent with active system access is an operational problem.

3. Lifecycle State

An agent should not simply be:

on

or:

off.

Useful lifecycle states might include:

• development

• testing

• approved

• production

• restricted

• under review

• deprecated

• retired

The exact labels matter less than having an explicit lifecycle.

4. Version and Change Management

Agents change.

Instructions change.

Models change.

Tools change.

Knowledge changes.

Policies change.

A management platform should help teams understand:

• what changed

• when it changed

• who changed it

• which version is running

• whether the new version performed better

5. Operational Visibility

Teams need to understand what agents are doing after deployment.

Useful signals include:

• run volume

• completed tasks

• success rates

• errors

• escalations

• latency

• tool failures

• unusual behavior

The exact metrics will vary by agent type.

6. Cost and Business Value

An agent being busy does not mean it is useful.

At scale, enterprises increasingly need to know:

• what each agent costs

• which department owns the spend

• what business outcome it supports

• whether people use it

• whether performance justifies continued operation

This is becoming an important part of the emerging agent-management category. Dataiku, for example, explicitly positions its new management product around usage, cost, quality, business value and risk across an organization's agent portfolio.

7. Retirement

Eventually, some agents should stop running.

A mature lifecycle should include retirement.

That can involve:

• disabling execution

• removing access

• revoking credentials

• removing integrations

• preserving required records

• transferring ownership of remaining workflows

Launching an agent is not the end of its lifecycle.

1. Feather AI

Feather is an enterprise AI agent platform designed to move agents from configuration into production and ongoing operation.

For agent management, its strongest advantage is that the management layer sits directly beside the execution environment.

Feather's current product describes four broad platform functions:

• Build

• Operate

• Improve

• Govern

Its homepage describes operating agents across channels, routing work and handing off to humans; improving them using analytics, simulations and traces; and governing them with policy enforcement, access controls and audit trails.

Configure, Simulate, Publish, Iterate

Feather's product defines a simple production lifecycle.

Configure

Teams define the agent's:

• persona

• model

• tools

• knowledge

• policies

• workflow

Simulate

The agent can be run against realistic scenarios before customers interact with it.

Publish and Iterate

A revision can be activated, observed in production and replaced with newer revisions without disrupting conversations already underway.

That is a useful lifecycle pattern because production management starts before deployment.

Running Agent Visibility

Feather's current product interface publicly shows agent-level operational information including:

• running agents

• tasks

• success rate

• live status

The platform also says teams can monitor every decision, tool call and interaction through its governance layer.

Where Feather Fits

Feather is particularly relevant when the organization is using the same platform to:

build agent

↓

test agent

↓

deploy agent

↓

run customer or operational workflow

↓

monitor performance

↓

improve revision

↓

govern execution

This provides tight operational continuity.

The limitation is equally important.

Feather's public site currently emphasizes management of agents running through the Feather platform. It does not publicly document automatic discovery and inventory of arbitrary third-party agents across unrelated cloud and SaaS environments.

Why Consider Feather AI?

• Agent configuration

• Agent lifecycle

• Simulation

• Production deployment

• Revisions

• Running-agent visibility

• Task monitoring

• Success-rate monitoring

• Workflow execution

• Human handoffs

• Access controls

• Audit trails

• Policy enforcement

Best suited to: Enterprises that want to build and operate customer and operational AI agents in one environment, with management directly connected to execution.

2. Microsoft Agent 365

Microsoft Agent 365 is explicitly positioned as a control plane for AI agents.

Microsoft says Agent 365 enables IT and security leaders to observe, secure and govern agents across the organization regardless of where they originate.

That makes it substantially different from simply building agents in Copilot Studio.

Agent Inventory

Microsoft's administration environment provides a centralized agent inventory.

Administrators can review:

• agents in the organization

• availability

• users

• publishers

• capabilities

• data sources

• custom actions

• security information

• compliance information

Agent Identity

One of Microsoft's strongest advantages is the surrounding identity architecture.

Agent 365 integrates with Microsoft Entra and treats agents as manageable identities.

That can allow organizations to apply identity, authorization and lifecycle controls to agent identities rather than treating them as invisible background software.

External Agents

Microsoft also documents onboarding agents created outside its own development environments.

Custom and third-party agents can be connected to the Agent 365 control plane for management, security and observability.

Why Consider Microsoft Agent 365?

• Central agent inventory

• Agent identity

• Ownership

• Usage visibility

• Security

• Lifecycle management

• External-agent onboarding

• Microsoft Entra

• Microsoft Purview

• Microsoft Defender

• Microsoft 365 administration

Best suited to: Enterprises already standardized on Microsoft's identity, productivity and security stack that want AI agents managed through the same administrative environment.

3. ServiceNow AI Control Tower

ServiceNow AI Control Tower focuses on centralized visibility and control across enterprise AI.

ServiceNow says the platform can discover agents, models, identities and MCP servers and connect that inventory to the organization's Configuration Management Database.

Agent Discovery

The platform is designed around the idea that enterprises cannot control AI assets they cannot see.

AI Control Tower provides centralized inventory and context for both first-party and third-party AI.

Enterprise Context

ServiceNow's differentiation is the connection with the CMDB and existing ServiceNow workflows.

An agent does not exist only as:

Agent #394

It can be connected with relevant business context around:

• ownership

• systems

• services

• workflows

• risk

• enterprise assets

Performance and Value

ServiceNow also positions AI Control Tower around measuring AI performance and value rather than only security.

That matters as enterprises move from:

How many agents have we deployed?

to:

Which agents are worth continuing to operate?

Why Consider ServiceNow AI Control Tower?

• Enterprise AI inventory

• Agent discovery

• Model discovery

• MCP discovery

• Runtime monitoring

• CMDB context

• Risk visibility

• Performance measurement

• Value measurement

• ServiceNow workflows

Best suited to: Large ServiceNow customers that want agent management connected to broader enterprise configuration, risk and workflow information.

4. Kore.ai Agent Management Platform

Kore.ai launched its Agent Management Platform as a centralized environment for managing AI systems across different frameworks, clouds and development environments.

The company describes the product as supporting agents from environments including:

• LangGraph

• CrewAI

• AutoGen

• Google ADK

• AWS AgentCore

• Microsoft environments

• Salesforce Agentforce

• proprietary systems

Cross-Environment Agent Management

This is Kore.ai's biggest distinction in this comparison.

The platform is designed around a heterogeneous agent estate.

Instead of assuming every agent was built using Kore.ai, AMP attempts to provide a common management layer across agents created elsewhere.

Management at Enterprise Scale

Kore.ai's current positioning includes:

• monitoring

• evaluation

• security

• policy enforcement

• execution tracing

• operational visibility

• cost attribution

• cross-platform management

This makes Kore.ai relevant once an enterprise's AI program extends beyond one platform.

Why Consider Kore.ai AMP?

• Cross-framework agent management

• Cross-cloud support

• Centralized agent visibility

• Monitoring

• Execution tracing

• Lifecycle management

• Cost attribution

• Enterprise policy integration

• Heterogeneous agent estates

Best suited to: Enterprises that expect agents to be built across multiple platforms and want a centralized management layer above them.

5. Dataiku Agent Management

Dataiku approaches the problem primarily as portfolio management for enterprise agents.

Its Agent Management product is designed to discover agents across connected platforms and place them into a centralized inventory.

Dataiku says each agent record can include information such as:

• owner

• connected systems

• recent activity

• purpose

Manage Agents Without Moving Them

One of the most interesting differences is what Dataiku says it doesn't do.

Agent Management monitors agents where they already run.

Dataiku says it does not step into the agent's decisions or require organizations to rewrite or redeploy those agents.

That creates a clear use case.

The organization may already have agents built in:

• Microsoft

• Salesforce

• AWS

• Google

• Databricks

• Snowflake

• custom environments

Dataiku can become the portfolio visibility layer above them.

Business and Technical Performance

Dataiku's positioning extends beyond technical uptime.

It describes tracking:

• health

• usage

• cost

• quality

• behavior

• business performance

• risk

This is useful for organizations trying to decide which agents should be scaled and which should be reconsidered.

Why Consider Dataiku Agent Management?

• Cross-platform discovery

• Central agent inventory

• Ownership tracking

• Usage monitoring

• Cost monitoring

• Quality monitoring

• Behavior monitoring

• Business-value measurement

• Risk visibility

• Existing-agent compatibility

Best suited to: Enterprises that already have agents spread across multiple platforms and need a management and portfolio-visibility layer without moving execution into another runtime.

6. Boomi Agentstudio

Boomi Agentstudio combines agent development, management and orchestration.

Its product is organized into three core components:

• Agent Designer

• Agent Garden

• Agent Control Tower

Agent Control Tower

Boomi Agent Control Tower provides a central environment for registering, managing, governing and monitoring agents.

Boomi documents support for agents created in its own environment as well as agents from external providers such as Amazon Bedrock.

The Control Tower can provide information about agents including their:

• goals

• tasks

• connected tools

• provider

• activity

Connect Agents to Enterprise Work

Boomi comes from an integration and automation background.

That matters.

Agent management becomes more valuable when the system also understands the applications, data and workflows the agents are acting on.

Boomi Agentstudio is therefore particularly relevant for organizations where agent management and integration architecture are closely connected.

Why Consider Boomi Agentstudio?

• Agent Designer

• Agent Garden

• Agent Control Tower

• Central agent inventory

• Third-party agent registration

• Monitoring

• Agent lifecycle management

• Integration platform

• Guardrails

• Agent orchestration

• Centralized administration

Best suited to: Enterprises that want agent lifecycle management tightly connected to application integration and automation infrastructure.

7. OneReach.ai Generative Studio X

OneReach.ai takes a broader runtime and orchestration approach.

Generative Studio X combines agent development, communication, contextual memory, orchestration, governance and human oversight.

The platform is particularly oriented toward complex enterprise AI systems rather than lightweight personal agents.

Managing Mission-Critical Agents

OneReach positions GSX for enterprises where agents may:

• work across channels

• access multiple systems

• collaborate with other agents

• use different AI models

• maintain context over time

• execute complex workflows

Its architecture is built around keeping those systems coordinated and observable.

External Agents

OneReach also describes support for registering, orchestrating and auditing agents built outside GSX.

Its own current comparison notes that native agents receive the deepest controls, while externally built agents can receive registration, orchestration and audit capabilities depending on integration depth.

That distinction is useful because no management platform should be evaluated only on whether it says:

third-party agents supported.

The real question is:

Which management controls actually extend to those third-party agents?

Why Consider OneReach.ai GSX?

• Agent lifecycle management

• Multi-agent systems

• Agent registry

• Cross-platform agents

• Runtime orchestration

• Context management

• Human-in-the-loop

• Agent observability

• Cost attribution

• Enterprise integrations

Best suited to: Enterprises running complex, high-volume or mission-critical multi-agent systems that need management tightly connected with runtime orchestration.

Three Types of AI Agent Management Platforms

Not every product in this category manages agents in the same way.

For buyers, it is useful to separate three operating models.

1. Native Lifecycle Management

The platform primarily manages agents running inside its own environment.

This provides deep visibility because management and execution happen together.

Example: Feather.

Best when:

• your core agents run on one platform

• deep execution visibility matters

• lifecycle management should connect directly to deployment

2. Suite-Centered Control Planes

The platform manages agents through the broader identity, security, data or workflow environment of an enterprise software suite.

Examples: Microsoft Agent 365 and ServiceNow AI Control Tower.

Best when:

• the surrounding enterprise suite already has strategic importance

• agents should inherit existing IT administration patterns

3. Cross-Environment Agent Management

The management layer is designed to sit above agents created across several vendors or frameworks.

Examples: Kore.ai AMP, Dataiku Agent Management, Boomi Agentstudio and parts of OneReach.ai GSX.

Best when:

• many teams build agents independently

• different clouds or frameworks are involved

• centralized inventory is becoming difficult

This distinction matters more than the number of features in a comparison table.

How to Evaluate an AI Agent Management Platform

Do not begin with a vendor demo.

Begin with your agent estate.

Test 1: Find Every Agent

Give the platform agents from several environments.

Ask:

• Which can it discover automatically?

• Which require registration?

• Which require SDK integration?

• Which remain invisible?

Test 2: Assign Accountability

For each agent, ask whether you can see:

• owner

• department

• purpose

• production status

• dependencies

• relevant business process

Test 3: Change a Production Agent

Create a new version.

Then check:

• how it is tested

• who approves it

• how it becomes production

• whether rollback exists

• whether history is preserved

Test 4: Break Something

Cause:

• a tool failure

• an authentication failure

• an unexpected response

• an unavailable dependency

Then inspect what the management platform shows.

Test 5: Find the Cost

Ask:

How much did this exact agent cost us last month?

Then:

What business outcome did it produce?

A mature management platform should move organizations closer to answering both questions.

Test 6: Retire the Agent

This is one of the most overlooked tests.

Attempt to retire an agent.

Check what happens to:

• credentials

• access

• integrations

• active workflows

• historical records

• ownership

• audit data

Agent management should cover the end of the lifecycle too.