Top 10 AI Agent Management Platforms in 2026

This guide compares ten platforms built to register, govern, coordinate, and monitor AI agents across an enterprise.

Disclosure: This guide is published by OneReach, and our platform, Generative Studio X (GSX), is one of the ten platforms reviewed. We held GSX to the same format, sources, and scrutiny as every other vendor, including the same consideration of trade-offs. See the How We Built This Guide section for details on our approach. For disclosures, corrections, or updates, contact [email protected]. 

Key Takeaways:

• As agents increase in number, management becomes as important as deployment. Organizations need to know which agents exist, what they can access, who is responsible for them, what they cost, and whether they still deliver value.
• AI agent management platforms provide centralized visibility and governance for agents, helping organizations register agents, assign ownership, control access, monitor activity, and manage costs.
• The right platform depends on where your agents run and how your organization operates. Cloud-native platforms prioritize runtime control, suite-anchored platforms connect governance to business workflows, and cross-environment platforms provide oversight across clouds, frameworks, and vendors.

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Not long ago, getting one AI agent into production was a milestone. Today, building and deploying an agent is easier. CIOs and other IT leaders face a different question: “How do we govern these agents as they multiply?”

Gartner predicts that by 2028, the average Fortune 500 enterprise will be running more than 150,000 agents, compared with fewer than 15 in 2025. Max Goss, Sr. Director Analyst at Gartner, said, “CIOs and IT leaders see an explosion of AI agents across their organizations, many are contending with an ungoverned sprawl of agents that expose their organizations to a range of risks, including misinformation, oversharing and data loss.”

McKinsey’s 2026 AI Trust Maturity Survey found that only 30% of organizations reach level three or higher (out of four levels overall) in strategy, governance, and agentic AI controls. Nearly two-thirds of respondents identified security and risk concerns as the top barrier to scaling agentic AI, ranking it above regulatory uncertainty and technical limitations.

The constraint on deploying AI agents is often a lack of confidence in governance and controls. As agent fleets grow, organizations need a way to understand which agents are running, what levels of access they have, who owns them, what they cost, and what value they bring to the organization. AI agent management platforms are designed to provide management and control across these areas.

What Is an AI Agent Management Platform?

An AI agent management platform helps ensure that all enterprise agents are registered, governed, and orchestrated. Gartner describes this market category as “a centralized software platform that manages AI agents across a deployment environment. It supports both custom and marketplace agents, providing a unified interface for cost and ROI management, governance oversight, and mitigation of agent sprawl.” It is distinct from an agent builder, which is a point solution for creating individual agents one at a time.

Gartner’s market overview (request your complimentary copy here) names three problems this category exists to solve:

• Agent sprawl: agents multiply across departments faster than IT can track them, leaving no clear view of what is running, who owns it, or what it costs.
• Agentic FinOps: token spend and delivered outcomes need to be tracked down to specific autonomous decisions, not just reported as a monthly bill.
• Unified cross-platform oversight: agents built in-house and agents acquired from external marketplaces need to be supervised from one hub, rather than from the separate environments that produced them.

Gartner also predicts that by 2030, AMPs will guardrail 50% of all agent-to-agent interactions and capture over 60% of AI’s compounded value (source).

How We Built This Guide

We reviewed each platform using publicly available information: vendor product documentation, release notes, pricing pages, and announcements; published analyst research, including Gartner’s coverage of the agent management platform market; and public user reviews on Gartner Peer Insights and G2. No vendor paid for inclusion, and no other vendor reviewed its entry before publication.

Every profile follows the same structure: where the product came from, what the vendor says it does, who it fits best and least, and what buyers should weigh. When we report a limitation, we tie it to documentation or a named review source, and we note when reviews cover a vendor’s broader platform rather than its agent management product. We applied the same standard to GSX.

Platforms are grouped by approach and listed alphabetically within each group. The grouping reflects where each product’s control is strongest; it is not a ranking. The analysis is current as of October 2026. If you believe an entry is inaccurate, contact [email protected], and we will review it and date any correction.

Three Approaches to Agent Management

The vendors on this list came to agent management from different markets, including cloud infrastructure, integration, conversational AI, contact center software, CRM, IT service management, productivity software, process automation, and HR and finance systems. That background shapes each product’s strengths and limitations, and it sorts them into three broad approaches.

Table 1. Three Approaches to Agent Management

Approach	Where control is strongest	Main trade-off	Platforms in this guide
Cloud-native runtimes	Agents built and run inside one cloud	Weaker control over agents running elsewhere	Amazon Bedrock AgentCore
Suite-anchored control planes	Agents that use the suite’s data, identity, and workflows	Governance is centered on the suite; outside agents may get registration more than control	Microsoft Agent 365, NiCE Cognigy, Salesforce Agentforce 360, ServiceNow AI Control Tower, UiPath Maestro, Workday ASOR
Cross-environment platforms	Agents spread across clouds, frameworks, and vendors	More architecture decisions up front; depth of control varies by integration	Boomi Agentstudio, Kore.ai AMP, OneReach GSX

The lines are not hard. Most suite vendors now register third-party agents, and most cross-environment platforms work best with agents built on their own tools. For each vendor, the question to ask is the same: which controls apply to agents built elsewhere, and which apply only to native agents?

The 10 Best AI Agent Management Platforms in 2026

Cloud-Native Runtime

AWS: Amazon Bedrock AgentCore

Cloud-native infrastructure for running and securing agents in production on AWS

Best fit: Enterprises building custom agents on AWS that want managed runtime, identity, and observability instead of building those layers themselves. It is less suited to organizations that want a business-facing console for agents that mostly run outside AWS.

Amazon introduced its first managed agent service, Agents for Amazon Bedrock, in 2023, for running agents within its own orchestration framework. AgentCore was previewed in July 2025 and reached general availability in October 2025 as a set of composable services that work with open-source frameworks and any model, whether or not it runs in Amazon Bedrock. The services cover Runtime, Memory, Gateway, Identity, Observability, Code Interpreter, Browser, Policy, and Evaluations.

AWS points to three services for agent management:

• AgentCore Identity provides identity-aware authorization and secure vault storage for refresh tokens, with native integration for OAuth-enabled services. Agents can act on behalf of users or autonomously, with access limited to the appropriate scope.
• AgentCore Gateway converts APIs and Lambda functions into agent-compatible tools and brings existing MCP servers together through a single governed endpoint.
• AgentCore Observability is OpenTelemetry-compatible. It reports through Amazon CloudWatch dashboards and integrates with external providers such as Dynatrace, Datadog, Arize Phoenix, LangSmith, and Langfuse.

AWS also says AgentCore can catalog agents and other resources regardless of where they are built or hosted, including on AWS, other cloud providers, or on premises.

What to weigh:

• Runtime performance and cost. Companies need to weigh speed and performance against cost to maintain positive ROI for each agent.
• Multiple cost meters. AgentCore services are billed separately, with model inference and CloudWatch observability costs on top. Policy also charges per authorization request. Factor these usage-based costs into your total cost estimate.
• AWS dependencies. AgentCore relies on AWS services such as IAM and CloudWatch. Ask how much of its policy, identity, and observability model applies to agents cataloged from other environments but run elsewhere.

Suite-Anchored Control Planes

Microsoft: Agent 365

A control plane for observing, governing, and securing agents, built on Microsoft’s identity, security, and compliance stack

Best fit: Microsoft 365-centric enterprises that want agents governed with the same identity, security, and compliance tools they already use for employees. It may be less suited to organizations that need deep runtime control over agents built and run outside the Microsoft ecosystem.

Microsoft previewed Agent 365 at Ignite in November 2025 and added capabilities in March 2026. It reached general availability on May 1, 2026, priced at $15 per user per month or included in Microsoft 365 E7. Microsoft describes it as the control plane to observe, govern, and secure agents and their interactions.

Agent 365 extends tools Microsoft customers already run:

• Registry and observability: Discovers and inventories local, cloud, and SaaS agents, including unsanctioned “shadow” agents, and tracks which devices they run on.
• Identity and access: Gives each agent its own identity in Microsoft Entra, with least-privilege policy templates and network controls that inspect agent traffic.
• Security and compliance: Connects to Defender for threat context, Intune for blocking unmanaged agents on Windows devices, and Purview for oversharing prevention and audit evidence.

Microsoft says Agent 365 manages ecosystem agents from partners such as Kore.ai, n8n, and Zendesk, with registry sync for AWS Bedrock and Google Cloud in public preview at launch.

What to weigh:

• SDK onboarding sets governance depth. Third-party agents need to be onboarded through the Agent 365 SDK for identity-level policy. Agents that aren’t onboarded can be discovered but are not fully governed.
• Per-user pricing. Agent 365 is licensed per user, not per agent. Model the cost against how many employees work with agents and whether you already plan to move to E7.
• Cost tracking. The core capabilities center on identity, security, and compliance. Ask how token spend and outcomes are attributed to individual agents.

NiCE Cognigy: Agentic AI Platform for CX

CX-native agentic orchestration built for enterprise contact centers

Best fit: Enterprise contact centers automating customer journeys end to end and managing AI and human agents under a single operating model. It is less suited to organizations managing agent estates that extend beyond customer service.

Cognigy was founded in Düsseldorf in 2016 and initially built a conversational AI platform for contact centers. NiCE, a long-standing contact center software vendor, acquired Cognigy in September 2025. NiCE offers Cognigy both as part of its CXone platform and as a standalone product.

At NiCE World 2026, the company announced Guardian AI, which provides runtime governance, guardrails, and real-time monitoring of AI and human actions. It also introduced the Workforce Empowerment Suite, a single operating model for managing and governing human employees and AI agents.

Earlier in 2026, NiCE Cognigy introduced embedded multivariate testing, which lets teams compare prompts, guardrails, routing logic, fulfillment strategies, and foundation models side by side before release. It also expanded its support for the Model Context Protocol (MCP), allowing Cognigy capabilities to be exposed as governed services to external AI tools and development environments.

What to weigh:

• Governance is strongest within the customer journey. If your agent estate includes finance, HR, and supply chain agents, establish early whether those agents fall within this control plane or require a different one.
• Integration. Ask how the roadmap differs for standalone Cognigy customers and CXone Mpower customers, and which Guardian AI capabilities require CXone.
• Customer reviews. Public reviews on G2 point to limitations in analytics, advanced chat-flow capabilities, and support for extensive workflows.

Salesforce: Agentforce 360

A CRM-anchored agentic platform extending into third-party tools, systems, and agents

Best fit: Salesforce-centric enterprises that want to manage native and third-party agents alongside CRM data and permissions. It may be less suited to organizations whose agent estates operate largely outside Salesforce and MuleSoft.

Salesforce added AI to its CRM with Einstein in 2016 and acquired integration platform MuleSoft in 2018. Agentforce launched at Dreamforce in September 2024, with pricing starting at $2 per conversation. In May 2025, after customer feedback on that model, Salesforce introduced Flex Credits at $0.10 per agent action, followed by user-based licenses and add-ons.

Agent management expanded through MuleSoft. MuleSoft Agent Fabric was introduced in 2025 as a layer for registering, orchestrating, governing, and observing agents across enterprise ecosystems. Agent Scanners reached general availability in January 2026, automatically discovering agents and registering them in the Agent Registry. MuleSoft says the scanners support ecosystems including Amazon Bedrock, Google Vertex AI, Microsoft Copilot Studio, Databricks, and Claude.

Trusted Agent Identity lets agents execute actions using specific user permissions, with privileged operations subject to verification and audit. In September 2026, Salesforce introduced the Trusted Enterprise AI Harness, bringing together six composable capabilities and a new AI Control Plane for discovering, managing, and controlling agents and AI across Salesforce and third-party systems.

What to weigh:

• Pricing. Agentforce offers per-conversation, per-action, and per-user pricing. Model each agent’s cost under your contract before scaling.
• Data Cloud adds to the cost. Some Agentforce features consume Data Cloud or Flex Credits. Check which Data Cloud, CRM, and Agentforce capabilities are included and which are metered separately.
• Discovery is broader than control. Agent Scanners can discover agents running in external ecosystems, while Agent Fabric can enforce governance at the communication layer. Confirm which controls apply to each external agent and whether its traffic passes through the required gateway.
• Test permissions end to end. Verify that object-, field-, and action-level permissions hold throughout an agent workflow.

ServiceNow: AI Control Tower

A command center for discovering, governing, and securing AI assets, built on ServiceNow’s IT service and asset management platform

Best fit: ServiceNow-centric enterprises that want to govern agents, models, and other AI assets through the same risk, asset, and workflow processes they already run for IT. It may be less suited to organizations without an established ServiceNow footprint, or those looking for a lightweight agent registry.

ServiceNow started in IT service management and expanded into workflows across IT, HR, and customer service. It introduced AI Control Tower at Knowledge 2025. At Knowledge 2026 in May, ServiceNow expanded the product across five areas (discover, observe, govern, secure, and measure), with the enhancements reaching general availability in August 2026. Recent acquisitions feed the product: Traceloop for agent observability and Veza for identity and access governance.

ServiceNow highlights:

• Discovery across systems: 30 new connectors spanning AWS, Google Cloud, Microsoft Azure, SAP, Oracle, and Workday, extending to non-human identities.
• Runtime oversight and enforcement: live monitoring of agent behavior, least-privilege enforcement using Veza’s access graph, and the ability to shut down agents that go off-script.
• Risk and value: risk frameworks aligned to NIST and the EU AI Act, plus cost and ROI dashboards for tracking AI spend.

What to weigh:

• Value depends on the ServiceNow foundation. Ask which modules and licenses AI Control Tower requires, and how much of its discovery and risk model depends on a well-maintained ServiceNow data model.
• Discovery versus control. Connectors can find agents across clouds. Confirm which runtime controls apply to agents whose actions do not pass through ServiceNow.

UiPath: Maestro

A cloud-native orchestration platform that unifies automation, AI agents, and people in end-to-end business processes

Best fit: Enterprises whose agents need to operate within long-running workflows with audit trails, recovery, and human checkpoints. It may be less suited to open-ended knowledge work where there is no defined process to orchestrate.

UiPath was founded in Bucharest in 2005 and grew from an RPA company into a broader automation platform. It went public in 2021 and has since expanded its focus to agentic automation. Maestro reached general availability on April 30, 2025, as part of the UiPath platform for agentic automation.

UiPath approaches agent management through business process orchestration. Maestro models processes in Business Process Model and Notation (BPMN) 2.0 and defines decision logic in Decision Model and Notation (DMN) decision tables, with decision tables versioned and audited alongside the BPMN model. Rules, robots, people, and AI agents can then run within the same governed workflow.

In August 2026, UiPath introduced Maestro Flow, a developer-first orchestration surface for building, running, observing, and governing end-to-end processes that connect coding agents, AI agents, automations, APIs, and enterprise systems.

What to weigh:

• Open-ended agents fit less cleanly into BPMN. Ask how agents without a defined process are registered and governed.
• Cost and skills can add up. Gartner Peer Insights reviewers cite licensing costs and a learning curve for more advanced capabilities. Plan for both when scaling.
• Check controls for third-party agents. UiPath says its governance layer applies across agents and models, including third-party models. Verify which approvals, audit trails, and recovery controls apply to external agents in a Maestro process.

Workday: Agent System of Record

A single system for managing a blended workforce of people and AI agents, with an analytics hub for visibility into agent activity

Best fit: Workday customers that want to onboard agents, manage ownership, track costs, and measure performance alongside their people and financial data. It may be less suited to organizations that need an agent management layer independent of Workday.

Workday has sold cloud-based HR and finance software since 2005. It announced the Agent System of Record (ASOR) in February 2025, applying the same lifecycle-management approach used for employees: registration, configuration, activation, and deactivation. In June 2025, it added the Agent Partner Network and Agent Gateway, using MCP and A2A to connect partner agents to ASOR.

ASOR became generally available in early 2026. Its governance model covers Workday, partner, and third-party agents, while OpenTelemetry alignment gives customers a centralized view of agent metrics.

In June 2026, Workday announced Agent Passport, which tests and verifies Workday and third-party agents before production and continuously monitors them afterward. It also provides signed, auditable attestations from verified partners, starting with Cisco AI Defense. Agent Passport was announced for early access in the second half of 2026, with general availability planned before the end of the year.

What to weigh:

• Governance is Workday-centered. For agents that operate outside the Workday ecosystem, check what visibility and controls ASOR provides when they don’t use Workday data.
• Registration isn’t enforcement. More than 65 partners connect agents to ASOR. Ask what controls apply to partner agents at runtime, beyond registration and metrics.
• Usability. Gartner Peer Insights reviews cite navigation and customization issues with Workday Platform.

Cross-Environment Platforms

Boomi: Agentstudio

An agent governance and lifecycle layer from a long-standing integration vendor, with cross-vendor agent registration and monitoring

Best fit: Organizations already standardized on Boomi for integration that want agent oversight in the same platform. It is less suited to teams looking for a standalone agent-building environment or deep operational control over agents built elsewhere.

Boomi was founded in 2000 as a cloud integration and iPaaS platform. Dell acquired the company in 2010, and Boomi became independent again in 2021 after being acquired by Francisco Partners and TPG.

Since then, Boomi has expanded through acquisitions into federated API management (APIIDA and the former Mashery business), data integration and change data capture (Rivery), and managed file transfer (Thru). Agentstudio, originally called Boomi AI Studio, reached general availability in May 2025. Its background in integration, connectivity, and governance shapes how Boomi approaches agent management, extending capabilities it already had rather than building them from scratch.

Boomi describes Agentstudio as four components:

• Agent Designer: Builds and deploys agents using low-code templates, trusted enterprise data, and built-in guardrails.
• Agent Garden: A central interface for managing, deploying, testing, and interacting with agents.
• Agent Control Tower: Centralized governance and monitoring across Boomi and registered third-party agents, including usage and errors.
• Agent Marketplace: Prebuilt and customizable agents from Boomi, its partners, and the Boomi Community.

What to weigh:

• Third-party control varies. Agent Control Tower supports native connections to Agent Garden, Amazon Bedrock, Microsoft Copilot, Salesforce, Snowflake Cortex, and Anthropic Claude Managed Agents. Custom accounts can register agents from other environments, but they require manual maintenance and have fewer management capabilities.
• Tracing favors native agents. Agent Control Tower’s session logs and step-by-step reasoning traces are available for Boomi Agent Garden agents, not custom-account agents.
• Editions matter. Agentstudio is available in Base, Pro, and Enterprise editions. Base is included with the platform license, while Pro and Enterprise are sold separately. Check which Control Tower capabilities are included in your edition.
• Operational effort. Gartner Peer Insights reviewers cite a steep learning curve and high maintenance costs for Boomi Enterprise Platform. Boomi Data Integration reviewers also mention limited logging that can make troubleshooting difficult. Those comments concern the integration platform, and agent governance in Boomi runs on the same foundation.

Kore.ai: Agent Management Platform

A unified command center for evaluating, monitoring, and governing agents across frameworks, clouds, and development environments

Best fit: Enterprises with agents spread across multiple frameworks, clouds, and development environments that want pre-production evaluation and per-interaction cost tracking in one place. The platform is relatively new, so buyers looking for a long public track record may find fewer years of product history and user reviews.

Kore.ai spent about a decade building enterprise AI for customer and employee experiences, initially through its conversational AI platform (XO Platform). It introduced its Agent Platform in March 2025 as a platform for building and orchestrating multi-agent systems. The Agent Management Platform (AMP) followed in March 2026 as a separate management layer for agents built across frameworks and clouds. In May 2026, Kore.ai launched Artemis, a new generation of its Agent Platform.

Kore.ai says AMP connects to agents built with LangGraph, CrewAI, AutoGen, Google ADK, AWS AgentCore, Microsoft Foundry, Salesforce Agentforce, and proprietary systems. It highlights two core capabilities: an evaluation studio for testing agent behavior before production and observability that tracks agent performance and costs across heterogeneous environments.

What to weigh:

• Limited AMP track record. AMP launched in March 2026, so public reviews largely cover Kore.ai’s broader platform. Gartner reviewers cite a steep learning curve and, in one private-instance review, bugs in newer GenAI features and slower access to updates. If you self-host, ask about the release cadence for private deployments.
• Usability issues. G2 reviewers frequently mention learning curve, usage limitations, slow performance, and software bugs. These reviews cover Kore.ai broadly, not AMP specifically.
• Product naming has evolved. Kore.ai has introduced several agent products and generations since 2024. Confirm which product and contract line include the management capabilities you are comparing.
• Cross-framework coverage depends on integrations. AMP supports agents across multiple frameworks and environments, but verify how each of your agents connects and what AMP can actually monitor and govern.

OneReach: Generative Studio X (GSX)

A platform for governing, orchestrating, and building AI agents for mission-critical, high-volume work, across models, channels, and enterprise systems

Best fit: Enterprises that can’t afford for their agents to fail, such as high-volume or highly regulated operations in healthcare, financial services, government, and telecommunications. It also suits organizations that want more than oversight: multi-agent systems and a shared library of reusable agents and components, so value compounds as the estate grows. It may be less suited to organizations whose main goal is to inventory and govern every agent across the enterprise, including the long tail of personal productivity agents. OneReach focuses on durable, mission-critical agents rather than the full multitude of lightweight ones.

OneReach was founded in 2013 and built its early platform around conversational AI. GSX extends that foundation into agentic orchestration. OneReach deploys GSX as a Private Dedicated Environment, either hosted by OneReach or installed inside the customer’s own cloud, and in 2026 it received FedRAMP authorization for public-sector use. In 2026, OneReach also released orchestration of externally built agents, with trace and audit for agents not built on GSX.

OneReach describes the GSX architecture in three core layers:

• Communication Fabric: an event-driven layer that unifies channels into one continuous session. OneReach says every agent passes through it, so it can record interactions and agent reasoning, and correct or cut off an agent’s access to channels and systems.
• Cognitive Orchestration Engine: Decouples agents from individual AI models and services, so models and deterministic flows can be selected and combined as needed.
• Contextual Memory System: Preserves and shares short- and long-term context across interactions, agents, and applications. OneReach says state is kept outside the model, with each write moderated and stamped with its source.

OneReach maps GSX to the three problems Gartner identifies for this market:

• Agent sprawl: A living agent registry identifies and monitors all agent behavior, plus a library of more than 1,200 prebuilt components that carry governance into every agent built from them. Teams reuse rather than rebuild.
• Agentic FinOps: A FinOps dashboard that attributes consumption and cost to each agent by flow, step, and department. OneReach passes model tokens and compute through at cost, and deterministic flows can replace model calls where reasoning isn’t needed.
• Cross-platform oversight: Registration, orchestration, and audit for agents built outside GSX, including agents from platforms such as ServiceNow and Workday, with support for open protocols including MCP and A2A.

What to weigh:

• Native agents get the most. Agents built on GSX inherit its guardrails, contextual memory, and certified components automatically. Externally built agents can be registered, orchestrated, and audited, but not every capability extends to them. Ask which controls apply to each agent you plan to bring in from another platform.
• A broad platform has a learning curve. GSX covers building, orchestration, and governance in one system, which means more to learn than a single-purpose tool. Reviewers say complex journeys can become hard to maintain without consistent naming, versioning, and testing. OneReach offers self-serve, guided, and fully managed support to offset this.
• Architecture takes planning. A cross-environment control plane can require more upfront design than agent management embedded in an existing application.
• Capacity-based pricing. GSX is licensed by environment size rather than per agent or per user. That makes costs easier to forecast as agent counts grow, but it means sizing capacity up front. Ask how environments are sized and how you move between tiers.

Managing AI Agents at Enterprise Scale

The platforms in this guide represent the main approaches emerging in the AI agent management market: cloud-native runtimes, suite-anchored control planes, and cross-environment platforms. They all tackle the same problem: helping organizations see and control AI agents as they spread across departments, systems, and environments.

Cloud-native runtimes give you deep control over agents running inside that cloud. Suite-anchored control planes keep governance close to the business data and workflows the suite already manages. Cross-environment platforms reach further across clouds, frameworks, and vendors, but you have to make more architecture decisions up front. Many enterprises will end up using more than one.

Agent counts will keep climbing, but more agents isn’t the goal. What matters is knowing which agents exist, who owns them, what they can access, what they’re doing, and whether they’re still earning their keep. Answering those questions is the job of an agent management platform.

Analysis current as of October 2026. Platform capabilities and corporate ownership change quickly; verify specifics with vendors before purchase.

FAQs

1. What is an AI agent management platform?

An AI agent management platform is software that helps organizations register, govern, monitor, coordinate, and manage AI agents across an enterprise. It provides centralized oversight of agents across different teams, systems, models, and deployment environments.

2. What should I look for in an AI agent management platform?

Key capabilities include agent registration and discovery, identity and access controls, governance and policy enforcement, observability, cost and ROI tracking, lifecycle management, multi-agent orchestration, and human oversight. The importance of each capability depends on the organization’s architecture and agent estate.

3. What are the main types of AI agent management platforms?

AI agent management platforms generally fall into three approaches:

• Cloud-native runtimes, which provide deep control over agents running in a specific cloud environment.
• Suite-anchored platforms, which manage agents alongside business applications, data, and workflows.
• Cross-environment platforms, which provide management across departments, clouds, models, and agent frameworks.