As organizations deploy AI agents across multiple platforms and frameworks, they're discovering a painful truth: there's no unified way to manage, secure, and govern them at scale. WSO2 has released Agent Manager, an open-source platform designed to solve this emerging infrastructure problem by providing centralized governance, identity management, security controls, and operational oversight for AI agents running across different models, frameworks, and deployment environments.

Why Is Agent Governance Becoming a Critical Enterprise Problem?

The challenge is straightforward but urgent. Organizations can now build AI agents using an expanding range of models and frameworks, but the infrastructure used to manage their identity, permissions, behavior, and lifecycle is often fragmented. Each agent framework and cloud provider handles governance differently, leaving enterprises without a consistent way to control who can do what, when, and where.

This fragmentation creates real risks. Unlike traditional applications, agents can invoke tools, access APIs, delegate work, and interact with other agents. This creates additional questions around authority and accountability that most organizations aren't equipped to answer. When an agent makes a decision or accesses sensitive data, who is responsible? What permissions did it actually have? Can you revoke access if something goes wrong ?

What Does Agent Manager Actually Do?

WSO2's approach separates agent governance from agent logic, allowing common controls to be applied independently of the underlying model, framework, or runtime. The platform entered beta in June 2026 and reached general availability with several key capabilities.

The core focus is agent identity and authorization. Agent Manager provides a central inventory and management layer with capabilities including verifiable agent identity, role-based access, delegation, token exchange, and access revocation. It also includes lifecycle controls for moving agents through development, staging, and production, as well as the ability to suspend agents when required.

The platform includes more than 40 built-in controls covering areas such as personally identifiable information masking and rate limiting. These policies can be applied across different parts of an agent workflow, including the agent, Model Context Protocol (MCP) interactions, and large language model (LLM) layers.

How to Implement Enterprise-Grade Agent Governance

• Establish Centralized Identity Management: Use verifiable agent identity and role-based access controls to ensure every agent has appropriate permissions and can be tracked throughout its lifecycle.
• Apply Consistent Security Policies: Implement controls like personally identifiable information masking and rate limiting across all agent workflows, regardless of which framework or model powers the agent.
• Monitor Agent Behavior Over Time: Use rule-based and LLM-based evaluations to identify issues such as unexpected token consumption, behavioral changes, or declining response quality.
• Isolate Execution Environments: Deploy agents in sandboxed runtimes that provide controlled environments for execution while allowing activity to be monitored and managed.
• Choose Framework-Independent Solutions: Select governance platforms that work across multiple models and frameworks rather than tying governance to a single AI provider.

The general availability release also introduces a sandboxed execution runtime, reflecting growing concerns about agents being given access to files, tools, APIs, and enterprise systems. The Kubernetes-native runtime is intended to provide a controlled environment for executing agents, while allowing their activity to be monitored and managed.

Agent Manager also uses OpenTelemetry for tracing and includes evaluation capabilities designed to monitor agent behavior over time. Rule-based and LLM-based evaluations can be used to identify issues such as unexpected token consumption, behavioral changes, or declining response quality.

Why Framework Independence Matters for Enterprise Deployments?

WSO2's approach is built around framework and model independence. The platform supports technologies including LangChain, CrewAI, Amazon Bedrock, Azure, Ballerina, and custom-built agents, while using standards and technologies such as OpenTelemetry, MCP, and OAuth 2 extensions.

This matters because models, frameworks, and providers can change rapidly. Organizations may use different combinations for different workloads. A separate governance layer allows teams to change those underlying technologies without rebuilding identity, policy, and monitoring controls each time. The wider industry conversation suggests that agent governance is increasingly becoming a platform problem rather than simply an AI development problem.

AWS, Microsoft, and other providers are all adding combinations of non-human identity, tool authorization, policy enforcement, runtime isolation, observability, and evaluation to their agent platforms. Recent industry analysis has argued that no single platform yet provides a complete governance stack across every layer, with enterprises likely to combine identity systems, policy engines, infrastructure control planes, and isolated runtimes. That makes the idea of a framework-independent control plane increasingly relevant, particularly for organizations that expect their agent estate to span multiple models and cloud platforms.

The emerging challenge is therefore similar to earlier shifts in cloud and platform engineering: workloads may use different technologies, but organizations still require common controls around identity, security, policy, observability, and lifecycle management. The question is no longer simply how enterprises build AI agents, but how they consistently identify, govern, and control them once they are operating at scale.