I’ve been looking at how AI agents are changing the way teams build software and manage their daily work, and one thing stands out to me: giving an AI agent the ability to do something is easy compared with making sure it does the right thing.
An agent might be able to read a codebase, update files, call APIs, access company data, or execute commands. But should it have permission to do all those things? Who checks its actions? And what happens when it makes a decision that nobody expected?
These questions are becoming more important as companies move beyond AI assistants that simply answer questions and start using agents that can take action with less human involvement.
That is where AI agent governance comes in. It covers everything from managing agent identities and permissions to monitoring actions, enforcing policies, tracking activity, and deciding when a human needs to step in.
I wanted to look at the tools that help organizations manage these risks, from enterprise identity and security platforms to specialized AI governance solutions and tools that coordinate agents within engineering workflows.
Here are six AI agent governance tools and platforms worth considering in 2026, what each one does, and how to decide which approach makes sense for your team.
What Is AI Agent Governance?
AI agent governance is the set of policies, controls, and processes used to ensure that AI agents operate within approved boundaries.
Traditional AI governance often focuses on model performance, fairness, transparency, privacy, and regulatory compliance. Agent governance adds another layer because agents can take actions, use tools, access resources, and sometimes delegate work to other agents.
For example, imagine an AI coding agent assigned to fix a bug. It might need permission to read a repository, modify specific files, and run tests. It probably does not need unrestricted access to production credentials or permission to deploy directly to production.
A governance framework helps define those boundaries and establish what happens when an agent reaches them.
In practice, I would break agent governance into six areas:
- Identity and access: Knowing which agent is acting and what resources it can access.
- Policy enforcement: Defining which actions are allowed, restricted, or require approval.
- Monitoring and auditability: Recording what an agent did, which tools it used, and what results it produced.
- Risk management: Identifying potentially harmful behavior, excessive permissions, and policy violations.
- Human oversight: Ensuring that people can review, approve, reject, or stop important actions.
- Lifecycle management: Tracking agents from creation and deployment through updates, suspension, and retirement.
Not every platform covers all six areas. Some focus on enterprise security and compliance, while others concentrate on controlling tool calls or coordinating the actual work agents perform.
Understanding that distinction is important when comparing the available options.
1. Microsoft Entra Agent ID: Best for Agent Identity and Access Governance
When an organization already uses Microsoft Entra for identity and access management, extending those controls to AI agents is a logical starting point.
Microsoft Entra Agent ID provides identity capabilities designed specifically for AI agents. It helps organizations establish agent identities, manage their lifecycle, govern access, and apply security controls.
The central idea is straightforward: an AI agent should not be treated as an anonymous process with unrestricted access to company resources. Its identity, ownership, and permissions should be managed deliberately.
Microsoft's approach includes agent identity management, access governance, Conditional Access, identity protection, and monitoring capabilities. The available features depend on the particular Microsoft services and licensing in use.
This becomes particularly useful when a company has many agents operating across different applications and departments. Without a consistent identity model, it can become difficult to determine which agent accessed a resource or who is responsible for that agent.
Key capabilities:
- Create and manage identities for AI agents.
- Establish ownership and accountability.
- Govern access to applications and resources.
- Apply identity-based security policies.
- Monitor agent activity and manage agent lifecycles.
Best for: Organizations that need centralized identity, permissions, and lifecycle controls for agents across their enterprise.
Considerations: Agent identity governance is only one part of the overall picture. Teams may still need separate tools for model evaluation, application-level policy enforcement, workflow orchestration, and engineering task management.
Website: Microsoft Entra Agent ID
2. Amazon Bedrock AgentCore: Best for Runtime Controls and Tool Permissions
An agent can have a valid identity and still perform an action it should not be allowed to perform. That is why identity management needs to be complemented by controls closer to the agent's execution environment.
Amazon Bedrock AgentCore provides infrastructure for building, deploying, and operating AI agents. Its capabilities include runtime environments, identity integration, gateways, and policy enforcement.
One particularly relevant feature is the AgentCore Policy engine. It can evaluate and authorize agent tool calls when configured with a compatible gateway, helping teams enforce rules about which actions an agent can take.
Consider an agent that needs to retrieve customer information through an internal tool. Rather than trusting the agent to follow its instructions every time, a team can establish policies that constrain which actions and resources it may access.
This is an important distinction: instructions tell an agent how it should behave, while enforced policies can place technical restrictions on what it is permitted to do.
Key capabilities:
- Run agents in managed execution environments.
- Connect agents to tools and services through gateways.
- Apply policies to supported agent tool calls.
- Integrate authentication and authorization controls.
- Configure access to specific resources.
Best for: Developers building agent applications on AWS who need infrastructure-level controls and policy enforcement.
Considerations: AgentCore is an infrastructure and developer platform, not simply a governance dashboard. Teams need to configure policies, identity, and integrations for their particular architecture. It is most attractive when its execution and infrastructure capabilities fit the existing stack.
Website: Amazon Bedrock AgentCore
3. Sharkly: Best for Coordinating and Governing AI Agent Workflows
When I think about AI agent governance, I don't think only about enterprise compliance policies or access permissions. I also think about what happens when agents start performing actual work across an engineering team.
Who assigned the task? What instructions did the agent receive? Is it still running? What changes did it make? And how can someone review the result before it becomes part of the codebase?
These are practical questions that become harder to answer when agents operate through disconnected sessions and developers have to track everything manually.
That is where Sharkly becomes relevant.
Sharkly connects task management with AI agent execution. Teams can organize engineering work, assign tasks to agents, track progress, and review results within a shared workflow. Rather than treating every agent session as an isolated activity, the platform connects execution to the task the team is trying to complete.
For example, a team could assign an agent to investigate a bug, with the task containing the problem description, requirements, and relevant discussion. The agent can execute the assignment, while its progress and results are associated with the task so the team can inspect what happened.
Sharkly also supports isolated working directories and Git worktrees for supported repository workflows. This helps reduce interference when multiple agents are modifying code concurrently. Workspace isolation does not eliminate every merge conflict, but it gives teams a more controlled way to run independent tasks.
Another useful aspect is compatibility with existing coding agents, including Claude Code and Codex, alongside other supported agent environments. Teams can incorporate agent execution into their workflow without necessarily replacing the tools they already use.
Sharkly also supports Jira integration, including importing issues and synchronizing work during migration. This is useful for teams that want to connect AI-assisted development to an existing issue-tracking process instead of rebuilding their entire workflow.
Key capabilities:
- Assign engineering tasks to AI agents.
- Keep task context, discussions, progress, and results together.
- Track execution states and investigate failed or blocked runs.
- Use isolated workspaces for supported repository workflows.
- Connect Jira and other tools to existing engineering processes.
- Keep people involved in reviewing results and making decisions.
Best for: Engineering teams that want better accountability, execution visibility, and coordination around AI agent work.
Considerations: Sharkly addresses the operational side of agent governance rather than replacing a dedicated enterprise AI risk or compliance platform. Organizations that require centralized agent identity governance, organization-wide regulatory reporting, or universal runtime policy enforcement may need additional controls.
Website: Sharkly
4. Credo AI: Best for Enterprise AI Risk and Compliance Governance
For some organizations, the biggest governance challenge is not controlling an individual tool call. It is demonstrating that AI systems across the business meet internal policies, risk requirements, and applicable regulations.
That is the area addressed by Credo AI.
Credo AI focuses on AI governance, risk management, and compliance. Its platform helps organizations assess AI systems, establish governance policies, manage risks, and collect evidence that supports oversight.
Its approach is particularly relevant for organizations that need to manage AI deployments across multiple departments, rather than evaluating every application in isolation.
For example, a company may need to establish which AI systems are deployed, what risks they introduce, who is accountable for them, and whether required reviews have been completed. A governance platform can help bring those responsibilities into a repeatable process.
As organizations introduce more agentic systems, the governance process also needs to account for how agents use tools, interact with other systems, and act within the permissions they receive.
Key capabilities:
- AI system and agent discovery, where supported.
- Risk assessment and governance workflows.
- Policy and compliance management.
- Risk visibility and monitoring capabilities.
- Audit evidence and accountability processes.
Best for: Enterprises managing AI risk across multiple teams, applications, and regulatory requirements.
Considerations: A governance platform does not automatically secure every agent or enforce every runtime restriction. Organizations should verify which controls are enforced directly and which depend on integrations, monitoring, or organizational processes.
Website: Credo AI
5. LangSmith: Best for Agent Tracing, Evaluation, and Observability
You cannot govern an agent effectively if you cannot understand what it is doing.
When an agent produces an unexpected result, developers need more than the final answer. They may need to inspect the sequence of model calls, tool invocations, intermediate steps, and errors that led to that result.
LangSmith provides tracing, observability, evaluation, and testing capabilities for applications built with language models and agents.
For developers, this makes it useful for investigating failures and measuring how agent behavior changes as prompts, models, tools, or workflows are updated.
For example, suppose a customer-support agent repeatedly provides incorrect answers after calling an internal knowledge tool. Tracing can help a team investigate whether the problem originated in the retrieved information, the tool response, or the agent's subsequent processing.
Evaluation workflows can also help teams test agent behavior against defined criteria before and after changes.
I would distinguish this from a complete enterprise governance platform. Observability gives teams visibility into behavior, but visibility alone does not guarantee that a risky action will be blocked.
Key capabilities:
- Trace agent execution and tool interactions.
- Debug multi-step workflows.
- Evaluate outputs against defined criteria.
- Test changes to prompts and agent configurations.
- Monitor application behavior during development and operation.
Best for: Engineering teams building AI agents that need detailed execution traces, testing, and ongoing evaluation.
Considerations: Teams may need additional identity, access-control, policy-enforcement, and compliance tools. The available capabilities depend on the application architecture and the LangSmith plan.
Website: LangSmith
6. IBM watsonx.governance: Best for Structured AI Governance Programs
Organizations deploying AI across multiple business functions may need a consistent way to manage risks, document evaluations, and oversee AI systems throughout their lifecycle.
IBM watsonx.governance is designed for AI governance, risk management, and compliance. It supports governance workflows around AI systems, including monitoring and documentation intended to help organizations manage their AI portfolios.
This makes it relevant for teams that need repeatable governance processes rather than handling every model or application as a separate case.
For example, an organization may require AI systems to undergo risk assessments, document their intended use, complete evaluations, and maintain evidence of ongoing monitoring. A structured governance platform can help coordinate those requirements.
When evaluating it for agentic AI, however, I would verify the exact agent-specific capabilities available in the current product configuration. Governance requirements for autonomous agents can extend beyond those for conventional predictive models or generative AI applications.
Key capabilities:
- Manage AI governance and risk workflows.
- Support documentation and oversight across AI systems.
- Help organize compliance-related processes.
- Monitor relevant AI performance and risk indicators.
- Integrate governance activities into enterprise AI operations.
Best for: Enterprises that need a structured AI governance program across a portfolio of models and applications.
Considerations: Confirm that the specific agent monitoring, runtime controls, and integrations your organization requires are supported by the products and plans being evaluated.
Website: IBM watsonx.governance
Quick Comparison: AI Agent Governance Tools
These platforms address different layers of governance. A tool that provides excellent tracing may not enforce access policies, while a platform that manages agent identities may not organize engineering tasks or evaluate every agent response.
The right combination depends on where your biggest risks lie and how much control your existing infrastructure already provides.
How to Choose the Right AI Agent Governance Platform
I would start by identifying the risk you are actually trying to control.
If your biggest concern is access and permissions
Look at Microsoft Entra Agent ID or the identity and authorization features available in your existing cloud platform. The goal is to ensure that agents have identifiable owners and only the access they need.
If agents can take actions through tools and APIs
Look at runtime controls and policy enforcement, including Amazon Bedrock AgentCore if it fits your architecture. You want technical restrictions that can block prohibited actions, not just instructions asking an agent to behave responsibly.
If compliance and audit evidence are the priority
Evaluate platforms such as Credo AI and IBM watsonx.governance. Check how they support your particular governance framework, reporting requirements, risk assessments, and evidence collection.
If developers need to understand agent failures
LangSmith is worth considering for tracing, debugging, and evaluating agent behavior. Pair observability with access controls and enforcement mechanisms where the risk requires them.
If your team needs accountability for coding-agent work
Consider Sharkly when the main challenge is connecting agents to real engineering tasks and making their progress and results visible. It is particularly relevant when your team already uses Jira and wants to incorporate agent execution into its existing workflow.
Best Practices for AI Agent Governance
Choosing a platform is only part of the process. I would establish a few basic practices before allowing agents to take on more consequential work.
1. Give Every Agent a Defined Purpose and Owner
Document what each agent is intended to do, who is responsible for it, and which systems it needs to access. Avoid deploying agents with broad permissions simply because those permissions make the initial setup easier.
2. Apply the Principle of Least Privilege
Give agents only the permissions needed for their assigned tasks. An agent that reviews code should not automatically have permission to deploy to production. An agent that summarizes documents should not automatically have access to every confidential file.
3. Separate Instructions From Enforced Controls
Agent instructions are useful, but they are not a security boundary by themselves. Use access controls, policy engines, restricted environments, and approval gates to enforce important limits.
4. Record Actions and Preserve Context
Keep useful records of agent assignments, tool calls, decisions, outputs, and errors. The exact level of detail should depend on the sensitivity of the work, but teams should be able to investigate important actions rather than relying solely on the final response.
5. Establish Human Approval for High-Impact Actions
Not every action requires manual approval. Low-risk tasks can often be automated. But actions involving production systems, sensitive data, financial transactions, or irreversible changes may need explicit human authorization.
6. Test Governance Controls Before Deployment
Test what happens when an agent requests unauthorized access, receives malicious instructions, encounters unexpected data, or attempts to perform an action outside its assigned scope.
A policy is only useful if it works under realistic conditions.
7. Review Agent Access Over Time
Agents change. Their tasks, integrations, and responsibilities may expand. Review permissions periodically, disable unused agents, and remove access when it is no longer required.
Frequently Asked Questions
What is an AI agent governance platform?
An AI agent governance platform helps organizations manage the risks associated with agents that can take actions, use tools, and access business systems. Depending on the product, it may provide identity controls, policy enforcement, monitoring, risk assessments, audit evidence, or workflow oversight.
What is the difference between AI governance and AI agent governance?
AI governance covers the broader management of AI systems, including risk, transparency, privacy, compliance, and accountability. AI agent governance adds specific concerns around agent identities, tool access, delegated tasks, autonomous actions, and the boundaries within which agents operate.
Can one platform handle all AI agent governance requirements?
Sometimes a platform covers several areas, but organizations should not assume that one product handles everything. Identity management, runtime policy enforcement, observability, compliance reporting, and engineering workflow management are different capabilities and may require separate tools.
How can engineering teams govern AI coding agents?
Engineering teams can start by assigning agents clear tasks, limiting repository and tool permissions, isolating concurrent work, recording execution results, and requiring review before important changes are merged. Tools such as Sharkly can help connect agent execution to task ownership and progress tracking, while other tools provide deeper identity or runtime controls.
Is monitoring enough to govern AI agents?
No. Monitoring helps teams understand what happened, but it may not prevent a harmful action. High-risk workflows should combine monitoring with appropriate access restrictions, enforced policies, and human approval where necessary.
Final Thoughts
I think AI agent governance will become increasingly important as organizations move from experimenting with assistants to delegating meaningful work to autonomous systems.
The challenge is not simply preventing agents from making mistakes. It is building a workflow in which agents have clear responsibilities, appropriate permissions, observable behavior, and well-defined limits on what they can do.
There is no single tool that solves every part of this problem. Microsoft Entra Agent ID focuses on identity and access, Amazon Bedrock AgentCore offers runtime and policy capabilities, Credo AI and IBM watsonx.governance address broader governance requirements, and LangSmith helps developers understand and evaluate agent behavior.
For engineering teams, I would also pay attention to the operational side of governance: who assigned the work, what the agent was asked to do, where it ran, what it changed, and who reviewed the result. Sharkly is worth considering for that layer, especially when teams want to connect agent execution with their existing engineering workflow.
Ultimately, good governance should not make every agent action unnecessarily difficult. It should make the boundaries clear, the risky actions controllable, and the important decisions accountable to people.
That is how teams can make greater use of AI agents without losing sight of security, reliability, and responsibility.
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