What Breaks First Without an Agent Registry?
Introduction
Can you remember the first time you began to use AI within your company? Was it via a string of interactions with ChatGPT? Was it through a data analysis tool that was released internally? During early adoption, AI was mostly used for answering questions and understanding information. Today, AI agents also make decisions autonomously based on defined workflows: responding to customers, classifying leads, and detecting and resolving incidents.
When agents take actions on behalf of an organization, governance and transparency are more critical than ever. If an agent is connected to customer PII and can action upon it, that agent should be vetted and behind an authentication flow. If an issue management agent opens a pull request that accidentally leaks secrets in the README, an incident response team should be able to easily determine which trigger and which agent were responsible for that action.
This is where a centralized registry comes in. Having one secure location where all agents and tools exist makes it easy for teams to discover existing infrastructure, prevent redundancies, and enforce granular permissions to ensure information is only accessible to authorized users and sensitive actions are not taken without human approval.
The Purpose of an Agent Registry
So what really happens when individuals within an organization are left to deploy, manage, and govern agents on their own versus operating within a centrally managed and audited system? Let’s walk through a few scenarios:
Scenario #1: Insufficient Authorization
A sales team at a subscription-based media company seeks to analyze churn by customer segment. They are given access to an internal analytics agent in order to query why lifetime customers may be leaving the platform. However, this agent runs under a broadly permissioned service account that has access to confidential billing data including partial card numbers. A sales team member asks the agent to run a detailed analysis on the five highest value customers, returning all available data points. The agent then returns that partial card information as a part of the dataset, exposing sensitive data that the sales team should not have access to.
With a secure agent registry, organizations can set up roles that define who can use specific MCPs, agents, and tools, as well as which data and actions each agent can access. This ensures that the correct individuals and teams have access to the agent, and that the agent itself operates with permissions appropriate to its intended function. Roles also allow agents to have high-privilege access to systems to perform complex analysis and critical tasks without exposing sensitive data to users that shouldn’t see it.
Scenario #2: Shadow IT
A CX specialist designs an agent to generate proposed email responses to customer queries as they come in and save them in Gmail drafts. The specialist needs to install the Postmark and Google MCP servers to set up the agent, and does so from the internet. A product manager at the company also installs Postmark MCP server to build a prototype for a new feature and downloads a different version of the server from the CX specialist. As multiple unreviewed versions circulate across the company, the IT team can’t sustainably detect, review, and approve each one, and the shadow IT risk expands.
Shadow IT introduces compounding tech debt as well as a host of security vulnerabilities. When employees across an organization are all independently installing MCP servers and building agents, it’s easy to end up with duplicated workflows that cause a mess for IT and multiple (often unregulated) versions of third-party servers. An agent registry allows employees to have a singular reference point when looking to use third-party integrations or set up specific workflows. Employees know to only use vetted integrations from the registry and to look for existing internal agents and workflows that may satisfy a given use case prior to building bespoke ones.
Scenario #3: Tool Conflicts
An engineer designs an internal agent that helps answer non-technical team members’ questions about product functionality in Slack. The workflow reads product documentation and Slack history and sends a message using the Slack MCP’s send_message tool. However, the environment also contains other send_message tools from other servers, and at runtime, the agent calls one of those instead of the Slack tool.
With a registry, all tools are assigned a namespace and version to prevent potential overlaps. They are also associated with access data as mentioned above to prevent those without the relevant permissions from using a specific tool. An agent registry like Credal’s also supports intelligent tool selection, where workflows automate tool routing based on enterprise context from memory to run the right tool every time without guessing or manual intervention.
Why we built Credal
Things can quickly go wrong when there isn’t oversight and governance around the MCPs, tools, and agents being used across an organization’s tech stack, especially when they are connected to live systems and data. It’s analogous to each engineer at the company downloading a specific third-party dependency from a Google search on the internet and connecting it to a production database.
In addition to the scenarios above, companies can face compliance violations, data exfiltration, and/or system functionality compromise when there’s no single, vetted source of truth around what can be trusted and who can use it. IT teams can try to chase down every installed MCP or every agent connected to production data, but it’s easy to imagine how that task expands into an insurmountable one as a company’s AI adoption grows without restraint.
Credal provides a secure agent registry that is SOC 2 compliant, syncs granular permission information from connected data sources, enforces RBAC for all entities, and can be deployed across any chatUI or interface with portable memory across tools. Companies like Wise, MongoDB, and Checkr rely on the platform for incorporating AI in a way that is scalable and permission-aware for internal teams. With a registry like Credal, your organization can scale up to hundreds of agents and tools that work together to provide rich context and functionality while doing so at an enterprise-grade security standard.
