The Problem Nobody Talks About Enough: Agent Sprawl

Most conversations about AI agents focus on building them — choosing a framework, designing prompts, connecting tools. But according to AWS Machine Learning, the challenge for organizations scaling their use of agents has quietly shifted: the bottleneck is no longer building agents, it's discovering and governing them.

When dozens of teams each maintain their own collections of agents and tools without a shared record, three compounding problems tend to emerge:

  • No authoritative inventory. There's no single place to see what exists, who owns it, or whether it's still maintained. Capabilities drift, versions diverge, and the overall landscape becomes opaque.
  • No cross-team discovery. Even well-built tools go unused because developers on other teams simply can't find them. The default response is to rebuild what already exists — wasting engineering effort and creating redundant infrastructure.
  • No governance or audit trail. Without a central record, there's no reliable way to track which agents have passed security review, who has access to what, or how to trace a failure back to a specific version and owner.

This pattern — sometimes called agent sprawl — is structurally similar to the technical debt problem in traditional software, but it compounds faster because AI agents can be created and deployed with relatively little friction.

What a Registry Actually Does

AWS Agent Registry, described as now generally available, is designed as a single, searchable, governed catalog for agents, tools, skills, and custom resources across an organization. Understanding its architecture helps clarify the broader concept.

The registry operates across two distinct planes:

The Governance Plane is the comprehensive, authoritative store. Administrators use it to configure compliance and security signals, set discovery policies that control which teams can see which resources, and define custom metadata schemas — for example, fields like cost center, data classification, or SLA tier. This plane gives admins visibility into the full agentic landscape, including resources that are still in draft or under review.

The Discovery Plane is what everyday developers and agents interact with. It surfaces only resources that have passed the organization's approval process, supports both semantic search (searching by intent, such as "find me a tool for ticket routing") and lexical search (searching by exact name), and is built for high-throughput queries so agents can search programmatically at scale.

The separation of these two planes is a meaningful design choice: administrators get comprehensive control, while consumers get a fast, curated experience that only shows resources ready for use.

What Can Be Cataloged

The registry supports four record types, which reflect the current landscape of agentic AI standards:

  • MCP — Model Context Protocol servers, including their tools, resources, and prompts
  • Agent — Agent2Agent (A2A) agent cards defining agents and their skills
  • Skill — Agent skill definitions in markdown files and associated code or packages
  • Custom — Custom descriptors in valid JSON format

This range means the registry isn't limited to one framework or protocol, which aligns with how real enterprise environments tend to look: heterogeneous, with tools and agents built using different approaches.

Real-World Context

Several organizations are cited in the AWS Machine Learning source as early adopters. Southwest Airlines described moving from dozens of agents and tools scattered across multiple technology teams with no shared record to a single, governed catalog that the entire organization trusts. Syngenta described a workflow where teams publish once, then discover and reuse what already works instead of rebuilding agents, connectors, and business procedures from scratch.

Partners are also extending the registry's reach. Informatica is integrating its hosted MCP servers so they can be discovered and used within agentic workflows. Check Point is using registry metadata alongside runtime telemetry to assess the security posture of deployed agents — correlating what an agent was designed to do with what it's actually doing during execution.

The Broader Concept Beyond One Product

The registry pattern itself is the educational takeaway here. Whether or not an organization uses this specific AWS service, the underlying problem — agents and tools proliferating without shared visibility, ownership, or governance — is a structural challenge that any team scaling agentic AI will likely encounter.

One practical implication is that governance infrastructure may need to be planned before sprawl sets in, not after. Once dozens of agents exist across multiple teams with no shared catalog, retroactively establishing ownership and audit trails becomes significantly harder.

Another lesson is that discoverability and reuse are governance problems as much as they are engineering problems. Semantic search over a curated catalog could reduce duplicative development — but only if teams actually register their work and curators maintain approval workflows consistently.

For smaller organizations or teams just beginning to deploy agents, this model suggests that even a lightweight shared inventory — tracking what agents exist, who owns them, and whether they've been reviewed — could provide meaningful value well before the scale that enterprise tooling is designed for.

Read the official announcement (opens in a new tab)

Sources