AWS Agent Registry: How Runtime Tool Discovery Replaces Hardcoded Agent Wiring

Hardcoded lists of tools become problematic when the number of available options grows from five to hundreds. The AWS Agent Registry tackles this issue by moving the process of choosing a tool from compile-time to runtime. Instead of embedding every possible tool into an orchestrator, agents dynamically query a semantic registry to find the best match for the required functionality at the moment it is needed.

The decision-making process follows three steps: Discover, Instantiate, and Execute. In the Discover phase, the orchestrator receives a user request and creates a semantic query outlining the desired capability. In Semantic Search, the registry performs a vector similarity search across the descriptions of all available tools to locate the best match.

After selecting a tool in the Instantiate phase, the orchestrator starts the chosen tool and establishes a connection. Finally, in the Execute phase, the orchestrator runs the tool, receives the results, and proceeds with the workflow.

The registry is designed to store essential metadata about each tool, including its unique identifier, a natural language description, input and output schema definitions, endpoint configuration, version information, and tags for filtering. The description field is particularly important as it enables semantic search to differentiate between tools with similar purposes. A well-crafted description should clearly explain the tool's function, key parameters, and any constraints.

When the orchestrator needs to choose between multiple tools that cover similar functionalities, additional disambiguation logic is applied. This may involve filtering tools based on tags or metadata, ranking tools using signals like usage frequency or latency history, and employing fallback prompts where an LLM can make a final selection decision. While the additional latency introduced by this process is usually negligible, it becomes significant in low-latency applications.

To manage the complexity of tool discovery, the registry implements caching mechanisms. Tool metadata and connection handles are stored locally after the first discovery, and query embeddings for common intents can be pre-computed and cached. Version drift is addressed by validating the cached schema against the live schema on each invocation and using semantic versioning to track changes in input/output schemas.