AWS Connected 3 AI Agents to Verify Aviation Component Replacements

When a required part is discontinued, finding a component with a similar name is not enough. An engineer must retrieve the specifications; compare voltage, weight, interfaces, and temperature range; inspect the enclosure geometry; determine the delivery lead time; and compile everything into a document for review. In aviation, a mistake here can cost far more than several days of manual work.

AWS demonstrated a prototype assistant that divides this task among three specialized agents. One searches for suitable parts, another checks technical tolerances, and the third works with documentation and current component lifecycle information. There is an important boundary: the system gathers evidence and prepares a draft assessment, but it does not authorize the replacement. The engineer retains the final say.

How a request moves through the system

Imagine that a reaction wheel for a satellite has become unavailable. An engineer asks the system to assess the risk that the model will be discontinued, find alternatives, and compare them with the original part.

First, a small planning component determines the sequence of actions. The catalog agent searches the parts database for candidates. The assessment agent compares their specifications. The knowledge agent retrieves information from technical descriptions, drawings, and web sources. If several specialists have worked on the request, a separate model combines their results into a single response.

This separation is useful for more than organization. Each agent can access only the tools it needs. The search agent cannot alter assessment results, while the assessment agent does not conduct unrestricted searches for suppliers. This reduces unexpected actions and simplifies auditing.

Where models are not trusted with calculations

The strongest part of the solution is that ordinary Python code performs the tolerance checks. Critical parameters and rules are defined in advance for each component category: an exact voltage match, the permitted weight increase, interface compatibility, and other constraints.

Given identical inputs, the check always produces the same result. A language model can explain the table and format a memo, but it cannot rewrite a status such as “passed,” “violation,” or “review required.” After every assessment, the system saves an action log, allowing the reviewer to see not only the conclusion but also how it was reached.

This is a useful pattern for any high-stakes process. The model handles search, document analysis, and coherent prose. Code enforces rules that must be reproduced without creative interpretation.

Why multimodal search is needed

Some engineering data does not live in tables. Torque may appear only in a chart, connector placement in a photograph, and mounting dimensions in a drawing. Amazon Nova Multimodal Embeddings maps text and images into a shared search space. As a result, a query for a specification can return the relevant diagram, while an uploaded image can retrieve a similar part.

Photographs of two components are also compared using cosine similarity, after which the model describes visible differences in mounts, connectors, and enclosure shape. Visual similarity, however, remains supporting evidence. The outcome of the check is determined by predefined engineering requirements.

The approach has clear limitations. Tolerance rules must be maintained manually, response quality depends on the completeness of the database, and web search cannot replace confirmation from the manufacturer. The demonstration uses only seven fictional parts, so it validates the architectural concept rather than readiness for real-world certification.

The practical lesson for developers is straightforward: do not build one all-knowing agent. Separate search, computation, and document preparation; move critical checks into deterministic code; retain sources and action logs; and leave consequential decisions to humans. This boundary is precisely what turns an impressive agent demonstration into a tool that engineers and auditors can seriously evaluate.