Adeptia has released Automate 5.3, expanding its enterprise data integration platform so business users, developers, and AI agents can create and validate data mappings through natural-language requests. Announced October 27, the update targets the work required to translate incoming partner data into formats a company’s systems can process.
The release also adds Bring Your Own LLM support, extends platform diagnostics through Observe, and introduces native Google Cloud deployment. Adeptia says version 5.3 is available to all customers.
From field suggestions to complete mappings
The central change is how mappings are built. Earlier versions offered AI suggestions within a manual mapping workflow. According to Adeptia’s AI Mapping product documentation, the new agent examines source and target schemas, builds a mapping from a plain-language description, and asks clarifying questions when it needs more information. Users can upload sample files to provide context.
Before publishing, teams can test real or sample data across an entire mapping or an individual field. The interface displays field-level reasoning and confidence, bringing decisions that require human judgment back for review. Experienced users can also inspect and edit the generated code.
For manufacturers and logistics businesses, the work can include translating product identifiers, order quantities, and shipping details received from suppliers, warehouses, or carriers. These differences in partner formats create recurring integration work whenever a new relationship starts or an existing data specification changes.
Adeptia says published mappings execute as compiled native code. The AI agent operates during design, rather than making live decisions each time a production mapping runs.
Expanding what enterprise agents can do
Automate 5.2 introduced a native Model Context Protocol (MCP) server for querying workflow status, execution history, and diagnostics. Version 5.3 extends that access to mapping creation and management, allowing customers’ own AI agents to perform those tasks within broader automated workflows.
Adeptia says the system learns from completed mappings and incorporates an organization’s terminology and business rules. Customers can add reference documents to a private knowledge base. The company claims this approach can reduce mapping work from weeks to minutes, although the announcement does not provide a benchmark methodology for that claim.
Model choice and operational monitoring
Bring Your Own LLM support lets customers use their own accounts for Anthropic, OpenAI, or Azure OpenAI, or access Anthropic models through Amazon Bedrock in their AWS environment. Self-hosted knowledge bases and deployment options across AWS, Microsoft Azure, and Google Cloud provide additional infrastructure choices.
Observe now analyzes Kubernetes logs, pod metrics, and AI traces through natural-language queries to diagnose issues and recommend fixes. Teams can monitor platform health through persistent dashboards or access Observe from Claude and ChatGPT, according to the company’s release announcement.
Sources
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