Boomi, Claude, and Enterprise AI

At Boomi, we've embedded Anthropic's Claude across our entire technology stack - from how we build our platform to how our customers build with it. This is that story.

AI has crossed the enterprise threshold. The shift happened beneath the hype cycle: agents that maintain context, orchestrate workflows, and write to real business systems. Production deployments are replacing proof-of-concepts. This shift demands infrastructure and reliability.

In our view, Anthropic read this moment correctly with two distinct strategies:

• First, their "race to the top" made safety a feature, not a constraint. By open-sourcing safety research, techniques, and standards that could have been a competitive advantage, Anthropic forced the industry toward reliability - unlocking enterprises where trust is table stakes. Constitutional AI and interpretability aren't academic exercises; they're audit trails and compliance frameworks that let enterprises deploy AI in production with defensible governance.

• Second, they built for enterprise reality: MCP for tool integration, Computer Use for automation, models that generate production code, and manage real spreadsheets. They're building heavy machinery, not art galleries.

This infrastructure becomes transformative when connected to enterprise systems - live data, system updates, triggered workflows. Boomi operationalizes these capabilities at three levels:

1. Development layer: AI powered mapping, error resolution, and process generation.
2. Orchestration layer: Model chaining, business logic, and complex connected workflows.
3. Mediation layer: Data pre-processing, error handling, and prompt distillation before AI models engage.

We amplify what works. AI multiplies the value of existing systems rather than replacing them.

Building the Boomi Platform with Claude

The best tool is one that helps build better tools. We use Claude throughout our development process - not as a replacement for engineering judgment, but as an amplification of it.

Claude Code as Copilot

Our developers use Claude Code as their copilot, paired with their IDE to handle the tedious work while they focus on interesting problems. Over one 10-week project, one of our engineers and Claude Code changed over 17,000 lines across dozens of repositories. The relationship evolved from "execute this command" in week 1 to genuine collaboration by week 10 - finishing each other's sentences, Claude catching missed dependencies, the engineer making strategic decisions while Claude handled the grind. The migration got done well, with no drama, in a timeframe that would have crushed a human working alone.

Engineering Skills for Development Workflows

Some teams are experimenting with Claude Skills to capture engineering best practices and workflow automation. These aren't generic templates - they're specialized agents that know how your team actually works.

• A Product Owner agent discovers and prioritizes JIRA tickets using your team's actual heuristics.
• A Developer Workflow agent orchestrates everything from branch creation through pull request, applying your commit standards and naming conventions automatically.
• A Safety Overseer agent scans for credentials before any commit touches git.
• A PR Review agent does pre-review analysis - code quality, security patterns, standards compliance - before human reviewers ever see it.

These agents don't replace judgment. They handle the mechanical parts of software delivery - the ticket validation, the workflow orchestration, the repetitive checks - so engineers can focus on the problems that require human expertise. The work still gets reviewed. The decisions still get made by people. But the tedious validation that slows teams down? Automated.

Building Agentic Solutions with Boomi and Claude

At its core, a Boomi process is workflow orchestration - it sequences business logic, manages connectivity, transforms data, and implements the mechanisms needed to reach business outcomes. These processes become powerful complements to AI models through bidirectional collaboration, with each element handling what it does best.

AI Models within Integration Workflows

When an integration process invokes an AI model via API, that model becomes a specialized step in the workflow sequence. The integration handles deterministic logic, connectivity, and data preparation, while the model processes unstructured content and makes intelligent decisions. This division of labor ensures the model receives high-signal, low-volume information precisely when needed.

This pattern scales elegantly to orchestrate tool use scenarios. When a model needs to interact with external systems, the integration process maintains workflow control - receiving structured requests from the model, calling target systems, transforming responses, and returning clean, actionable data for continued reasoning.

Integration Processes as AI Tools

The relationship also works in reverse: integration processes exposed as APIs can serve as sophisticated tools for AI agents. Rather than pointing an agent at raw endpoints, a Boomi process orchestrates complex operations - pulling from multiple sources, merging datasets, handling errors, and formatting results - all before the AI sees any data.

Consider the Boomi map step: direct agent access to a Salesforce opportunity query might return 100KB per record, most of it irrelevant to the task at hand. Route that same query through a Boomi process with mapping, and you visually select only essential fields - reducing payload size by 20x or more. This approach keeps sensitive data from the model, cuts token costs dramatically, and improves response accuracy by eliminating noise.

MCP: Making Integration Accessible to AI

Anthropic built the Model Context Protocol (MCP) to give Claude access to tools. It has become a standard protocol for any AI agent to discover and use external capabilities.

Boomi abstracts complex, cross system business functions into a catalog of out of the box Enterprise MCP Tools. These tools are deployed directly within the Boomi Fabric to enable autonomous, context aware execution of high value tasks on behalf of the AI Agent. This layer ensures that Agentic Automation is deeply integrated into the enterprise workflow, providing governed, dynamic access to proprietary productivity and application data while maintaining strict auditability and control. APIs published in Boomi's API Control Plane automatically translate into MCP servers without additional configuration or wrapper code. And agents built in Boomi's Agentstudio can discover and use MCP servers from any source - whether published from the Boomi platform or hosted externally.

This pervasive support of MCP in the Boomi platform provides a bridge between the existing landscape of deterministic automation and integration, and the reasoning-based future of agentic workflows and multi-agent systems. Processes you built for human-driven workflows become tools that agents discover and orchestrate themselves. It provides a massive headstart for any enterprise looking to drive business value through AI, while maximizing the value of existing IT investments.

Claude Code Harness for Boomi Development

Boomi's integration platform presents a challenge for AI-powered development: while AI models have extensive programming knowledge, they lack training data on Boomi's proprietary process definitions and components. We've solved this by building a harness that enables Claude Code to programmatically create, test, and deploy Boomi integrations and APIs.

The harness pairs specialized tools with a purpose-built reference corpus documenting Boomi's component specifications, patterns, and best practices. Claude manipulates components locally, then pushes directly to the Boomi platform via its tools - effectively bridging the gap between Claude’s general programming capabilities and Boomi’s specific requirements.

From Monolithic to Modular Architecture

Our initial implementation used a monolithic workspace containing tools, reference materials, and development components. Claude navigated this structure hierarchically - starting with CLAUDE.md for workspace orientation, consulting BOOMI_THINKING.md for platform philosophy, then drilling into specific component documentation as needed. This approach worked but limited flexibility and distribution.

With the release of Claude Agent Skills, we're restructuring this approach. By packaging our Boomi implementation capabilities as a distributable skill separate from the development workspace, we've gained better project-specific customization, simplified distribution to other teams, and enabled traditional Git version control for individual integration projects.

Measuring Success and Looking Forward

We built an evaluation harness that tracks Claude's first-attempt success rate at creating Boomi components. These metrics identify where our documentation or skill structure needs refinement and provide grounding for measuring improvement.

What began as pure research is evolving into practical applications. We're actively refining our implementation into a clean Agent Skill while exploring migration assistance use cases with customers and partners. Next, we'll experiment with decomposing the main skill into task-specific modules - individual skills for data mapping, SAP connections, event based architectures, and other discrete integration tasks. Through empirical evaluation, we’ll determine which approaches move from promising research to practical tools.

These are exciting times for technology in the enterprise. AI has the potential to transform every aspect of every organization’s digital landscape. Tapping that potential to its fullest starts with a solid foundation. Anthropic has become part of Boomi’s AI foundation, and the combination of Anthropic and Boomi is itself a solid foundation offering scalability and safety for all enterprises as they take their early steps into the post-AI paradigm.