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Global Hotel Alliance Replaces MuleSoft With PolyAPI Using a Governed Multi-Agent Framework

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GHA Replaces Mulesoft

Global Hotel Alliance Replaces MuleSoft With PolyAPI Using a Governed Multi-Agent Framework

Moving from legacy iPaaS to more modern technology has become a salient conversation across the enterprise. Organisations want to leverage AI as a core capability, but they want to choose which models to use, for which use cases, without the lock-in that legacy iPaaS inherently creates. The fact that years of business logic can be embedded in a proprietary runtime has made the economics of a migration difficult. AI-assisted engineering has changed that calculus. GHA and PolyAPI partnered to pioneer a new method of legacy iPaaS migration, enabling a transition that would have previously been prohibitively costly to be executed safely, efficiently, and successfully.

This white paper breaks down:

  • Why iPaaS migrations have historically been deferred, and how switching costs became a pricing strategy rather than a technical constraint

  • How GHA and PolyAPI designed a governed multi-agent migration framework, including the two specialised agents, the deterministic Skills library, and the manifest that acts as the contract between them

  • Where humans stayed in the loop, and why deliberately rejecting full autonomy produced faster and more reliable results than automating everything

If you are weighing the cost of moving off a legacy integration platform, or working out how agentic engineering applies to production enterprise delivery rather than prototypes, this paper is for you.

5 Key Takeaways:

  1. Specialisation beats generalisation. Rather than pointing one agent at the problem, the framework splits the work in two. The Origin Language Decoder reads and interprets the MuleSoft source. The Native Enterprise Workflow Writer builds the PolyAPI implementation in native Python. Each is deeply optimised for a single task, and neither has to be good at the other one.

  2. A structured manifest is the contract between agents. Raw source code is never passed from one agent to the next. The OLD Agent produces a clean, redacted, compressed migration manifest, and the NEWW Agent builds from that blueprint. The handoff is a defined artefact rather than an accumulation of context.

  3. Deterministic Skills constrain nondeterministic models. Skills bundle contextual guidance, executable scripts, validation logic and platform standards into reusable units covering credential redaction, source compression, MuleSoft interpretation, PolyAPI primitives, retry patterns and orchestration standards. This is where consistency across migration cycles came from.

  4. Human governance is built into the workflow, not bolted onto it. Target architecture is human-provided. Agents are instructed never to make independent architectural assumptions, and every consequential decision is surfaced for validation before implementation proceeds. One of the most effective Skills lets a human architect design in Figma and pass a PNG straight to the NEWW Agent, which interprets the visual, asks clarifying questions, and presents an implementation plan for approval before writing anything.

  5. The framework is portable because the intelligence sits above the model. GHA worked primarily in Microsoft Copilot while PolyAPI engineers worked across OpenAI Codex and Claude Code, with no adaptation required. The same principles that governed the migration apply directly to building net new capabilities, which is why the framework outlives the project that produced it.

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