The next shift in how work gets done. Building, modernizing, and migrating — at AI speed.

You’ve already decided to move to webMethods. What’s stopping you?

For years, the ROI of moving to a modern integration platform has been undermined by the cost of getting there: weeks of analysis, months of hand-coded re-implementation, risk of downtime, an unpredictable budget. So the conversation ends before it starts — not because the destination isn’t worth it, but because the cost of the journey cancels out the value of arriving.

That equation has changed.

Prolifics’ AI-Powered Migration Factory runs specialized agent pipelines continuously through your estate:

• Profile: AI agents analyze what exists, even the undocumented parts
• Design: agents produce the target-state design on webMethods + Confluent
• Transform: agents execute code transformation between platforms
• Test: automated test generation built into the pipeline, not bolted on after
• Migrate data: its own pipeline stage, with quality gates throughout

Human engineers direct the factory — reviewing output, making judgment calls, applying finishing touches. The result is a structured, de-risked migration at 3–5x the velocity and 40–60% of the cost of a conventional project.

Proof: A Fortune 500 healthcare distributor had made the right call — standardize on IBM webMethods — but faced a large, undocumented MuleSoft estate with hundreds of integrations. The traditional approach would have been multi-year, multi-million, and high-risk. Prolifics’ Migration Factory got them there at 3–5x velocity and 40–60% of the cost. They moved. And they stayed moved.

You’re exploring agentic AI. What’s stopping it from becoming real?

Most organizations are experimenting with AI agents, but few are getting them into production. The challenge isn’t the idea. It’s the infrastructure underneath it.

Agents need more than prompts and pilots. They need access to live business events, trusted data, governed workflows, and the ability to act across systems without creating new risk. Without that foundation, agentic AI stays disconnected: impressive in demos, difficult to scale, and hard to trust in the real world.

That is where Prolifics’ Agentic Modernization Factory comes in.

We help you modernize the integration and event backbone your agents will depend on, connecting IBM webMethods, Confluent, Kafka, APIs, workflows, and enterprise data — so agents can sense, decide, and act with context.

In practice, that means building an agent-ready foundation that is:

• Real-time – so agents act on what is happening now, not yesterday’s data
• Event-driven – so agents can subscribe to triggers, respond asynchronously, and work without blocking business processes
• Loosely coupled – so agents, applications, and services can be added or changed without breaking everything else
• Governed and traceable – so every action has context, lineage, auditability, and control
• Connected to people and systems – so agents support real workflows, not isolated experiments

Human experts stay in control — defining the right use cases, designing the operating model, reviewing outputs, and applying governance where it matters. The factory engine accelerates the build, but your business stays in charge of how agents operate.

The result: agentic AI moves beyond pilots and becomes part of the way work gets done, connected to real systems, grounded in real-time events, and governed for production.

Your backlog isn’t waiting. Your team doesn’t need more tools. They need throughput.

Every team has more work than they can deliver. The problem isn’t effort — it’s amplification. Prolifics’ Development Factory uses AI agent pipelines to multiply what your team can ship, without multiplying headcount.

We run development as a pipeline, not a series of handoffs. AI agents work alongside your engineers — not instead of them — scoped to the roles that keep work moving:

• Business Analyst agents — turn goals and requirements into clear, testable stories
• Developer agents — generate, refactor, and complete code with guardrails and standards
• Tester agents — design and execute tests, find issues earlier, raise quality
• IBM BOB at the point of authorship — the right help in the flow of work

The outcome: more features, fewer defects, faster cycle times, and a team that gets time back to think, solve, and deliver.