Not entirely. RPA remains strong for stable, rules-based UI automation. AI agents add reasoning over unstructured inputs, dynamic routing, and governed multi-step processes—often alongside RPA, not instead of every bot.

Workflow automation connects apps with predefined steps. AI agents add language-model reasoning, retrieval over enterprise knowledge, and adaptive decisions within guardrails—useful when steps depend on document content or exceptions.

When workflows mix unstructured inputs—emails, PDFs, quality notes, dealer ledgers—with ERP actions and exceptions that change week to week. RPA fits stable UI paths; agents fit document-heavy exception handling with approvals and audit evidence.

iPaaS tools excel at known API sequences. They struggle when steps require reading policies or contracts, enforcing role-based knowledge access, and retaining step-level audit trails for finance or compliance—where governed agent platforms are designed to operate.

Often yes. Many organizations keep RPA for legacy UI automation and add AI agents for exception triage, reconciliation, and compliance workflows. The integration point should share identity, approvals, and logging—not run as disconnected silos.

RPA logs bot steps; agents must also log retrieval sources, policy versions, and human approvals when models influence decisions. Regulated buyers in BFSI and healthcare should score vendors on exportable workflow evidence, not only bot success rates.