Reinventing iPaaS for the AI Era
Building the AI-Native iPaaS in Public
Building the AI-Native iPaaS in Public
AI-Native integration rewrites the rules, changing how humans and I work together, reshapes the architecture that connects systems, and changes how operations run every day.
We're not keeping this behind closed doors. Each week, we're sharing our discoveries, our progress, and the patterns that are emerging, so you can see the future and help shape it as part of a growing community of like-minded leaders and vendors.
Newsletter Issue #1:
Newsletter Issue #1:
Agentic Integration Without Risk? You Need the Right Mode
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The Agentic Integration Dilemma
The Agentic Integration Dilemma
High-Stakes Scenarios
Financial transactions, purchase orders, and regulatory compliance require reliability that's non-negotiable
Autonomy Becomes Liability
Misinterpreted rules, hallucinations, and task failures can eclipse the benefits of autonomous systems.
Runtime Risk
Traditional approaches let AI agents improvise at runtime, creating unpredictable outcomes in critical workflows
3 Agentic Modes Enabling Autonomy for Every Use Case
3 Agentic Modes Enabling Autonomy for Every Use Case
Use cases in real-world environments vary widely in speed, predictability, and acceptable risk. And the way you apply AI autonomoy must reflect that.
Before deciding how AI agents should beheve, it's essential to undersand the nature of the business scenario you're solving. That's why we've developed three distinct modes of AI autonomy.
Governed Mode
AI Agents build, maintain, and adapt Integration "As Code" for mission-critical processes with full governance and control.
Live Mode
AI Agents making live decisions for creative, exploratory, and human-triggered tasks.
Orchestrated Mode
AI Agents solve specific tasks within structured workflows, balancing reliability and flexibility.
The Future is Trusted Autonomy
The Future is Trusted Autonomy
The AI automation landscape has been plagued by confusion, driven by mixed success stories, overhyped results, massive potential impact, significant risks when AI goes wrong, and failed proof-of-concepts.
Until now, the conversation has been one-sided, focusing solely on AI agents automating tasks live, without addressing how to manage AI agents' accuracy issues and inherent risks.
The Agentic Integration Framework finally provides a trusted path forward, enabling organizations to use AI for automating integrations across business use cases with the contorl, governance, and reliability that enterprise success demands.