Designing Platform Engineering for the Agentic AI Era: Lessons from Boomi Agentstudio
In the halls at Boomi World 2025, a shift in the fundamental architecture of enterprise software is taking shape. The integration leader's announcements around Boomi Agentstudio reveal an important truth for platform engineers: We're moving from deterministic processes to agentic ones, and this demands a complete reimagining of how platforms are designed, governed, and scaled.
From Integration to Orchestration
"Integration has fundamentally changed," Boomi CEO Steve Lucas declared in his keynote. "Integration became automation. Automation is becoming orchestration." This evolution isn't merely semantic – it represents a monumental shift in how platform teams must approach their work.
Integration, once focused on connecting disparate systems, has evolved into automation that moves information seamlessly. Now, as AI agents proliferate, with Boomi reporting over 33,000 active agents already deployed across their customer base, platform engineers face their next great challenge: orchestrating not just systems but autonomous AI entities that can make decisions and initiate actions.
Data Foundation as a Prerequisite
In an interview following the keynote, Mani Gill, VP of Product Management for AI & Data Management at Boomi, highlighted a critical insight for platform engineers: the AI strategy requires a shift in data thinking.
"There's a big push from top down, CEOs are saying 'AI everything.' But have they solved the data problem yet? If the data problem isn't solved, how's AI going to work?" Gill explained. "What AI is forcing people to do is think about the data problem differently – starting with the use case they're trying to solve rather than boiling the ocean."
This represents a departure from traditional data governance approaches, which aim to perfect enterprise data management before activating it. Gill advocates for a more targeted approach: "Identify the use case, then start fixing the data issues that solve that particular problem."
For platform engineers, this means building data pipelines that support specific AI initiatives rather than tackling enterprise-wide data quality problems simultaneously. Boomi's data integration capabilities, now incorporating the Rivery acquisition, exemplify this targeted approach, allowing rapid pipeline creation for specific AI needs like agent memory, knowledge management, and RAG (Retrieval-Augmented Generation) implementations.
"Human-in-the-Loop Agents": Redefining Automation
The most revealing aspect of the future of platform engineering is Boomi's innovative approach to human-machine collaboration. While the keynote demo showed an expense report agent that could approve expenditures without human intervention, Gill described something more nuanced: "human-in-the-loop agents."
"Agents that recognize that AI could add hallucinations," Gill explained. "So humans are part of the agentic flow."
This introduces a profound concept for platform engineers. Instead of treating AI as fully autonomous or human-controlled, we're entering an era where AI systems determine when human expertise is needed. The platform must be engineered to support this fluid machine and human decision-making interchange.
Governance First: The Platform Engineering Imperative
While industry attention fixates on agent capability (what they can do) and interoperability (how they connect through standards like Model Context Protocol), Boomi's approach prioritizes governance.
"Where we started was the governance side," Gill emphasized. "Governance is not getting a lot of investment [industry-wide] because it's not sexy, but it's necessary."
Boomi's Agent Control Tower, demonstrated during the keynote and developed in partnership with AWS, represents an approach where observability, policy enforcement, and governance are designed into the platform initially, not bolted on later.
This signals a critical priority for platform engineers: building observability and governance capabilities into agent platforms must happen before widespread deployment, not after problems emerge.
"Boomi's Agentstudio highlights a crucial pivot for platform engineering, demanding we architect for orchestrating intelligent agents, not just integrating systems," said Mitch Ashley, VP and Practice Lead DevOps and Application Development at Futurum. "The emphasis on 'governance first' is a stark reminder that robust control and observability are paramount before unleashing autonomous AI within the enterprise. Ultimately, this shift compels us to rethink data strategies around specific AI use cases, moving towards semantic understanding rather than broad data perfection."
Data Connector Agents: Reimagining Integration
Boomi's introduction of the Data Connector Agent is particularly relevant for platform engineers. "An age-old issue in the data management space has been so many systems to connect to, and source system schemas change," Gill explained. We're building an agent that allows you to connect any endpoint specification, and the agent actually creates the connector and helps keep that connector up to date."
This represents a transformative approach to integration: instead of engineers manually maintaining connectors as APIs evolve, AI agents can autonomously adapt to schema changes. This hints at a future where platform engineers focus more on the governance and orchestration layer, while AI handles many of the routine maintenance tasks that consume engineering resources.
Semantic Structures for Unstructured Data
Looking forward, Gill described work underway to organize unstructured data through ontologies and semantic models that create relationships between different data elements. This approach moves beyond simplistic RAG implementations that treat documents as isolated knowledge bases.
"Within a legal document, you have to start to create relationships," Gill noted, describing Boomi's development of "knowledge accelerators" that can organize unstructured data through ontologies to expose connections between structured and unstructured information.
The Platform Engineer's New Mandate
As AI agents become central to enterprise operations, platform engineers must evolve their approach in several key ways:
- Design for orchestration, not just integration: Build platforms that can manage the interactions between multiple autonomous agents, not just data flows between systems.
- Prioritize governance and observability: Implement robust monitoring of agent behavior, including detecting drift and hallucinations.
- Embrace human-in-the-loop architectures: Design workflows where AI can intelligently engage human expertise rather than making binary choices between automation and manual processes.
- Focus on targeted data foundations: Build data pipelines that support specific agent use cases rather than pursuing perfect data across the enterprise.
- Shift to semantic data organization: Move beyond simple document storage to create relationship-rich knowledge structures that agents can navigate intelligently.
The transformation described at Boomi World – from processes with "1000 steps to 10" through agentic automation – won't happen without platform engineers who understand both the promise and the challenges of the agentic era. By learning from Boomi's governance-first, human-integrated approach, platform teams can prepare for what appears to be the most significant shift in enterprise architecture since cloud computing.