Digibee

🎊 Excited to share a new enhancement that makes building intelligent, agent-driven integrations even faster in Digibee. You can now add MCP tools directly from the registry into the Agent Component. Previously, configuring an MCP tool meant manually copying endpoint information from the Run page. That small but persistent friction is gone. With the new MCP From Registry tab, you get: ✅ Instant access to available pipelines deployed via the MCP Server Trigger (Test or Prod) ✅ Visibility into authentication type, configured tools, parameters, and version ✅ Auto-filled key fields like Name and Server URL when you select a pipeline ✅ A multi-step setup reduced to just a few clicks For technology leaders building data-driven, customer-centric experiences, this means less time wiring up infrastructure and more time delivering the personalized, outcome-driven journeys that move the business forward. Exactly the kind of compounding productivity gain that helps high-performing teams stay future-ready in a rapidly evolving AI landscape.

The friction is real. MIT researchers recently deployed an AI agent in a hospital. They spent 80% of their time on the project handling implementation and deployment details, and only 20% of their time on the core of the agent. "For every hour spent perfecting a model, expect roughly four hours to make it work in the real world," the team wrote. In the report "A Field Guide to Deploying AI Agents in Clinical Practice," the team detailed five categories of "heavy lifts" that delayed them from deploying the agent into production, including data integration and model validation. Building the gold-standard data set to measure the model against took six months, according to the report. This friction exists for good reason. Deploying AI agents in sensitive environments comes with risks. Those risks should be managed. This team's experience demonstrates that, for most organizations, deploying agentic AI takes more time than you might predict. But organizations that invest in centralized data infrastructure, standardized governance frameworks, and reusable security templates can compress that timeline significantly. The heavy lifts don't disappear, but they do get lighter.

Here's what the Digibee team has learned while on-site at #AdobeSummit. The proliferation of AI has increased the need for more data. More data has increased the need for more integrations and better orchestration. We've heard from a number of you here that you have a full suite of Adobe products but unsure how to get optimal value from what you have. DM any of our team members on-site to learn how to get better value realization from your tech stack: Brandon Bruce, MBA Tiago Bernardinelli Wayne Lopez

HOT OFF THE PRESS! 📰 Create your own AI Agent with the Agent Component. This component abstracts the APIs of major LLM providers so you can easily perform tasks such as text classification, information extraction, summarization, content evaluation, and more — all inside your Digibee pipelines. Check out some of the practical examples of how to use it today. 👇 https://lnkd.in/gWpEa9NA

Innovation doesn’t fail because of core systems. It fails when integration can’t keep up. On April 2, Digibee joins the IBM Ecosystem Spotlight to discuss how modern integration architectures are helping banks move faster, extend their core and prepare for AI. If you want to innovate at scale, this is the conversation.

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Your martech stack is only as intelligent as the data flowing through it. We're seeing marketing teams rush to layer AI onto their existing infrastructure in order to achieve: Smarter personalization Predictive analytics Automated campaigns But all without addressing the fundamental question: is our data actually ready for this? When customer records are duplicated across Salesforce, HubSpot, and your data warehouse, when attribution data conflicts between platforms, or when campaign performance metrics don't reconcile, AI doesn't fix these problems. It amplifies them at scale. The reality is that most marketing operations challenges aren't AI problems. They're integration problems. Before you can trust AI to make decisions about customer journeys or budget allocation, you need clean, synchronized data moving reliably between your systems. By establishing a solid data foundation with real-time syncs, proper governance, and seamless connections across your martech ecosystem, you're not just preparing for AI, you're ensuring your marketing decisions today are based on a single source of truth. The teams winning with AI aren't the ones who adopted it first. They're the ones who got their data house in order before turning on the automation.