AI agents that actually work: why integration makes the difference 

How Boomi AgentStudio bridges the gap between AI experimentation and practical applicability 

Recently, together with colleagues from Yenlo, I attended the Boomi AI Agentic Workshop. We spoke with Boomi experts about strategic choices when applying agentic AI versus generative AI, and about when to choose a deterministic automation approach versus a non-deterministic AI agent architecture. We also built an AI agent hands-on in Boomi AgentStudio and connected it to relevant enterprise data via Boomi Integration and API Management. 

What stood out to me most was that the question many organisations are asking is no longer “Can we use AI?” but rather “How do we actually integrate AI into our business processes?” 

AI adoption is growing, but scalable success remains limited 

Interest in AI is significant. McKinsey reports that 62% of organisations are currently experimenting with AI agents, while only 23% are working on scalable implementations. 
In retail and supply chain, we see a similar pattern: according to SupplyBrain, around 90% of large retailers have experimented with AI in their supply chain, yet only one third have developed a strategic vision for actual integration. 
BCG notes that 74% of companies struggle to scale AI initiatives and realise value. Expectations are high: AI is expected to improve demand forecasting, enable more efficient inventory management, and support real-time decision-making. In practice, however, many initiatives remain stuck in pilot phases. 

The gap between generative AI and enterprise data

Tools such as ChatGPT, Gemini and Copilot rely on powerful large language models (LLMs) that can generate impressive text and suggestions. However, these generic language models lack the specific knowledge required to deliver real value within an organisation: up-to-date inventory data, delivery schedules, customer profiles, or the integrated data from systems that support core business processes. 

Deloitte reports that 60% of AI leaders indicate that application integration with legacy systems and addressing risks are their biggest challenges. IBM reports that 42% of companies lack sufficient access to high-quality enterprise data to successfully deploy AI models. 

The result? Organisations remain stuck in proof-of-concepts and struggle with data silos and limited governance. Without access to integrated data across systems, AI simply cannot generate actionable output that can be directly used for concrete business decisions. 
In addition, the risk of uncontrolled AI adoption arises: when teams independently implement AI tools without central coordination, AI sprawl emerges, leading to insecure data access and a lack of visibility and control over which AI initiatives are running within the organisation. 

The cost of disconnected AI

If an organisation is unable to connect AI to reliable, up-to-date enterprise data and processes, investments stall. AI projects consume time, resources and budget, but fail to deliver measurable results. The same BCG study shows that the 26% of companies that are successful with AI (“AI leaders”) achieved 1.5 times higher revenue growth and 1.6 times higher shareholder returns over the past three years. 

Teams spend months developing and testing AI pilots that never scale to production. At the same time, organisations miss tangible opportunities: in supply chain, AI could optimise inventory levels and save millions in overstock; in customer service, agents could handle repetitive tasks at lower cost and with higher customer satisfaction; and in operations, processes that currently require hours of manual effort could be automated. 

The difference between experimenting with AI and actually realising value from AI is not the technology itself, but the ability to connect that technology to enterprise data and processes. Without that connection, AI initiatives remain theoretical exercises without measurable business impact. 

How AgentStudio connects AI to your business

This is where Boomi AgentStudio comes into play: a platform for building AI agents that not only use language models, but also actively connect them to real data, tools and processes within your organisation. 

The three pillars of AgentStudio: 

With the Agent Designer, agents are designed in an accessible way using no-code templates. Here, agent behaviour is defined, guardrails are configured for safe and ethical use, and agents are tested iteratively in a controlled test environment. 

The Agent Control Tower serves as a central point of visibility for all agents within the organisation. Not only Boomi agents, but also agents running on other platforms such as AWS Bedrock or Microsoft Copilot can be registered and monitored here. This enables centralised compliance logging, observability and governance. 

With Agent Garden, agents are managed, tested and deployed into production. From this unified environment, end users can interact with agents via a chat interface, while IT maintains control over versioning, deployment and lifecycle management. 

The crucial difference: tools and integrations

Agents only become valuable when they gain access to your specific enterprise data and systems. AgentStudio provides several tool types that can be connected to agents: 

• API Tools – Execute API calls to retrieve data or perform actions in external systems 
• Integration Tools – Run Boomi integration processes to orchestrate complex workflows and connect systems 
• DataHub Query Tools – Directly query Boomi DataHub repositories to access centralised, validated enterprise data (golden records) 
• Prompt Tools – Provide agents with specific instructions and examples for specialised tasks 
• MCP (Model Context Protocol) – Connect agents to external tools through a standardised interface, making it easier to integrate with various systems without repeatedly building custom integrations 

These tools enable AI agents to operate in a context-aware manner. When a user asks a question via the chat interface, the agent determines which tools are required to answer it. The agent can consult multiple data sources via API Tools, execute complex workflows via Integration Tools, and based on the combined information formulate a well-considered response or propose a follow-up action. The agent reasons about which data is relevant and orchestrates the appropriate tools to arrive at the desired result. 

Relevant for supply chain

In sectors where fast decision-making and real-time visibility are essential, these integration patterns offer concrete benefits. 

Scenario 1 – Agent initiated via chat: 
A supply chain planner asks via the chat interface: “Which products are at risk of stock shortages next week?” The agent uses API Tools to retrieve current inventory levels and supplier lead times, and an Integration Tool to perform complex calculations combining historical sales data, logistics delays and seasonal influences. Based on this combined information, the agent generates a prioritised list of products and advises which purchase orders should be prioritised. 

Scenario 2 – Agent invoked from an integration process: 
An automated Boomi integration process handles incoming purchase orders. For each order, the process performs standard checks such as inventory availability and credit limits. When an order contains unusual characteristics, for example an uncommon combination of products or a delivery date under time pressure, the process invokes an agent via the Agent step. The agent analyses the order data, compares it with historical patterns, and determines whether special approval is required or if the order can be processed immediately. The integration process processes this advice and routes the order to the appropriate follow-up path. 

From AI experimentation to production

AI remains one of the greatest opportunities for organisations, especially in dynamic sectors such as retail and supply chain. But without the right connections to enterprise data, systems and processes, AI remains an exercise in experimentation without context. Boomi AgentStudio helps organisations combine generative AI with the power of integration, enabling concrete and actionable AI use cases. 

Would you like to explore which processes within your organisation are suitable for AI agents, and how integration makes the difference? Feel free to get in touch or continue following us for more insights.