Connecting Legacy Systems to Google Agentspace: A Practical Guide

AI agents are everywhere in business talk these days, and for good reason. They can instantly find information, automate repetitive tasks, and genuinely streamline your daily operations. It sounds fantastic, and it absolutely can be. 

But here's a common problem many companies face: these powerful AI agents often operate in a bit of a bubble. They're cut off from what we call "enterprise truth", that crucial, real-time, and secure data living across your company's various systems.

When your AI agents don't have this direct link to your core business data, especially from legacy systems, they're essentially working with incomplete information. This really limits how much value they can bring. 

While Google Agentspace provides a brilliant AI platform with its own connectors for popular apps and the ability for agents to call APIs, these tools often hit their limits when you're dealing with the real complexities of a large enterprise. Think intricate, multi-step processes, diverse legacy systems that have been around for ages, and super strict security demands.

This is precisely why a robust integration layer is essential. Application Integration is the bridge, connecting Agentspace to every corner of your enterprise, especially those complex, legacy systems and bespoke applications that hold your most critical 'enterprise truth.' It's not just another way to connect; it's often the best way. Let's break down why.

Understanding the Integration Challenge with Legacy Systems

Before we dive into the 'how,' it's important to recognise the specific challenges legacy systems pose when trying to integrate them with modern AI platforms like Agentspace:

Complex Workflows and Orchestration

Legacy systems often aren't standalone data repositories; they're part of intricate, multi-step business processes. Agentspace's native tools are great for simple data retrieval. Still, they aren't designed to orchestrate a sequence of conditional steps across multiple systems, wait for asynchronous responses, or handle complex error recovery.

Diverse and Obscure Connectivity

While Agentspace has a growing list of connectors for modern SaaS applications, it won't have a direct connector for every bespoke, decades-old, or on-premise system your enterprise relies on. Building custom connections directly into Agentspace or managing raw API calls for these systems can be complex, time-consuming, and hard to maintain.

Ensuring "Enterprise Truth" with Consistent Execution

For critical business operations, you simply can't have AI agents guessing or producing inconsistent results. Data must be accurate, and processes must be followed precisely.

Embedding Advanced Security and Governance Logic

Retrieving data is one thing; applying specific security rules, anonymising sensitive information, or enforcing complex access controls before data is presented to an agent or user is another. Agentspace might not have the granular control needed for highly bespoke governance at the data source level.

Handling Asynchronous Operations

Many enterprise processes aren't instantaneous. They might involve a request to a backend system that takes time to process and then sends a callback. Agentspace's direct interactions are typically synchronous, which can be a limitation.

Enabling Legacy Connectivity to Google Agentspace through Application Integration

Application Integration is purpose-built to address these very challenges. It's a visual, low-code platform that lets you build custom, reusable business processes with surprising ease. 

Instead of writing complicated code, you can design sophisticated workflows just by dragging and dropping elements. This makes advanced integration accessible to a much wider group of people in your organisation.

Here's how Application Integration specifically helps connect Agentspace to your legacy systems:

Mastering Complex Workflows and Orchestration

Application Integration is built for designing and executing complex, deterministic workflows. It can coordinate calls to different systems, wait for their responses, apply business logic, transform data, and then pass the result to the next step, all in a reliable, auditable way. 

This is important for processes like generating an insurance quote (checking risk, credit, calling a premium calculator) or creating a policy (extracting data from a PDF, validating, calling a backend system).

Seamless Connectivity to "Any" System (Especially Legacy and On-Prem)

Application Integration boasts over 150 pre-built connectors, but crucially, it also provides robust capabilities for building custom connectors and managing private access to systems behind your firewall. This means it can reach virtually any data source, no matter how old or obscure, and expose its functionality in a standardised, secure way that Agentspace can then easily consume. It acts as the universal translator for your enterprise's diverse tech stack.

Ensuring "Enterprise Truth" with Deterministic Execution

Its workflow engine is designed for deterministic execution. This means every time a workflow runs, it follows the same logic, ensuring predictable and consistent outcomes. This allows maintaining "enterprise truth", knowing that the data and actions driven by your AI agents are always reliable and compliant.

Embedding Advanced Security and Governance Logic

It allows you to embed sophisticated security and governance logic directly within the integration workflows. For example, as seen in the Slack summary use case (Slide 13), Application Integration can fetch data, then deterministically apply rules to scrub sensitive financial figures or anonymise user IDs before the summary is returned to Agentspace. This ensures compliance and data integrity at the integration layer.

Handling Asynchronous Operations

As highlighted in the broker payout example (Slide 10), Application Integration has the unique ability to trigger a job in a legacy application and wait for an asynchronous callback before proceeding. This capability is critical for automating real-world business processes that involve delays or external system processing.

Practical Approaches to Integration

So, now that we understand how Application Integration helps, let's get into the practical side: how do you actually connect your data and processes? We'll focus on strategies you can put into action.

A. Utilising Application Integration Workflows as Tools in ADK

If you're a developer working with the Agent Development Kit (ADK), you can easily make Application Integration workflows available as "tools" for your Agentspace agents.

This means if an AI agent runs into a question that needs a complex, multi-step process or access to an older system, it can simply "call" an Application Integration workflow to handle it.

This process involves visually designing your business logic within Application Integration, then publishing that workflow as a secure API endpoint. Your ADK agent can then be configured to invoke this API as a tool when a specific task is required, allowing the integration workflow to handle the complex interactions with backend or legacy systems. The agent simply receives the processed result, without needing to manage the intricate sequence of operations itself.

A Real-World Example:

Think about an insurance company. When someone asks an Agentspace agent to "calculate an insurance quote for customer XY", the agent doesn't need to know all the tiny steps. Instead, it hands off the job to an Application Integration workflow. 

This workflow then precisely handles everything: checking the customer's risk, looking at credit scores, invoking a custom premium calculator, and getting the "premium" back. The agent just gets the final result, benefiting from the robust, deterministic execution managed by Application Integration.

B. Utilising Connectors as Tools in ADK

Beyond custom workflows, Application Integration has a library of over 150 ready-made connectors for popular business apps and databases. These connectors fundamentally simplify the process of integrating with common systems such as SAP, Salesforce, ServiceNow, and many others.

Leveraging these connectors involves identifying the appropriate pre-built connector for your target system within the Application Integration library. In cases where a pre-built connector doesn't exist, a custom connector can be built, extending connectivity to virtually any system. 

Once configured with the necessary connection details and authentication, your AI agent can directly expose and utilise the connector's functionality. This allows the agent to interact with the connected system, for instance, to retrieve customer information or create new records, without needing to manage the underlying API complexities.

A Real-World Example:

Imagine an employee asking an Agentspace agent, "What are the latest orders for customer XY from our AS/400 system?. The Agentspace agent, already set up with the AS/400 connector via Application Integration, can directly ask the system and instantly get back the order details. Or, for customer service, an agent could utilise the ServiceNow connector to "create a new support ticket" directly from a user's request. This direct, ready-to-go connection saves a ton of development time and complexity, getting you results much faster.

At Crystalloids, we specialize in connecting conversational AI agents like those built with Agentspace to the real heartbeat of your business: your data. From legacy systems to modern APIs, our integration expertise ensures your agents operate with complete, secure, and real-time information.