How Google‘s new platform is transforming business processes with practical AI agent implementation

In the rapidly evolving landscape of enterprise AI, Google’s Agentspace has emerged as a significant platform that enables organizations to deploy AI agents powered by Google Gemini and search technology. The platform promises to streamline knowledge indexing and retrieval while empowering business users to create automated workflows without deep technical expertise.

What is Google Agentspace?

Google Agentspace is designed to address one of the fundamental challenges organizations face when implementing generative AI (GenAI) solutions: the need to build extensive Retrieval Augmented Generation (RAG) systems to index their knowledge bases.

According to Justin Hartung, Principal Architect at Egen, “What Agentspace offers is that Google does all of that for you. They manage the backend indexing and RAG, so you can focus on querying and interacting with Gemini, grounded in your own internal knowledge.”

The platform goes beyond simple chatbot functionality by incorporating agentic capabilities that enable systems to take actions based on user inputs. Hartung explains this advancement: “You can now start to say, ‘I need to do X,’ and then the system can figure out, ‘Well, how do I get X done?’ and go do it for me.”

Practical Applications and Use Cases

While Agentspace offers exciting possibilities, Hartung emphasizes that its primary strength lies in enabling ad hoc workflows rather than fully industrialized processes. This distinction is crucial for organizations to understand when planning their AI implementation strategy.

Deep Research Capabilities

One compelling use case described by Hartung involves procurement optimization, “We worked with a group that had hundreds of people just browsing the web, trying to find who provides a part, what the price is, the lead time, the minimum order quantity that fits their needs—and that ended up taking a huge amount of time. So that’s a great example where Agentspace, using one of the agents Google provides—called Deep Research—can actually handle that.”

By automating this research process, organizations can significantly reduce the time spent on routine tasks while improving procurement outcomes.

The “Stare and Compare” Solution

Among the most impactful early applications, Hartung highlights what he calls the “Stare and Compare” category, “Anytime you have a human reviewing a document or visual image and filling out a form with data—like in traditional data entry—that’s a scenario where it’s very easy to start leveraging Gemini, especially with its multi-modal capabilities.”

This approach eliminates the need for traditional OCR and manual data mapping. Instead, AI can now:

• Pre-scan documents
• Pre-populate forms
• Show users exactly where in the source document each piece of information originated
• Enable humans to verify rather than manually enter data

One client implementation of this approach was so successful that it “negated the need to hire 2,000 employees based on a wave of work that was coming their way,” according to Hartung.

Implementation Challenges and Best Practices

Organizations looking to adopt Agentspace and similar AI agent platforms face several challenges that go beyond technical implementation.

Avoiding Analysis Paralysis

The rapid pace of AI advancement can lead to decision paralysis for many organizations. Hartung recommends working with experienced partners to “bring some structure” to the process by formulating clear hypotheses and business cases before testing specific implementations.

Responsible AI Implementation

A common pitfall is rushing from successful proof-of-concept to full production without adequate safeguards. Hartung cautions against this approach, “If a company rushes to incorporate that at production scale and starts making autonomous decisions without going through the right kind of checks and balances and guardrails… that’s an irresponsible use of AI.”

Instead, he advocates for a gradual implementation approach:

• Run AI agents in shadow mode alongside human workers
• Compare AI decisions with human decisions
• Use different AI approaches to validate outputs
• Gather comprehensive data on performance
• Implement A/B testing for gradual rollout
• Maintain detailed audit logs

Training and Enablement

Perhaps the most overlooked aspect of successful AI implementation is comprehensive training across all organizational levels, “Training and enablement is critical. So in Agentspace, I wouldn’t approach it like a traditional project where you say, ‘Hey, let me just turn it on for everybody and hope they start using it,’ because they won’t have the knowledge about when to use it versus when they need something at an industrialized scale.”

Hartung recommends a multi-layered approach to training that includes:

• Executive awareness of AI capabilities and business mapping
• Business user training on when and how to leverage AI tools
• Technical training for implementation teams
• Comprehensive understanding of business processes to identify automation opportunities

Security and Data Protection

Security concerns remain paramount for organizations considering AI agent implementations. Google’s approach to security is a key differentiator according to Hartung, “The stack is incredibly thorough in how it protects security at every layer. It starts with security as the default—protection is built in by default—and in many cases, you can’t even turn it off, from encryption at rest to encryption in transit and every layer in between.”

He contrasts this with free tools available on the internet where data usage policies may be unclear or problematic, warning that offerings that seem “too good to be true” often involve compromises in data security.

Cultural Impact and Skill Development

Rather than replacing human learning opportunities, Hartung observes that AI tools like Agentspace are creating new pathways for skill development, “What I’m seeing now is that this generation is using AI as a mentor—alongside their human mentors—which is accelerating their development and fueling their curiosity.”

This creates a “self-fulfilling learning process” where successful implementations drive further adoption through demonstrated value rather than top-down mandates.

The Path Forward

As organizations continue to explore the potential of AI agents, the most successful implementations will balance technological innovation with thoughtful governance, security, and human enablement. Google Agentspace represents a significant step forward in making AI capabilities more accessible to business users while maintaining enterprise-grade security and control.

For technical teams looking to implement AI agents effectively, the message is clear: start with well-defined business problems, implement gradually with appropriate safeguards, and invest in comprehensive training across all organizational levels.

By following these principles, organizations can realize the transformative potential of AI agents while avoiding common pitfalls that have hindered previous technology adoption cycles.

Guest: Justin Hartung (LinkedIn)
Company: Egen
Show: An Eye on AI